Tricks and Traditional Whiz-dim

Will the Houston Spaceport at Ellington Field be subject to more than the standard FAA regulations?

From the Florida Spaceport System Plan 2018, p. 92:

Source: https://www.spaceflorida.gov/wp-content/uploads/2018/12/FSSP18_FINAL__03-06-2018__Low-Res.pdf

A.5 COMMERCIAL RANGE INSTRUMENTATION AND CAPABILITIES

Space Florida is developing approaches to support and facilitate the emergence of commercial range safety and flight monitoring instrumentation that can lessen or perhaps even eliminate the reliance on traditional federal ground-based tracking and control systems. Additionally, Space Florida is seeking to develop independent, commercially operated services for safety analysis and operational support of launch, reentry, and test operations. These may include capabilities such as tailored weather forecasting and real-time information; flight safety hazards analysis and real-time monitoring; and required public clear zone verification monitoring. Space Florida’s objective in advancing the development and commercial availability of such capabilities with an efficient delivery system is to furnish a tool kit of operations support capabilities wherever needed throughout the Florida Spaceport System. This will address the Systems Plan goal for a safer and secure spaceport transportation system throughout Florida. It will support industry efforts to meet FAA safe flight requirements by transitioning to a space-based range, enabling private and government investment in systems which allow an increased launch tempo to meet market demand. New technologies for launch vehicle tracking and flight control will meet a standard of safety for the public and property that is equivalent to or higher than that afforded in the current operating environment, which has remained reliant on aging, limited capacity ground based stations.

FLORIDA SPACEPORT SYSTEM PLAN APPENDIX A A.6 LAND USE PLANNING TO SUPPORT FUTURE CAPACITY NEEDS

Space Florida is employing its spaceport authority role to proactively participate in Florida land use planning, including the planning for future land uses on federal properties, to help ensure and support capacity for future space transportation needs. This is a critical function to address and balance the goals of a Florida Spaceport System that achieves a growing, safe, and secure space transportation network while ensuring environmental stewardship. This function will address the federal transportation requirements related to how federal transportation projects, including FAA-licensed spaceports, may impact conservation lands and public access/ recreational uses. It will address how to balance these requirements with the needs for safe and secure space transportation launch areas and reentry sites that meet industry safety standards and federal regulations adopted to protect the uninvolved public.

Will the Houston Spaceport (Ellington Field) be subject to more than the standard FAA regulations?

From FAA PPM 5300-1B, p. 2: (Runway Protection Zones and Clear Zones)

Background.

 

Approach Protection Zones were originally established to define land areas underneath aircraft approach paths in which control by the airport operator was highly desirable to prevent the creation of airport hazards.

 

Subsequently, clear zones beyond runway ends were established to preclude obstructions potentially hazardous to aircraft and to control building construction as a protection for people on the ground.

 

[Policy and Regulations include:]

FAA Runway Protection Zone Policy, Federal Register, Friday, August 4, 1989 states that the FAA will resist or oppose objects or activities in the vicinity of an airport that conflict with an airport’s planning or design, or recommendation to protect the public’s investment in the national airport system.

Advisory Circular (AC) 150/5300-13 (dated 8/29/89), Airport Design, introduced the Runway Protection Zone (RPZ) criteria.

This replaced the prior AC 150/5300-4B, and redefined the area as a RPZ in lieu of a “clear zone”. The RPZ function is to enhance the protection of people and property on the ground. This AC also introduced the recommendation for the RPZ to be accessible to rescue and fire fighting vehicles.

Advisory Circular 150/5300-13, changes 1 & 2, paragraphs 211 and 212 set forth required and recommended Airport Object Clearing Criteria to ensure safe and efficient aircraft operations at an airport.

Advisory Circular 150/5300-13, paragraph 213 a. (2)(a) states that “automobile parking facilities, although discouraged, may be permitted provided the parking facilities and any associated appurtenances, in addition to meeting all of the preceding conditions, are located outside of the object free area extension”.

FAA Order 5100.38A, paragraph 602 b. (1) states that “the sponsor should be strongly encouraged to acquire fee title to all land within the runway protection zone, with first priority given to land within the object free area. Structures or activities located on this land must be removed unless excepted by the regional Airports Division manager or needed for air navigational aids.”

FAA Order 5100.38A, Appendix 9 – Special Condition 12.a. “Acquisition of Fee Title to Runway protection Zone. The sponsor agrees to prevent the erection or creation of any structure or place of public assembly in the runway protection zone, as depicted on the Exhibit “A” . . . . . Any existing structures or uses within the runway protection zone will be cleared or discontinued unless approved by the FAA.”

Sponsor requirements regarding the clearance of approaches of obstructions identified under Part 77 is generally based on obligations (Assurance No. 23) incurred via grant agreements issued prior to October 1, 1980. Change 9 to FAR Part 152, which revised the clearing of obstructions to the clearing of airport hazards, was provided May 22, 1980. Assurances based on Change 9 were provided in August of 1981, and were included in the FY-81 grants. As such, grant agreements executed after October 1, 1980 do not require the sponsors to seek the removal of obstructions, but rather the removal of airport hazards. Currently, Airport Sponsor agreements with FAA require sponsors to protect terminal airspace by mitigating existing airport hazards and “preventing” future airport hazards.

FAA Order 8260.3B, Chapter 12, specifies the Obstacle Clearing requirements to be applied to diverse departures, departure routes and Standard Instrument Departures (SID’s).

FAA Order 5200.5A, “Waste Disposal Sites on or Near Airports” presents FAA’s determination that a waste disposal site within 5,000 feet of a runway end used by piston powered aircraft only or within 10,000 feet of a runway end used or planned to be used by turbine powered aircraft is an incompatible land use incompatible airport operation and therefore unacceptable within the RPZ’s.

FAR Part 139, Section 139.337, Wildlife Hazard Management, requires airport owners to take immediate measures to alleviate wildlife hazards whenever they are detected. Consistent interpretation and application of the above guidance is essential to the safe, orderly development of airports consistent with available financial resources and the increased land-use requirement from all segments of society. This document establishes a uniform policy for the application of the above guidance and other related documents.

Does this mean to kill any creature that wanders onto or above airport property?

Definition.

Airport Hazard –

Any structure or object of natural growth located on or in the vicinity of a public-use airport, or any use of land near such an airport, which obstructs the airspace required for the flight in landing or take off at such airport, or is otherwise hazardous to such landing or taking off of aircraft is an mart hazard (see FAA Order 5190.6A, Appendix 5). In the Great Lakes Region, an item will only be considered an airport hazard based on an unfavorable airspace determination.

Clearway –

A defined rectangular area beyond the end of a runway cleared or suitable for use in lieu of runway to satisfy takeoff distance requirements.

Runway Protection Zone –

An area off the runway end (formerly the clear zone) used to enhance the protection of people and property on the ground.

Runway Safety Area –

A defined surface surrounding the runway prepared or suitable for reducing the risk of damage to airplanes in the event of an undershoot, overshoot, or excursion from the runway and provides greater accessibility for firefighting and rescue equipment during such incidents.

Threshold –

The beginning of that portion of the runway available for landing. When the threshold is located at a point other than at the beginning of the pavement and the portion of pavement behind the threshold may be used for takeoffs in either direction or landings from the opposite direction, it is referred to as a displaced threshold. When the threshold is located at a point other than at the beginning of the pavement and the portion of pavement behind the threshold is not available for takeoff or landing in either direction, this area behind the threshold may be made available for taxiing aircraft.

 

Policy/Procedures.

 

Airport owner control over Runway Protection Zones will enhance protection of people and property on the ground. Such control includes clearing Runway Protection Zone areas (and maintaining them clear) of incompatible objects and activities. (See AC 150/5300-13, paragraph 212).

 

The Runway Protection Zone is trapezoidal in shape and centered about the runway centerline. It begins 200 feet beyond the end of the area usable for takeoff or landing. On turf runways, the Runway Protection Zone begins at the end of the area usable for takeoff or landing or at the threshold when declared distances are implemented.

 

The Airport Layout Plan (ALP) is the required vehicle for documenting the sponsor’s property acquisition plan for implementing the FAA Runway Protection Zone policy.

 

New
Airports –

The required Runway Protection Zones must be acquired by the Sponsor in fee and cleared, unless a plan for less than fee acquisition is approved by FAA.

 

Clearing includes grubbing, removing all objects, and avoiding the introduction of sudden grade changes. The sponsor must maintain RPZ’s clear of incompatible land uses and above ground objects which do not need to be located in the Runway Protection Zone for air navigation or aircraft maneuvering purposes (NAVAIDS would be an exception).

 

Incompatible land uses include, but are not limited to uses which might create glare and misleading lights, residences, fuel handling and storage facilities, smoke generating activities, places of public assembly (i.e.: churches, schools, hospitals, office buildings, shopping centers, stadiums, recreational facilities etc.), waste disposal sites (i.e.: open dumps, landfills, cornposting, sludge disposal, effluent spraying, waste water treatment lagoons, etc.), storm water retention or detention basins, creation of wetlands, uses which might impede visual and electronic NAVAIDs and uses that attract wildlife.

 

NOTE: The prohibition of “recreational facilities” is intended to encompass incompatible places of public assembly, such as swimming pools, water parks, gymnasiums, baseball fields, soccer fields, tennis courts, etc. and their associated structures. Golf courses (except for clubhouses and other related facilities) are not prohibited by the PPM, however the clearing, grubbing and object removal requirements of paragraph 3-b. (1) may make the golf course less interesting.

 

FAR Part 77, TERPS, threshold siting criteria and NAVAID clearance planes and object free areas may also restrict golf courses in RPZ’s. The clearing, grubbing and the removal of objects from the Runway Protection Zone shall provide a surface which would be accessible to airport rescue and firefighting vehicles and permit passage of aircraft that land short or overshoot the runway without severe damage. These requirements would preclude the following:

(a) Continual flow open waterways (drainage ditches and swales with side slopes of 4:l or less would be acceptable).

(b) Surface gradients greater than 4:l (traverseways such as roadways and railroads with side slopes meeting the 4:l grading criteria would be acceptable).

(c) Parked vehicles and railroad cars.

(d) Stored crops and farm equipment.

(e) Power lines and facilities.

(f) Fencing (fencing with breakable mountings would be acceptable).

 

Fee acquisition of land beyond the Runway Protection Zones needed to achieve compatible land use, is strongly encouraged.

Airport Improvement Program Investments Involving
New
Runways or Runway Extensions at Existing Airports.

The required Runway Protection Zone should be acquired in fee and cleared subject to the clearing requirements and land use restrictions listed in paragraph 3.b.(1).

 

If fee acquisition is determined to be infeasible, for any part of the Runway Protection Zone, that portion of the Runway Protection Zone must be protected by an aviation easement, (see FAA Order 5100.37) against incompatible land use restrictions listed in paragraph 3.b.(l). In all cases, the Runway Safety Area portion of the Runway Protection Zone must be acquired in fee and cleared, subject to all conditions listed per paragraph 3-b.(l).

 

The aviation easement must provide protection for FAR Part 77, Subpart C, Surfaces, Obstacle Free Zone, Runway Object Free Area, Clearways, NAVAID Critical Areas, Approach Light Clearing Planes, Runway Visibility Zones, Obstacle Clearing Planes (PAPI, VASI, PLASI), Airport Traffic Control Tower lines of sight, and departure obstacle identification surface clearances (refer to Chapter 12 of Order 8260.3B). This easement must prohibit incompatible land uses as listed in paragraph 3.b.(l). If the present land use on the proposed easement property is incompatible, it must be properly mitigated and approved by the FAA.

 

In lieu of an aviation easement, Runway Protection Zone protection may be provided by written agreements with a public agency (i.e. State Highway Division) to control use of the land. These agreements must include the incompatible land use restrictions listed in paragraph 3.b.(l) and be approved by FAA.

 

In the case of runway extensions into the Runway Protection Zone at existing constrained airports, a minimum Runway Protection Zone area, beginning 200 feet beyond the far end of the Takeoff Distance Available (TODA) for departures, is required. See paragraph 5.b., of Appendix 14, of AC 150/5300-13 for the standard dimensions of this departure Runway Protection Zone. The Runway Protection Zone configuration associated with the approaches shall begin 200 feet before the threshold. Table 2-5, of AC 150/5300-13 contains the standards dimensions for the approach Runway Protection Zones.

 

Fee and/or easement acquisition of land beyond the Runway Protection Zones, needed to achieve compatible land use, is strongly encouraged.

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Standard
GIS Tips and Tricks

Naming Conventions for Census Bureau Geographic and Demographic GIS Attributes

This discussion addresses field names for use in GIS practice when integrating U.S. Census Bureau data fields. Common Census software such as the American Fact Finder
quickly
outputs useful data but notes it using cryptic fieldnames that can only be fully deciphered with technical documentation found for the Decennial Census at https://www.census.gov/programs-surveys/decennial-census/technical-documentation.html and the ACS at https://www.census.gov/programs-surveys/acs/technical-documentation.html. Here I suggest conventions that can be successfully applied in ESRI ArcGIS and related software to code field names that are succinct but user-friendly.

For wider consideration of geodatabase name and size limits, you may refer to http://desktop.arcgis.com/en/arcmap/10.3/manage-data/administer-file-gdbs/file-geodatabase-size-and-name-limits.htm.

Establishing naming conventions for field names in GIS Attribute files can be extremely challenging if we are to preserve backward compatibility with the now-ancient Data Base Format (.dbf) file conventions still commonly applied to attribute data storage and retrieval in ESRI Shape File datasets. More recent Access-style file geodatabases (.gdb) overcame the extreme 10-character limitation for field name character length presented by the .dbf format. Even more limiting were those original MS-DOS positional filename.ext convention of only EIGHT characters length (dot, three characters “extension”). To remain readable at such short identifier lengths, complex geographic and demographic characteristics have always been difficult to describe mnemonically with success, and many of us have tried over time to grapple with the problem.

Jack Dangermond’s ESRI ArcGIS

Hegemony of ESRI GIS software in the majority of use cases introduced additional considerations. In ArcGIS field names must not begin with a numeric character. ArcGIS keywords must be avoided. I find this mostly impacts such variables as XLon for decimal degree longitude, rather than XLong which conflicts with the long integer data type “long”.

Python

The adoption of the Python language by ESRI as its scripting language requires compatibility with Python programming naming conventions and keywords to avoid confusion in practice. Thus underscores must be limited to use within a field name, and any other punctuation in general is unsafe for .dbf backward compatibility. I have come to limit underscore to represent unavoidable punctuation which cannot be entered into a field name.

“In Python, if a name is intended to be “private“, it is prefixed by an underscore. Private variables are only enforced by convention in Python. Names can also be suffixed with an underscore to prevent conflict with Python keywords. Prefixing with double underscores changes behavior in classes with regard to name mangling. Prefixing and suffixing with double underscores are reserved for “magic names” which fulfill special behavior in Python objects.[32]” — https://en.wikipedia.org/wiki/Naming_convention_(programming), last retrieved 12/11/2018.

SQL

Additionally, the use of Structured Query Language (SQL) presents another set of keywords that could make life difficult if they were repeated inside fieldnames. For more information on SQL in ArcGIS, please see http://desktop.arcgis.com/en/arcmap/latest/manage-data/administer-file-gdbs/sql-reporting-and-anlysis-file-geodatabases.htm

These are limitations that we all agree to live with in the world of GIS.

Complete synthesis of all other practicable naming conventions is likely impossible, based on the general discussion of them on Wikipedia, reprinted from last retrieval 12/11/2018 below.

Source of the following: https://en.wikipedia.org/wiki/Naming_convention_(programming)

A General Discussion of (Programming) Naming Conventions from Wikipedia

Length of identifiers

Fundamental elements of all naming conventions are the rules related to identifier length (i.e., the finite number of individual characters allowed in an identifier). Some rules dictate a fixed numerical bound, while others specify less precise heuristics or guidelines.

Identifier length rules are routinely contested in practice, and subject to much debate academically.

Some considerations:

  • shorter identifiers may be preferred as more expedient, because they are easier to type (although many IDEs and text-editors provide text-completion, which mitigates this)
  • extremely short identifiers (such as ‘i’ or ‘j’) are very difficult to uniquely distinguish using automated search and replace tools (although this is not an issue for regex-based tools)
  • longer identifiers may be preferred because short identifiers cannot encode enough information or appear too cryptic
  • longer identifiers may be disfavored because of visual clutter

It is an open research issue whether some programmers prefer shorter identifiers because they are easier to type, or think up, than longer identifiers, or because in many situations a longer identifier simply clutters the visible code and provides no perceived additional benefit.

Brevity in programming could be in part attributed to:

  • early linkers which required variable names to be restricted to 6 characters to save memory. A later “advance” allowed longer variable names to be used for human comprehensibility, but where only the first few characters were significant. In some versions of BASIC such as TRS-80 Level 2 Basic, long names were allowed, but only the first two letters were significant. This feature permitted erroneous behaviour that could be difficult to debug, for example when names such as “VALUE” and “VAT” were used and intended to be distinct.
  • early source code editors lacking autocomplete
  • early low-resolution monitors with limited line length (e.g. only 80 characters)
  • much of computer science originating from mathematics, where variable names are traditionally only a single letter

    Letter case and numerals

Some naming conventions limit whether letters may appear in uppercase or lowercase. Other conventions do not restrict letter case, but attach a well-defined interpretation based on letter case. Some naming conventions specify whether alphabetic, numeric, or alphanumeric characters may be used, and if so, in what sequence.

Multiple-word identifiers

A common recommendation is “Use meaningful identifiers.” A single word may not be as meaningful, or specific, as multiple words. Consequently, some naming conventions specify rules for the treatment of “compound” identifiers containing more than one word.

As most programming languages do not allow whitespace in identifiers, a method of delimiting each word is needed (to make it easier for subsequent readers to interpret which characters belong to which word). Historically some early languages, notably FORTRAN (1955) and ALGOL (1958), allowed spaces within identifiers, determining the end of identifiers by context. This was abandoned in later languages due to the difficulty of tokenization. It is possible to write names by simply concatenating words, and this is sometimes used, as in mypackage for Java package names,[3] though legibility suffers for longer terms, so usually some form of separation is used.

Delimiter-separated words

One approach is to delimit separate words with a nonalphanumeric character. The two characters commonly used for this purpose are the hyphen (“-“) and the underscore (“_”); e.g., the two-word name “two words” would be represented as “two-words” or “two_words“. The hyphen is used by nearly all programmers writing COBOL (1959), Forth (1970), and Lisp (1958); it is also common in Unix for commands and packages, and is used in CSS.[4] This convention has no standard name, though it may be referred to as lisp-case or COBOL-CASE (compare Pascal case), kebab-case, or other variants.[5][6][7][8] Of these, kebab-case, dating at least to 2012,[9] has achieved some currency since.[10][11]

By contrast, languages in the FORTRAN/ALGOL tradition, notably languages in the C and Pascal families, used the hyphen for the subtraction
infix operator, and did not wish to require spaces around it (as free-form languages), preventing its use in identifiers. An alternative is to use underscores; this is common in the C family (including Python), with lowercase words, being found for example in The C Programming Language (1978), and has come to be known as snake case. Underscores with uppercase, as in UPPER_CASE, are commonly used for C preprocessor macros, hence known as MACRO_CASE, and for environment variables in Unix, such as BASH_VERSION in bash. Sometimes this is humorously referred to as SCREAMING_SNAKE_CASE.

Letter case-separated words

See also: Letter case § Special case styles

Another approach is to indicate word boundaries using medial capitalization, called “CamelCase“, “Pascal case”, and many other names, thus rendering “two words” as either “twoWords” or “TwoWords“. This convention is commonly used in Pascal, Java, C#, and Visual Basic. Treatment of acronyms in identifiers (e.g. the “XML” and “HTTP” in XMLHttpRequest) varies. Some dictate that they be lowercased (e.g. XmlHttpRequest) to ease typing and readability, whereas others leave them uppercased (e.g. XMLHTTPRequest) for accuracy.

Metadata and hybrid conventions

Some naming conventions represent rules or requirements that go beyond the requirements of a specific project or problem domain, and instead reflect a greater overarching set of principles defined by the software architecture, underlying programming language or other kind of cross-project methodology.

Hungarian notation

Perhaps the most well-known is Hungarian notation, which encodes either the purpose (“Apps Hungarian”) or the type (“Systems Hungarian”) of a variable in its name.[12] For example, the prefix “sz” for the variable szName indicates that the variable is a null-terminated string.

Positional notation

A style used for very short (8 characters and less) could be: LCCIIL01, where LC would be the application (Letters of Credit), C for COBOL, IIL for the particular process subset, and the 01 a sequence number.

This sort of convention is still in active use in mainframes dependent upon JCL and is also seen in the 8.3 (maximum 8 characters with period separator followed by 3 character file type) MS-DOS style.

Composite word scheme (OF Language)

IBM’s “OF Language” was documented in an IMS (Information Management System) manual.

It detailed the PRIME-MODIFIER-CLASS word scheme, which consisted of names like “CUST-ACT-NO” to indicate “customer account number”.

PRIME words were meant to indicate major “entities” of interest to a system.

MODIFIER words were used for additional refinement, qualification and readability.

CLASS words ideally would be a very short list of data types relevant to a particular application. Common CLASS words might be: NO (number), ID (identifier), TXT (text), AMT (amount), QTY (quantity), FL (flag), CD (code), W (work) and so forth. In practice, the available CLASS words would be a list of less than two dozen terms.

CLASS words, typically positioned on the right (suffix), served much the same purpose as Hungarian notation prefixes.

The purpose of CLASS words, in addition to consistency, was to specify to the programmer the data type of a particular data field. Prior to the acceptance of BOOLEAN (two values only) fields, FL (flag) would indicate a field with only two possible values.

Language-specific conventions

ActionScript

Adobe’s Coding Conventions and Best Practices suggests naming standards for ActionScript that are mostly consistent with those of ECMAScript.[citation needed] The style of identifiers is similar to that of Java.

Ada

In Ada, the only recommended style of identifiers is Mixed_Case_With_Underscores.[13]

C and C++

In C and C++, keywords and standard library identifiers are mostly lowercase. In the C standard library, abbreviated names are the most common (e.g. isalnum for a function testing whether a character is alphanumeric), while the C++ standard library often uses an underscore as a word separator (e.g. out_of_range). Identifiers representing macros are, by convention, written using only uppercase letters and underscores (this is related to the convention in many programming languages of using all-upper-case identifiers for constants). Names containing double underscore or beginning with an underscore and a capital letter are reserved for implementation (compiler, standard library) and should not be used (e.g. __reserved or _Reserved).[14][15] This is superficially similar to stropping, but the semantics differ: the underscores are part of the value of the identifier, rather than being quoting characters (as is stropping): the value of __foo is __foo (which is reserved), not foo (but in a different namespace).

Go

In Go, the convention is to use MixedCaps or mixedCaps rather than underscores to write multiword names.[16]

Java

In Java, naming conventions for identifiers have been established and suggested by various Java communities such as Sun Microsystems,[17] Netscape,[18] AmbySoft,[19] etc. A sample of naming conventions set by Sun Microsystems are listed below, where a name in “CamelCase” is one composed of a number of words joined without spaces, with each word’s initial letter in capitals — for example “CamelCase”.

Identifier type

Rules for naming

Examples

Classes Class names should be nouns in UpperCamelCase, with the first letter of every word capitalised. Use whole words — avoid acronyms and abbreviations (unless the abbreviation is much more widely used than the long form, such as URL or HTML).
  • class Raster {}
  • class ImageSprite {}
Methods Methods should be verbs in lowerCamelCase or a multi-word name that begins with a verb in lowercase; that is, with the first letter lowercase and the first letters of subsequent words in uppercase.
  • run();
  • runFast();
  • getBackground();
Variables Local variables, instance variables, and class variables are also written in lowerCamelCase. Variable names should not start with underscore (_) or dollar sign ($) characters, even though both are allowed. This is in contrast to other coding conventions that state that underscores should be used to prefix all instance variables.

Variable names should be short yet meaningful. The choice of a variable name should be mnemonic — that is, designed to indicate to the casual observer the intent of its use. One-character variable names should be avoided except for temporary “throwaway” variables. Common names for temporary variables are i, j, k, m, and n for integers; c, d, and e for characters.

  • int i;
  • char c;
  • float myWidth;
Constants Constants should be written in uppercase characters separated by underscores. Constant names may also contain digits if appropriate, but not as the first character.
  • static final int MAX_PARTICIPANTS = 10;

Java compilers do not enforce these rules, but failing to follow them may result in confusion and erroneous code. For example, widget.expand() and Widget.expand() imply significantly different behaviours: widget.expand() implies an invocation to method expand() in an instance named widget, whereas Widget.expand() implies an invocation to static method expand() in class Widget.

One widely used Java coding style dictates that UpperCamelCase be used for classes and lowerCamelCase be used for instances and methods.[17] Recognising this usage, some IDEs, such as Eclipse, implement shortcuts based on CamelCase. For instance, in Eclipse’s content assist feature, typing just the upper-case letters of a CamelCase word will suggest any matching class or method name (for example, typing “NPE” and activating content assist could suggest NullPointerException).

Initialisms of three or more letters are CamelCase instead of uppercase (e.g., parseDbmXmlFromIPAddress instead of parseDBMXMLFromIPAddress). One may also set the boundary at two or more letters (e.g. parseDbmXmlFromIpAddress).

JavaScript

The built-in JavaScript libraries use the same naming conventions as Java. Data types and constructor functions use upper camel case (RegExp, TypeError, XMLHttpRequest, DOMObject) and methods use lower camel case (getElementById, getElementsByTagNameNS, createCDATASection). In order to be consistent most JavaScript developers follow these conventions.[20] See also: Douglas Crockford’s conventions

Lisp

Common practice in most Lisp dialects is to use dashes to separate words in identifiers, as in with-open-file and make-hash-table. Dynamic variable names conventionally start and end with asterisks: *map-walls*. Constants names are marked by plus signs: +map-size+.[21][22]

.NET

Microsoft
.NET recommends
UpperCamelCase for most identifiers. (lowerCamelCase is recommended for parameters and variables) and is a shared convention for the .NET languages.[23] Microsoft further recommends that no type prefix hints (also known as Hungarian notation) are used.[24] Instead of using Hungarian notation it is recommended to end the name with the base class’ name; LoginButton instead of BtnLogin.[25]

Objective-C

Objective-C has a common coding style that has its roots in Smalltalk .

Top-level entities, including classes, protocols, categories, as well as C constructs that are used in Objective-C programs like global variables and functions, are in UpperCamelCase with a short all-uppercase prefix denoting namespace, like NSString, UIAppDelegate, NSApp or CGRectMake. Constants may optionally be prefixed with a lowercase letter “k” like kCFBooleanTrue.

Instance variables of an object use lowerCamelCase prefixed with an underscore, like _delegate and _tableView.

Method names use multiple lowerCamelCase parts separated by colons that delimit arguments, like: application:didFinishLaunchingWithOptions:, stringWithFormat: and isRunning.

Pascal, Modula-2 and Oberon

Wirthian languages Pascal, Modula-2 and Oberon generally use Capitalized or UpperCamelCase identifiers for programs, modules, constants, types and procedures, and lowercase or lowerCamelCase identifiers for math constants, variables, formal parameters and functions.[26] While some dialects support underscore and dollar signs in identifiers, snake case and macro case is more likely confined to use within foreign API interfaces.[27]

Perl

Perl takes some cues from its C heritage for conventions. Locally scoped variables and subroutine names are lowercase with infix underscores. Subroutines and variables meant to be treated as private are prefixed with an underscore. Package variables are title cased. Declared constants are all caps. Package names are camel case excepting pragmata—e.g., strict and mro—which are lowercase. [28]
[29]

Perl 6

Perl 6 follows more or less the same conventions as Perl, except that it allows an infix hyphen – or an apostrophe ‘ (or single quote) within an identifier (but not two in a row), provided that it is followed by an alphabetic character. Perl 6 programmers thus often use kebab case in their identifiers; for example, fish-food and don’t-do-that are valid identifiers. [30]

PHP

PHP recommendations are contained in PSR-1 (PHP Standard Recommendation 1) and PSR-2.[31]

Python and Ruby

Python and Ruby both recommend UpperCamelCase for class names, CAPITALIZED_WITH_UNDERSCORES for constants, and lowercase_separated_by_underscores for other names.

In Python, if a name is intended to be “private“, it is prefixed by an underscore. Private variables are only enforced by convention in Python. Names can also be suffixed with an underscore to prevent conflict with Python keywords. Prefixing with double underscores changes behaviour in classes with regard to name mangling. Prefixing and suffixing with double underscores are reserved for “magic names” which fulfill special behaviour in Python objects.[32]

Rust

Rust recommends UpperCamelCase for type aliases and struct, trait, enum, and enum variant names, CAPITALIZED_WITH_UNDERSCORES for constants or statics, and lowercase_separated_by_underscores for other names.[33]

Swift

Swift has shifted its naming conventions with each individual release. However a major update with Swift 3.0 stabilised the naming conventions for lowerCamelCase across variables and function declarations. Constants are usually defined by enum types or constant parameters that are also written this way. Class and other object type declarations are UpperCamelCase.

As of Swift 3.0 there have been made clear naming guidelines for the language in an effort to standardise the API naming and declaration conventions across all third party APIs. [34]

See also

  1. ^ Derek M. Jones “Operand names influence operator precedence decisions” An experiment investigating the effect of variable names on operator precedence selection
  2. ^
    Raymond, Eric S. (1 October 2004). “religious issues”. The Jargon File (version 4.4.8 ed.). Retrieved 7 November 2011.
  3. ^
    Naming a Package
  4. ^
    “CSS reference”. Mozilla Developer Network. Retrieved 2016-06-18.
  5. ^
    “StackOverflow – What’s the name for snake_case with dashes?”.
  6. ^
    “Programmers – If this is camelCase what-is-this?”.
  7. ^
    “Camel_SNAKE-kebab”.
  8. ^
    UnderscoreVersusCapitalAndLowerCaseVariableNaming
  9. ^
    jwfearn (5 September 2012). “Revisions to jwfearn’s answer to What’s the name for dash-separated case?”.
  10. ^
    Living Clojure (2015), by Carin Meier, p. 91
  11. ^
    lodash: kebabCase
  12. ^
    http://www.joelonsoftware.com/articles/Wrong.html
  13. ^
    http://www.adaic.org/resources/add_content/docs/95style/html/sec_3/3-2-1.html
  14. ^
    “ISO/IEC 9899:1999 Programming languages — C”. ISO.
  15. ^
    “ISO/IEC 14882:2011 Information technology — Programming languages — C++”. ISO.
  16. ^
    https://golang.org/doc/effective_go.html#mixed-caps
  17. ^ Jump up to: a
    b “Code Conventions for the Java Programming Language”, Section 9: “Naming Conventions”
  18. ^ “NETSCAPE’S SOFTWARE CODING STANDARDS GUIDE FOR JAVA”,Collab Software Coding Standards Guide for Java
  19. ^
    “AmbySoft Inc. Coding Standards for Java v17.01d”
  20. ^
    Morelli, Brandon. “5 JavaScript Style Guides – Including AirBnB, GitHub, & Google”. codeburst.io. Retrieved 17 August 2018.
  21. ^
    http://www.gigamonkeys.com/book/variables.html
  22. ^
    Naming conventions on CLiki
  23. ^
    Microsoft .NET Framework Capitalization Styles
  24. ^
    .NET Framework Developer’s Guide – General Naming Conventions
  25. ^ [Framework Design Guidelines, Krzysztof Cwalina, Brad Abrams Page 62]
  26. ^
    Modula-2 Name Convention
  27. ^
    Foreign API Identifiers in Modula-2 Name Convention
  28. ^
    “Perl style guide”.
  29. ^
    “perlmodlib – constructing new Perl modules and finding existing ones”.
  30. ^
    “General rules of Perl 6 syntax”.
  31. ^
    “PHP standards recommendations”.
  32. ^
    Style Guide for Python Code PEP8
  33. ^
    “Naming conventions”. doc.rust-lang.org. Retrieved 2018-02-04.
  34. External links

My Census Field Naming Convention in Action with 2010-vintage U.S. Census ACS data

Shape dbf field Mnemonic geodatabase field ITEM STUB
DPSF1. SEX AND AGE [57]
Universe: Total population
TottPop TottPop DP0010001 Total:
AgeUnd5 AgeUnd5 DP0010002 Under 5 years
Age5_9 Age5_9 DP0010003 5 to 9 years
Age10_14 Age10_14 DP0010004 10 to 14 years
Age15_19 Age15_19 DP0010005 15 to 19 years
Age20_24 Age20_24 DP0010006 20 to 24 years
Age25_29 Age25_29 DP0010007 25 to 29 years
Age30_34 Age30_34 DP0010008 30 to 34 years
Age35_39 Age35_39 DP0010009 35 to 39 years
Age40_44 Age40_44 DP0010010 40 to 44 years
Age45_49 Age45_49 DP0010011 45 to 49 years
Age50_54 Age50_54 DP0010012 50 to 54 years
Age55_59 Age55_59 DP0010013 55 to 59 years
Age60_64 Age60_64 DP0010014 60 to 64 years
Age65_69 Age65_69 DP0010015 65 to 69 years
Age70_74 Age70_74 DP0010016 70 to 74 years
Age75_79 Age75_79 DP0010017 75 to 79 years
Age80_84 Age80_84 DP0010018 80 to 84 years
Age85Up Age85Up DP0010019 85 years and over
Male Male DP0010020 Male:
MaAgeUnd5 MaAgeUnd5 DP0010021 Under 5 years
MaAge5_9 MaAge5_9 DP0010022 5 to 9 years
MaAge10_14 MaAge10_14 DP0010023 10 to 14 years
MaAge15_19 MaAge15_19 DP0010024 15 to 19 years
MaAge20_24 MaAge20_24 DP0010025 20 to 24 years
MaAge25_29 MaAge25_29 DP0010026 25 to 29 years
MaAge30_34 MaAge30_34 DP0010027 30 to 34 years
MaAge35_39 MaAge35_39 DP0010028 35 to 39 years
MaAge40_44 MaAge40_44 DP0010029 40 to 44 years
MaAge45_49 MaAge45_49 DP0010030 45 to 49 years
MaAge50_54 MaAge50_54 DP0010031 50 to 54 years
MaAge55_59 MaAge55_59 DP0010032 55 to 59 years
MaAge60_64 MaAge60_64 DP0010033 60 to 64 years
MaAge65_69 MaAge65_69 DP0010034 65 to 69 years
MaAge70_74 MaAge70_74 DP0010035 70 to 74 years
MaAge75_79 MaAge75_79 DP0010036 75 to 79 years
MaAge80_84 MaAge80_84 DP0010037 80 to 84 years
MaAge85Up MaAge85Up DP0010038 85 years and over
Female Female DP0010039 Female:
FeAgeUnd5 FeAgeUnd5 DP0010040 Under 5 years
FeAge5_9 FeAge5_9 DP0010041 5 to 9 years
FeAge10_14 FeAge10_14 DP0010042 10 to 14 years
FeAge15_19 FeAge15_19 DP0010043 15 to 19 years
FeAge20_24 FeAge20_24 DP0010044 20 to 24 years
FeAge25_29 FeAge25_29 DP0010045 25 to 29 years
FeAge30_34 FeAge30_34 DP0010046 30 to 34 years
FeAge35_39 FeAge35_39 DP0010047 35 to 39 years
FeAge40_44 FeAge40_44 DP0010048 40 to 44 years
FeAge45_49 FeAge45_49 DP0010049 45 to 49 years
FeAge50_54 FeAge50_54 DP0010050 50 to 54 years
FeAge55_59 FeAge55_59 DP0010051 55 to 59 years
FeAge60_64 FeAge60_64 DP0010052 60 to 64 years
FeAge65_69 FeAge65_69 DP0010053 65 to 69 years
FeAge70_74 FeAge70_74 DP0010054 70 to 74 years
FeAge75_79 FeAge75_79 DP0010055 75 to 79 years
FeAge80_84 FeAge80_84 DP0010056 80 to 84 years
FeAge85Up FeAge85Up DP0010057 85 years and over
DPSF2. MEDIAN AGE BY SEX [3] (1 expressed decimal)
Universe: Total population
Median age-
MedianAge MedianAge DP0020001 Both sexes
MaMedAge MaMedAge DP0020002 Male
FeMedAge FeMedAge DP0020003 Female
DPSF3. SEX FOR THE POPULATION 16 YEARS AND OVER [3]
Universe: Population 16 years and over
Age16Up Age16Up DP0030001 Total:
MaAge16Up MaAge16Up DP0030002 Male
FeAge16Up FeAge16Up DP0030003 Female
DPSF4. SEX FOR THE POPULATION 18 YEARS AND OVER [3]
Universe: Population 18 years and over
Age18Up Age18Up DP0040001 Total:
MaAge18Up MaAge18Up DP0040002 Male
FeAge18Up FeAge18Up DP0040003 Female
DPSF5. SEX FOR THE POPULATION 21 YEARS AND OVER [3]
Universe: Population 21 years and over
Age21Up Age21Up DP0050001 Total:
MaAge21Up MaAge21Up DP0050002 Male
FeAge21Up FeAge21Up DP0050003 Female
DPSF6. SEX FOR THE POPULATION 62 YEARS AND OVER [3]
Universe: Population 62 years and over
Age62Up Age62Up DP0060001 Total:
MaAge62Up MaAge62Up DP0060002 Male
FeAge62Up FeAge62Up DP0060003 Female
DPSF7. SEX FOR THE POPULATION 65 YEARS AND OVER [3]
Universe: Population 65 years and over
Age65Up Age65Up DP0070001 Total:
MaAge65Up MaAge65Up DP0070002 Male
FeAge65Up FeAge65Up DP0070003 Female
DPSF8. RACE [24]
Universe: Total population
TotPop TotPop DP0080001 Total:
PopOneRace PopOneRace DP0080002 Population of one race:
WhiteAlone WhiteAlone DP0080003 White
BlackAlone BlackAlone DP0080004 Black or African American
AIANAlone AIANAlone DP0080005 American Indian and Alaska Native
AsianAlone AsianAlone DP0080006 Asian:
SAIndianAl SAIndianAlone DP0080007 Asian Indian
ChineseAlo ChineseAlone DP0080008 Chinese
FilipinoAl FilipinoAlone DP0080009 Filipino
JapaneseAl JapaneseAlone DP0080010 Japanese
KoreanAlon KoreanAlone DP0080011 Korean
Vietnamese VietnameseAlone DP0080012 Vietnamese
OtAsianAlo OtAsianAlone DP0080013 Other Asian
NHOPIAlone NHOPIAlone DP0080014 Native Hawaiian and Other Pacific Islander:
NHAlone NHAlone DP0080015 Native Hawaiian
GuamAlone GuamAlone DP0080016 Guamanian or Chamorro
SamoanAlon SamoanAlone DP0080017 Samoan
OPIAlone OPIAlone DP0080018 Other Pacific Islander
OtAlone OtAlone DP0080019 Some Other Race
MultiRace MultiRace DP0080020 Population of Two or More Races
WhiteAIAN WhiteAIAN DP0080021 White; American Indian and Alaska Native
WhiteAsian WhiteAsian DP0080022 White; Asian
WhiteBlack WhiteBlack DP0080023 White; Black or African American
WhiteOther WhiteOther DP0080024 White; Some Other Race
DPSF9. RACE (TOTAL RACES TALLIED) [6]
Universe: Total races tallied
WhiteCombo WhiteCombo DP0090001 White alone or in combination with one or more other races
BlackCombo BlackCombo DP0090002 Black or African American alone or in combination with one or more other races
AIANCombo AIANCombo DP0090003 American Indian and Alaska Native alone or in combination with one or more other races
AsianCombo AsianCombo DP0090004 Asian alone or in combination with one or more other races
NHOPICombo NHOPICombo DP0090005 Native Hawaiian and Other Pacific Islander alone or in combination with one or more other races
OtherCombo OtherCombo DP0090006 Some Other Race alone or in combination with one or more other races
DPSF10. HISPANIC OR LATINO BY SPECIFIC ORIGIN [7]
Universe: Total population
TotPop TotPop DP0100001 Total:
HispLatAny HispLatAnyRace DP0100002 Hispanic or Latino (of any race):
Mexican Mexican DP0100003 Mexican
PeurtoRica PeurtoRican DP0100004 Puerto Rican
Cuban Cuban DP0100005 Cuban
OtherHispL OtherHispLat DP0100006 Other Hispanic or Latino
NHLAnyRace NHLAnyRace DP0100007 Not Hispanic or Latino
DPSF11. HISPANIC OR LATINO AND RACE [17]
Universe: Total population
TotPop TotPop DP0110001 Total:
HispLatAny HispLatAnyRace DP0110002 Hispanic or Latino:
HispWhiteA HispWhiteA DP0110003 White alone
HispBlackA HispBlackA DP0110004 Black or African American alone
HispAIANA HispAIANA DP0110005 American Indian and Alaska Native alone
HispAsianA HispAsianA DP0110006 Asian alone
HispNHOPIA HispNHOPIA DP0110007 Native Hawaiian and Other Pacific Islander alone
HispOtherA HispOtherA DP0110008 Some Other Race alone
HispMultiR HispMultiRace DP0110009 Two or More Races
NHLAnyRace NHLAnyRace DP0110010 Not Hispanic or Latino:
NHLWhiteA NHLWhiteA DP0110011 White alone
NHLBlackA NHLBlackA DP0110012 Black or African American alone
NHLAIANA NHLAIANA DP0110013 American Indian and Alaska Native alone
NHLAsianA NHLAsianA DP0110014 Asian alone
NHLNHOPIA NHLNHOPIA DP0110015 Native Hawaiian and Other Pacific Islander alone
NHLOtherA NHLOtherA DP0110016 Some Other Race alone
NHLMultiRa NHLMultiRace DP0110017 Two or More Races
DPSF12. RELATIONSHIP [20]
Universe: Total population
TotPop TotPop DP0120001 Total:
HHPop HHPop DP0120002 In households:
HHr HHr DP0120003 Householder
Spouse Spouse DP0120004 Spouse
Child Child DP0120005 Child
OCU18 OCU18 DP0120006 Own child under 18 years
OtRel OtRel DP0120007 Other relatives
OtRelU18 OtRelU18 DP0120008 Under 18 years
OtRel65Up OtRel65Up DP0120009 65 years and over
NnRel NnRel DP0120010 Nonrelatives
NnRelU18 NnRelU18 DP0120011 Under 18 years
NnRel65Up NnRel65Up DP0120012 65 years and over
UnmarriedP UnmarriedPartner DP0120013 Unmarried partner
GQPop GQPop DP0120014 In group quarters:
Institutio Institutionalized DP0120015 Institutionalized population:
MaInstitut MaInstitutionalized DP0120016 Male
FeInstitut FeInstitutionalized DP0120017 Female
NnInstitut NnInstitutionalized DP0120018 Noninstitutionalized population:
MaNnInstit MaNnInstitutionalized DP0120019 Male
FeNnInstit FeNnInstitutionalized DP0120020 Female
DPSF13. HOUSEHOLDS BY TYPE [15]
Universe: Households
TotHH TotHH DP0130001 Total:
FamilyHH FamilyHH DP0130002 Family households (families)
FmHHOCU18 FmHHOCU18 DP0130003 With own children under 18 years
FmHHHW FmHHHW DP0130004 Husband-wife family
FmHWOCU18 FmHWOCU18 DP0130005 With own children under 18 years
FmHHMaSgl FmHHMaSgl DP0130006 Male householder, no wife present
FmMaSglOCU FmMaSglOCU18 DP0130007 With own children under 18 years
FmHHFeSgl FmHHFeSgl DP0130008 Female householder, no husband present
FmFeSglOCU FmFeSglOCU18 DP0130009 With own children under 18 years
NnFmHH NnFmHH DP0130010 Nonfamily households
NnFmHHSgl NnFmHHSgl DP0130011 Householder living alone:
NnFmHHSMal NnFmHHSMale DP0130012 Male
NnFmHHSM65 NnFmHHSM65Up DP0130013 65 years and over
NnFmHHSFem NnFmHHSFemale DP0130014 Female
NnFmHHSF65 NnFmHHSF65Up DP0130015 65 years and over
DPSF14. HOUSEHOLDS WITH INDIVIDUALS UNDER 18 YEARS [1]
Universe: Households with individuals under 18 years
HHw_AgeU18 HHw_AgeU18 DP0140001 Total
DPSF15. HOUSEHOLDS WITH INDIVIDUALS 65 YEARS AND OVER [1]
Universe: Households with individuals 65 years and over
HHw_Age65U HHw_Age65Up DP0150001 Total
DPSF16. AVERAGE HOUSEHOLD SIZE [1] (2 expressed decimals)
Universe: Households
AvgHHSize AvgHHSize DP0160001 Average household size
DPSF17. AVERAGE FAMILY SIZE [1] (2 expressed decimals)
Universe: Families
AvgFmSize AvgFmSize DP0170001 Average family size
DPSF18. HOUSING OCCUPANCY [9]
Universe: Total housing units
TotHU TotHU DP0180001 Total:
OccHU OccHU DP0180002 Occupied housing units
VacHU VacHU DP0180003 Vacant housing units:
VacHUForRe VacHUForRent DP0180004 For rent
VacHURNnOc VacHURNnOcc DP0180005 Rented, not occupied
VacHUForSa VacHUForSaleOnly DP0180006 For sale only
VacHUONnOc VacHUONnOcc DP0180007 Sold, not occupied
VacHUSeaso VacHUSeasonalRecreationalOccasionalUse DP0180008 For seasonal, recreational, or occasional use
VacHUOther VacHUOther DP0180009 All other vacants
DPSF19. HOMEOWNER VACANCY RATE [1] (1 expressed decimal)
Universe: Owner-occupied, vacant for sale only, and vacant sold but not occupied housing units
P_OVacRt P_OVacRt DP0190001 Homeowner vacancy rate (percent)
DPSF20. RENTAL VACANCY RATE [1] (1 expressed decimal)
Universe: Renter-occupied, vacant for rent, and vacant rented but not occupied housing units
P_RVacRt P_RVacRt DP0200001 Rental vacancy rate (percent)
DPSF21. HOUSING TENURE [3]
Universe: Occupied housing units
DP0210001 Total:
OOcHU OOcHU DP0210002 Owner-occupied housing units
ROcHU ROcHU DP0210003 Renter-occupied housing units
DPSF22. POPULATION IN OCCUPIED HOUSING UNITS BY TENURE [2]
Universe: Population in occupied housing units
PopOOcHU PopOOcHU DP0220001 Owner-occupied housing units
PopROcHU PopROcHU DP0220002 Renter-occupied housing units
DPSF23. AVERAGE HOUSEHOLD SIZE OF OCCUPIED HOUSING UNITS BY TENURE [2] (2 expressed decimals)
Universe: Occupied housing units
Average household size-
AvgHHSzOOc AvgHHSzOOc DP0230001 Owner occupied
AvgHHSzROc AvgHHSzROc DP0230002 Renter occupied

How to Use My Census Field Naming Convention

Since Census data for statistical (permanent) rather than purely political (impermanent or incrementing) areas are persistent over many decadal periods, the fields that describe them are closer to classes than methods or instances. They are the farthest thing from temporary variables. Following my old training in structured programming and Pascal, now partially adapted within the Java custom, a mix primarily of UpperCamelCase
for word boundaries using medial capitalization and positional and some common case keys has resulted in the following recommendation. All punctuation is replaced inside of field names by certain abbreviation and underscore conventions, and full word spellings are preserved as much as is practical. Some of the abbreviations included here are forward-looking to a time when sexual orientation and gender identity is described by the U. S. Census to assist with future equal protection and treatment under American law.

FOR CENSUS FILE AND FIELD NAMES:
Standard Mnemonic Abbreviations: Meaning:
2010 Final Year of Count or Sample Data Used (ACS field suffix, 1-yr, 3-yr, or 5-yr samples, or field suffix for year of decennial count, dropped when only one year is consistent throughout a dataset and appears in the metadata)
_ (Underscore character) the only permissible field name punctuation character; wildcard that also means ” to ” when used between numeric characters that define a range; may stand for any other obvious punctuation mark
A Alone (of a single characteristic)
As Asian (race-ethnicity), extreme abbreviation
Asex Asexual (gender identity), mid-sexual as opposed to male or female
ACS American Community Survey (source code)
AI American Indian (race-ethnicity)
AN Alaskan Native (race-ethnicity)
Avg Average, or mean (statistic)
Bl Black or African American (race-ethnicity)
Black Black or African American (race-ethnicity); Bl is the extreme abbreviation
BG Census Block Group (statistical area)
Bel Below, or “and below”, “under the value of ” a numeric character
Bis Bisexual (sexual orientation)
Cd Code (case code)
Cens Census (code), decennial Census, U.S. Census Bureau
Combo Alone or in combination with one or more other races
Dist District (case code)
E_ Estimate prefix, as opposed to a hard number, count or sum (ACS field prefix)
Fe Female (gender identity, as opposed to male)
FIPS Federal Information Processing Standard (case code)
Fm Family (relationship)
Geo Geographic (case code)
GQ Group quarters (relationship)
Hect Hectare, hectarea (area)
HH Household (OccHU)
HHr Householder
His Hispanic or Latino (origin, race-ethnicity); HL is the extreme abbreviation
Hisp Hispanic or Latino (origin, race-ethnicity); HL is the extreme abbreviation
HispLat Hispanic or Latino (origin, race-ethnicity); HL is the extreme abbreviation
HL Hispanic or Latino (origin, race-ethnicity)
HU Housing Units
HW Husband and Wife, spousal (relationship); married couple
ID Identifier (case code)
IntLat Internal Point Latitude, as opposed to shape centroid which may fall outside of the entity (Y-coordinate in decimal degrees); sometimes Ylat
IntLon Internal Point Longitude, as opposed to shape centroid which may fall outside of the entity (X-coordinate in decimal degrees); sometimes Xlon
LGBTQ Lesbian or Gay or Bisexual or Transsexual or Queer (sexual orientation minority grouping)
Lon Long or longitude or longitudinal, to avoid the keyword “long”
LWO Living with Others (2 or more HH Size)
LSAD Legal Statistical Area Description
M_ Margin of Error prefix (ACS field prefix)
Ma Male (gender identity, as opposed to female)
Med Median (statistic)
Mod Mode (statistic) or model (source code)
Multi Multiple e.g. Multifamily, Multiracial
MultiRace Two or more races (race-ethnicity), or MR in extreme abbreviation
NH Native Hawaiian (race-ethnicity)
NHL Not of Hispanic or Latino Origin (origin, race-ethnicity)
Nn Non
O Owner (tenure)
OC Own Child (relationship)
Oc Occupied (Housing Unit)
Occ Occupied (Housing Unit)
OPI Other Pacific Islander (race-ethnicity)
Ot Other
Ownr Owner (tenure)
P_ Percent prefix (in units of 100, ACS field positional identifier, as opposed to Pt, which is a part or rate or ratio or ration compared to a unit of one)
Pr_ Projection prefix, field positional identifier, ideally in conjunction with suffix indicating year and source of projection data)
_P Plus, “and up”, “or greater”, “or larger”, “or more” (suffix to a numeric character)
P1_ Commissioner’s Precinct One (prefix to fieldname)
P2_ Commissioner’s Precinct Two (prefix to fieldname)
P3_ Commissioner’s Precinct Three (prefix to fieldname)
P4_ Commissioner’s Precinct Four (prefix to fieldname)
pAcre per Acre, which is 43,560 square feet, or one 640th of a square mile (area relationship)
pHect per Hectare, which is 100 meters x 100 meters, 10000 square meters, or one hundredth of a square kilometer (area relationship)
pSqMi per Square Mile, which is 640 acres, or 27,878,400 square feet (43560*640)
Pl Place, includes city and Census Defined Place (CDP) in Texas (statistical area)
PM_ Percent Margin of Error prefix (ACS field positional identifier)
Pop Population
_Pt_ Point, decimal (conjunction when used between numeric characters)
Pt_ Part, rate, ration or ratio prefix (compared to one unit, prefix to fieldname); partial unit
R Renter (tenure)
Rc Race (race-ethnicity)
Rel Relative, related (relationship)
Rent Renter (tenure)
Rt Rate, same at part prefix, ratio or ration (compared to one unit), as opposed to P_, prefix for a percentage compared to 100 units
Rur Rural or pastoral (area, subject to Farmland Protection Act if less than 0.75 buildings per acre)
S_ Sample, number, count or sum prefix (this may be dropped unless needed for clarity)
SA South Asian (Indian, race-ethnicity)
Sch School
SFem Single Female (relationship)
Sis Straight individual (sexual orientation)
Sgl Single (relationship)
SMal Single Male (relationship)
SqFt Square Feet, U.S. square foot (area, prefix with “p” for area relationships, as in “pSqFt” for “per Square Foot”) (Square meters may be converted to square feet with the following formula: SqMet*10.7639 =SqFt)
SqMet Square Meters, international square meter (area, prefix with “p” for areal relationships, as in “pSqMet” for “per Square Meter”) (Square meters may be converted to acres with the following formula: SqMet*0.000247105 = Acres)
SqMi Square Miles, U.S. square mile (area, prefix with “p” for area relationships, as in “pSqMi” for “per Square Mile”) (Square meters may be converted to square miles with the following formula: SqMet*.0000003861 = SqMi)
Sz Size
TAZ Traffic Analysis Zone (statistical area)
Tr Census Tract (statistical area)
Trans Transsexual (sexual orientation)
Tot Total, universe
U Under (age prefix); also most extreme abbreviation of “Und”
Und Under (age prefix); U is most extreme abbreviation
_Up And Over, Plus (age or size suffix)
Urb Urban or Urbanized (area)
USD U.S. Dollars (substituted for “$”, prefixed with inflation base year if applicable)
Vot Voting-age (persons age 18 and over)
Vac Vacancy or vacant (housing units)
_w_ with (conjunction in field name)
White White or Anglo (race-ethnicity); Wh as extreme abbreviation
Yr Year(s, age)
Rule: If the abbreviation can be fully or partly spelled out within the 10-character limit of the old DBF field header limitations, including prefix/suffix, then that is preferred. E.g. NotHL instead of NHL, whereas NotHispLat allows no room for a prefix or suffix to be added.
Capitalization/Number/Punctuation Rule: UpperCamelCase, with the first letter of every word capitalized, allowing elimination of spaces and all punctuation except underscores, consistent with DBF 4.0+. Field names cannot begin with a number.
(Use UpperCamelCase, and the above abbreviations for classes common to the Census)

From whence doth such craziness arise?

Part of what has informed my thinking about this over the years included datasets such as the example following. Actually, many predecessors came before this; the data houses that produced products from Census and other data sources were known under various corporate identities over time, but have largely now consolidated. I used some fieldnames used by Geolytics as an example below, since I’ve done some business with that firm recently.

Extended Basic Estimates from Geolytics

Extended Basic Estimates 2014 / 2019 /2024
for Harris County block groups
1-800-577-6717
(Geolytics, Inc., in Somerville, NJ: website at http://www.geolytics.com/USCensus,ExtendedEstimates,Data,Features,Products.asp).

Methodology they used:  http://www.geolytics.com/USCensus,Estimates-Projections,Data,Methodology,Products.asp

The following list of variables came with their products.

Geolytics used prefix “s” for summary file data (decennial Census), “e” for current year Estimate, a “p” for the 5-year Projection, “q” for the 10-year projection, and “r” for the 15-year projection. Eg:

Example Geographic Identifiers

AREAKEY Block Group FIPS Code
AREANAME Block Group Name
LAT Latitude
LON Longitude
SQMILES Square Miles
STUSAB State Abbreviation
STATECE State Census Code
STATE State FIPS Code
STNAME State Name
REGION State Region Code
DIVISION State Division Code
COUNTY County FIPS Code
CNTYNAM County Name
MACODE Metropolitan Area Code
MANAME Metropolitan Area Name
ZIP_CODE ZIP Code
PONAME Post Office Name
CITYNAME City Name
CITYCODE City Code
CBSANAME Core Based Statistical Area Name
CBSACODE Core Based Statistical Area Code
CBSATYPE Core Based Statistical Area Type
AREACODE Area Code

Example Summary Data

STOTPOP Total estimated Population
STOTCNPOP Total Census 2000 Population
SPOPCHPCT Pop Change (per cent)
SPOPDENS Population Density
SPOP0_5 Population age 0-5 (2010
SPOP6_17 Population age 6-17
SPOP0_4 Population 0-4
SPOP5_9 Population 5-9
SPOP10_14 Population 10-14
SPOP15_29 Population 15-20
SPOP20_24 Population 20-24
SPOP25_29 Population 25-29
SPOP30_34 Population 30-34
SPOP35_39 Population 35-39
SPOP40_44 Population 40-44
SPOP45_49 Population 45-49
SPOP50_54 Population 50-54
SPOP55_59 Population 55-59
SPOP60_64 Population 60-64
SPOP65_69 Population 65-69
SPOP70_74 Population 70-74
SPOP75_79 Population 75-79
SPOP80_84 Population 80-84
SPOP85P Population 85 plus
SMEDAGE Median Age
SPOPWA Total White Alone population
SPOPBA Total Black Alone population
SPOPNA Total Native Americans Alone population
SPOPAA Total Asian Alone population
SPOPPA Total Pacific Alone population
SPOPR2 Total 2 or more races population
SPOPHS Total Hispanic population
SPOPWN Total White non-Hispanic population
STOTMALES Total Males
STOTFEMAL Total Females
SPOPWAM Total White Males Alone
SPOPBAM Total Black Males Alone
SPOPNAM Total Native Americans Males Alone
SPOPAAM Total Asian Males Alone
SPOPPAM Total Pacific Males Alone
SPOPR2M Total 2 or more races Males
SPOPHSM Total Hispanic Males
SPOPWNM Total White Alone non-Hispanic Males
SPOPWAF Total White Females Alone
SPOPBAF Total Black Females Alone
SPOPNAF Total Native Americans Females Alone
SPOPAAF Total Asian Females Alone
SPOPPAF Total Pacific Females Alone
SPOPR2F Total 2 or more races Females
SPOPHSF Total Hispanic Females
SPOPWNF Total White non-Hispanic Females
STOTHH Total estimated Households
STOTCNSHH Total Census 2000 Households
SHHCHGE Household Change
SAVGHHSZE Average HH Size
SONEPRSHH One Person Households
STOTFAM Total Families
STOTHU Total Housing Units
SHUOWNER Owner
SHURENTER Renter
SHUVACANT Vacant Housing Units
SHUNDR10K Households w/ income under $10,000
SH10_15 HH w/ income $10,000-$14,999
SH15_20 HH w/ income $15,000-$19,999
SH20_25 HH w/ income $20,000-$24,999
SH25_30 HH w/ income $25,000-$29,999
SH30_35 HH w/ income $30,000-$34,999
SH35_40 HH w/ income $35,000-$39,999
SH40_45 HH w/ income $40,000-$44,999
SH45_50 HH w/ income $45,000-$49,999
SH50_60 HH w/ income $50,000-$59,999
SH60_75 HH w/ income $60,000-$74,999
SH75_100 HH w/ income $75,000-$99,999
SH100_125 HH w/ income $100,000-$124,999
SH125_150 HH w/ income $125,000-$149,999
SH150_200 HH w/ income $150,000-$199,999
SH200KP HH w/ income $200,000+
SHMEDINC Median HH income
SHAGGINC Aggregate HH Income
SHAVGINC Average HH Income
SPERCPINC Per Capita Income

Geolytics followed some of the C++ programmers’ customs by fully capitalizing all fieldnames and relying on code words such as H for Household and INC for Income undelimited due to shape file (.dbf) name space constraints. Using “S” as a prefix to denote summary data as opposed to “E” for current year estimate or “P, Q and R” for projected data informed my suggestion to use “E”stimate, “M”argin of error, and “P”ercent in my Census field name convention.

Options for projecting demographic data

Demographic Projections are generally not viewed as trustworthy in any dynamically vibrant area such as Los Angeles or Houston. These areas have differential natural increase among various subgroups with heavy both internal and external migration overlaid. If you wish to project data, I would suggest contacting a former state demographer for lessons, or better yet buying someone else’s plausibly deniable projected data! When my workplace looked into projected data for population over the age of 50 in the year prior to the 2010 Census a decade ago, this was my advice:

  1. Free data.  We contacted the office of the TX state demographer, Dr. Lloyd Potter.  Published data is projected at the county level.  No free information will reliably project the number of individuals aged 50+, especially for such a rapidly changing subarea of Harris County as Precinct 4.  A 2020 projection for all of Harris County from the State Data Center was developed as HarrisCountyProjection2020.xls
  2. Prepared data from a demographic data seller.  Geolytics, Inc. at 1-800-577-6717 can provide “Extended Basic Estimates 2014 / 2019 /2024” for either block groups, tracts, or ZIP codes within Harris County at a cost of $300.   After selecting the geographic elements that approximate Precinct 4 in a GIS, the answer to your question can be calculated by adding together the following variables, first for the 2014 estimate and then for the 2024 projection:
    1. Population 50-54
    2. Population 55-59
    3. Population 60-64
    4. Population 65-69
    5. Population 70-74
    6. Population 75-79
    7. Population 80-84
    8. Population 85 plus
  3. Expert projection prepared by a professional demographer at cost.  Dr. Potter may be willing to further discuss with you this option and/or some recommendations at 210-458-6530 or by email  at lloyd.potter@utsa.edu.  Please don’t be surprised if professional demographers offer to project figures no further than five years into the future with a reasonable certainty.
  4. Finally, you can try your own hand at predicting an uncertain future by reviewing past counts and estimates from the U. S. Census Bureau for the Census Tracts that approximate (though not perfectly) Precinct 4 as we begin to show below:

[Table 1]

U. S. Census Bureau ACS 5-Year Rolling Estimates for (Approximately) Harris County Precinct 4 based on all census tracts that are located within, in whole or in part.

Year       Persons Age 50+              Total Population

2011       254,493                                 1,049,811

2012       268,248                                 1,080,509

2013       280,888                                 1,107,919

Another example applied to block group data from a 5-year (2012-2016) ACS dataset:

Data Dictionary for BG_Precinct_Data_2016 Description 1st 10 Char (dbf)
OBJECTID Geographic Object ID OBJECTID
STATEFP10 State FIPS code STATEFP10
COUNTYFP10 County FIPS code COUNTYFP10
TRACTCE10 Tract FIPS code TRACTCE10
BLKGRPCE10 Block Group FIPS code BLKGRPCE10
GEOID10 Geographic ID (2010 Census) GEOID10
NAMELSAD10 NAMELSAD (2010 Census) NAMELSAD10
MTFCC10 MTFCC (2010 Census) MTFCC10
FUNCSTAT10 Functional Status Code (2010 Census) FUNCSTAT10
ALAND10 Area Land in Square Meters (2010 Census) ALAND10
AWATER10 Area Water in Square Meters (2010 Census) AWATER10
INTPTLAT10 Internal point latitude (2010 Census) INTPTLAT10
INTPTLON10 Internal point longitude (2010 Census) INTPTLON10
FIPS Federal Information Processing Standards code FIPS
TotPop Total Population , any race or origin TotPop
NotHL Population Not of Hispanic or Latino Origin NotHL
HispLat Hispanic or Latino Origin Population HispLat
WhiteAlone White alone, any origin WhiteAlone
NHLWhiteAlone White alone, not of Hispanic or Latino origin NHLWhiteAl
HLWhiteAlone White alone, of Hispanic or Latino origin HLWhiteAlo
BlackAlone Black alone, any origin BlackAlone
NHLBlackAlone Black alone, not of Hispanic or Latino origin NHLBlackAl
HLBlackAlone Black alone, of Hispanic or Latino origin HLBlackAlo
AIANNHOPIAlone American Indian and Alaska Native or Native Hawaiian and Other Pacific Islander alone, any origin AIANNHOPIA
NHLAIANNHOPIAlone American Indian and Alaska Native or Native Hawaiian and Other Pacific Islander alone, not of Hispanic or Latino origin NHLAIANNHO
HLAIANNHOPIAlone American Indian and Alaska Native or Native Hawaiian and Other Pacific Islander alone, of Hispanic or Latino origin HLAIANNHOP
AsianAlone Asian alone, any origin AsianAlone
NHLAsianAlone Asian alone, not of Hispanic or Latino origin NHLAsianAl
HLAsianAlone Asian alone, of Hispanic or Latino origin HLAsianAlo
OtherAlone Other alone, any origin OtherAlone
NHLOtherAlone Other alone, not of Hispanic or Latino origin NHLOtherAl
HLOtherAlone Other alone, of Hispanic or Latino origin HLOtherAlo
Multiracial Two or more races, of any origin Multiracia
NHLMultiracial Two or more races, not of Hispanic or Latino origin NHLMultira
HLMultiracial Two or more races, of Hispanic or Latino origin HLMultirac
U18Pop Population under 18 U18Pop
U18NotHL Population under 18 not of Hispanic or Latino Origin U18NotHL
U18HispLat Hispanic or Latino Origin population under 18 U18HispLat
U18WhiteAlone White alone, any origin, under 18 U18WhiteAl
U18NHLWhiteAlone White alone, not of Hispanic or Latino origin, under 18 U18NHLWhit
U18HLWhiteAlone White alone, of Hispanic or Latino origin, under 18 U18HLWhite
U18BlackAlone Black alone, any origin, under 18 U18BlackAl
U18NHLBlackAlone Black alone, not of Hispanic or Latino origin, under 18 U18NHLBlac
U18HLBlackAlone Black alone, of Hispanic or Latino origin, under 18 U18HLBlack
U18AIANNHOPIAlone American Indian and Alaska Native or Native Hawaiian and Other Pacific Islander alone, any origin, under 18 U18AIANNHO
U18NHLAIANNHOPIAlone American Indian and Alaska Native or Native Hawaiian and Other Pacific Islander alone, not of Hispanic or Latino origin, under 18 U18NHLAIAN
U18HLAIANNHOPIAlone American Indian and Alaska Native or Native Hawaiian and Other Pacific Islander alone, of Hispanic or Latino origin, under 18 U18HLAIANN
U18AsianAlone Asian alone, any origin, under 18 U18AsianAl
U18NHLAsianAlone Asian alone, not of Hispanic or Latino origin, under 18 U18NHLAsia
U18HLAsianAlone Asian alone, of Hispanic or Latino origin, under 18 U18HLAsian
U18OtherAlone Other alone, any origin, under 18 U18OtherAl
U18NHLOtherAlone Other alone, not of Hispanic or Latino origin, under 18 U18NHLOthe
U18HLOtherAlone Other alone, of Hispanic or Latino origin, under 18 U18HLOther
U18Multiracial Two or more races, of any origin, under 18 U18Multira
U18NHLMultiracial Two or more races, not of Hispanic or Latino origin, under 18 U18NHLMult
U18HLMultiracial Two or more races, of Hispanic or Latino origin, under 18 U18HLMulti
VotPop Population age 18 or older VotPop
VotNotHL Population age 18 or older not of Hispanic or Latino origin VotNotHL
VotHispLat Population age 18 or older of Hispanic or Latino origin VotHispLat
VotWhiteAlone White alone, any origin, age 18 or older VotWhiteAl
VotNHLWhiteAlone White alone, not of Hispanic or Latino origin, age 18 or older VotNHLWhit
VotHLWhiteAlone White alone, of Hispanic or Latino origin, age 18 or older VotHLWhite
VotBlackAlone Black alone, any origin, age 18 or older VotBlackAl
VotNHLBlackAlone Black alone, not of Hispanic or Latino origin, age 18 or older VotNHLBlac
VotHLBlackAlone Black alone, of Hispanic or Latino origin, age 18 or older VotHLBlack
VotAIANNHOPIAlone American Indian and Alaska Native or Native Hawaiian and Other Pacific Islander alone, any origin, age 18 or older VotAIANNHO
VotNHLAIANNHOPIAlone American Indian and Alaska Native or Native Hawaiian and Other Pacific Islander alone, not of Hispanic or Latino origin, age 18 or older VotNHLAIAN
VotHLAIANNHOPIAlone American Indian and Alaska Native or Native Hawaiian and Other Pacific Islander alone, of Hispanic or Latino origin, age 18 or older VotHLAIANN
VotAsianAlone Asian alone, any origin, age 18 or older VotAsianAl
VotNHLAsianAlone Asian alone, not of Hispanic or Latino origin, age 18 or older VotNHLAsia
VotHLAsianAlone Asian alone, of Hispanic or Latino origin, age 18 or older VotHLAsian
VotOtherAlone Other alone, any origin, age 18 or older VotOtherAl
VotNHLOtherAlone Other alone, not of Hispanic or Latino origin, age 18 or older VotNHLOthe
VotHLOtherAlone Other alone, of Hispanic or Latino origin, age 18 or older VotHLOther
VotMultiracial Two or more races, of any origin, age 18 or older VotMultira
VotNHLMultiracial Two or more races, not of Hispanic or Latino origin, age 18 or older VotNHLMult
VotHLMultiracial Two or more races, of Hispanic or Latino origin, age 18 or older VotHLMulti
HousingUnits Total Housing Units HousingUni
OccupiedHU Occupied Housing Units OccupiedHU
VacantHU Vacant Housing Units VacantHU
HHPop Persons living in households HHPop
AvgHHSize Average household size AvgHHSize
GQPop Persons living in group quarters GQPop
GQInstitutionalized Persons living in institutionalized group quarters GQInstitut
GQCorrectionalFacilities Persons living in correctional facilities group quarters GQCorrecti
GQJuvenileFacilities Persons living in juvenile facilities group quarters GQJuvenile
GQNursingSNIF Persons living in nursing facilities group quarters GQNursingS
GQOtherInstitutional Persons living in other group quarters GQOtherIns
GQNoninstitutionalized Persons living in noninstitutionalized group quarters GQNoninsti
GQCollegeUniversity Persons living in college facilities group quarters GQCollegeU
GQMilitaryQuarters Persons living in military facilities group quarters GQMilitary
GQOtherNoninstitutional Persons living in other group quarters GQOtherNon
TrGeoID10 Tract Census FIPS Code (2010 Census vintage), six characters TrGeoID10
GEOID Geographic identifier (2010 Census vintage), retained from table join GEOID
TotPop10 Total Population (2010 Census) TotPop10
Age55Up Persons aged 55 years or older (2010 Census) Age55Up
STBGGEOID State USA abbreviation concatenated with Tract and Block Group Geographic identifiers (2010 Census vintage), for table joins that use this structure STBGGEOID
geoname Geographic name (Block Group, Tract, and County) geoname
stusab Postal state code abbreviation, 2 alpha characters stusab
countyname County name (County, State USA) countyname
state State FIPS code state
county County FIPS code county
tract Tract FIPS code tract
blkgrp Blkgrp FIPS code blkgrp
low Low income persons (HUD est), at or below 50% Area Median Income low
lowmod Low-Mod income persons (HUD est.) at or below 80% Area Median Income. The numerator in a Low-Mod income percentage. lowmod
LMMI Low-Mod-Middle income persons (HUD est.) at or below 120% Area Median Income LMMI
lowmoduniv Low-Mod income persons universe (HUD est.), similar to persons in households with an estimable income. The denominator in a Low-Mod income percentage. lowmoduniv
lowmod_pct Low-Mod income percentage (HUD est.) calculated from Low-Mod income persons divided by Low-Mod persons universe lowmod_pct
LMBA Low-Mod income benefit area flag. Qualified block groups contain “1” in this field. 1 denotes a block group with 51% or more low to moderate income persons. LMBA
Pct1Percent Percent of land area within Precinct 1, used as a multipier in Precinct demographic calculations Pct1Percen
Pct2Percent Percent of land area within Precinct 2, used as a multipier in Precinct demographic calculations Pct2Percen
Pct3Percent Percent of land area within Precinct 3, used as a multipier in Precinct demographic calculations Pct3Percen
Pct4Percent Percent of land area within Precinct 4, used as a multipier in Precinct demographic calculations Pct4Percen
GeoID2_dbl Geographic identifier (2010 Census vintage), double (numeric) data format GeoID2_dbl
P1Tot Total Population in Commissioner Precinct 1 P1Tot
P1STotPop2010 Total Population in 2010 Commissioner Precinct 1 P1STotPop2
P1ETotPop2014 Total Population in 2014 Commissioner Precinct 1 P1ETotPop2
P1ETotPopChg2010_2014 Population Change 2010 to 2014 in Commission Precinct 1 P1ETotPopC
P1ETot60Plus Persons aged 60 plus in Commissioner Precinct 1 P1ETot60Pl
P1MTot60Plus Margin of Error for Persons aged 60 plus in Commissioner Precinct 1 P1MTot60Pl
P1PTot60Plus Percent Persons aged 60 plus in Commissioner Precinct 1 P1PTot60Pl
P1MPTot60Plus Margin of Error for Percent Persons aged 60 plus in Commissioner Precinct 1 P1MPTot60P
P1PDisability Percent Persons with disability in Commission Precinct 1 P1PDisabil
P1MPDisability Margin of Error for Percent Persons with disability in Commission Precinct 1 P1MPDisabi
P1EDisability Persons with disability in Commission Precinct 1 P1EDisabil
P1MDisability Margin of Error for Persons with disability in Commission Precinct 1 P1MDisabil
P1EMedAge Median age of persons in Commission Precinct 1 P1EMedAge
P1MMedAge Margin of Error for Median age of persons in Commission Precinct 1 P1MMedAge
P1EFemPop Female population in Commission Precinct 1 P1EFemPop
P1MFemPop Margin of Error for Female population in Commission Precinct 1 P1MFemPop
P1PFemPop Percent Female population in Commission Precinct 1 P1PFemPop
P1EMalPop Male population in Commission Precinct 1 P1EMalPop
P1MMalPop Margin of Error for Male population in Commission Precinct 1 P1MMalPop
P1PMalPop Percent Male population in Commission Precinct 1 P1PMalPop
P1EMinority Minority population in Commission Precinct 1 P1EMinorit
P1MMinority Margin of Error for Minority population in Commission Precinct 1 P1MMinorit
P1PMinority Percent Minority population in Commission Precinct 1 P1PMinorit
P1EAvgHHSz Average household size in Commission Precinct 1 P1EAvgHHSz
P1MAvgHHSz Margin of Error for Average household size in Commission Precinct 1 P1MAvgHHSz
P1PBelowPoverty Percent Persons below poverty level in Commissioners Precinct 1 P1PBelowPo
P1MPBelowPoverty Margin of Error for Percent Persons below poverty level in Commissioners Precinct 1 P1MPBelowP
P1EBelowPoverty Persons below poverty level in Commissioners Precinct 1 P1EBelowPo
P1MBelowPoverty Margin of Error for Persons below poverty level in Commissioners Precinct 1 P1MBelowPo
P1EOver25Yrs Education universe, people age 25 years and older in Commissioner Precinct 1 P1EOver25Y
P1MOver25Yrs Margin of Error for Education universe, people age 25 years and older in Commissioner Precinct 1 P1MOver25Y
P1EEduAtLTHS Education less than High School, people age 25 years and older in Commissioner Precinct 1 P1EEduAtLT
P1MEduAtLTHS Margin of Error for Education less than High School, people age 25 years and older in Commissioner Precinct 1 P1MEduAtLT
P1PEduAtLTHS Percent Education less than High School, people age 25 years and older in Commissioner Precinct 1 P1PEduAtLT
P1EEdAtHSGED Education High School or GED, people age 25 years and older in Commissioner Precinct 1 P1EEdAtHSG
P1MEdAtHSGED Margin of Error for Education High School or GED, people age 25 years and older in Commissioner Precinct 1 P1MEdAtHSG
P1PEdAtHSGED Percent Education High School or GED, people age 25 years and older in Commissioner Precinct 1 P1PEdAtHSG
P1EEduAtSCPlus Education some college or more, people age 25 years and older in Commissioner Precinct 1 P1EEduAtSC
P1MEduAtSCPlus Margin of Error for Education some college or more, people age 25 years and older in Commissioner Precinct 1 P1MEduAtSC
P1PEduAtSCPlus Percent Education some college or more, people age 25 years and older in Commissioner Precinct 1 P1PEduAtSC
P1EHispLat Hispanic or Latino Population in Commissioner Precinct 1 P1EHispLat
P1MHispLat Margin of Error for Hispanic or Latino Population in Commissioner Precinct 1 P1MHispLat
P1PHispLat Percent Hispanic or Latino Population in Commissioner Precinct 1 P1PHispLat
P1ENHLWhA White alone, not of Hispanic or Latino origin in Commission Precinct 1 P1ENHLWhA
P1MNHLWhA Margin of Error for White alone, not of Hispanic or Latino origin in Commission Precinct 1 P1MNHLWhA
P1PNHLWhA Percent White alone, not of Hispanic or Latino origin in Commission Precinct 1 P1PNHLWhA
P1ENHLBlA Black or African American alone, not of Hispanic or Latino origin in Commission Precinct 1 P1ENHLBlA
P1MNHLBlA Margin of Error for Black or African American alone, not of Hispanic or Latino origin in Commission Precinct 1 P1MNHLBlA
P1PNHLBlA Percent Black or African American alone, not of Hispanic or Latino origin in Commission Precinct 1 P1PNHLBlA
P1ENHLAsA Asian alone, not of Hispanic or Latino origin in Commission Precinct 1 P1ENHLAsA
P1MNHLAsA Margin of Error for Asian alone, not of Hispanic or Latino origin in Commission Precinct 1 P1MNHLAsA
P1PNHLAsA Percent Asian alone, not of Hispanic or Latino origin in Commission Precinct 1 P1PNHLAsA
P1ENHLOther American Indian and Alaska Native alone, or Native Hawaiian and Other Pacific Islander alone, not of Hispanic or Latino origin, some other race or 2 or more races in Commission Precinct 1 P1ENHLOthe
P1MNHLOther Margin of Error for American Indian and Alaska Native alone or Native Hawaiian and Other Pacific Islander alone, not of Hispanic or Latino origin, some other race or 2 or more races in Commission Precinct 1 P1MNHLOthe
P1PNHLOther Percent American Indian and Alaska Native alone or Native Hawaiian and Other Pacific Islander alone, not of Hispanic or Latino origin, some other race or 2 or more races in Commission Precinct 1 P1PNHLOthe
P1ETotHH Households, or Occupied Housing Units in Commissioner Precinct 1 P1ETotHH
P1MTotHH Margin of Error for Households, or Occupied Housing Units in Commissioner Precinct 1 P1MTotHH
P1EHHwU18 Households with persons aged under 18 years P1EHHwU18
P1MHHwU18 Margin of Error for Households with persons aged under 18 years P1MHHwU18
P1PHHwU18 Percent Households with persons aged under 18 years in Commissioner Precinct 1 P1PHHwU18
P1E0_30KUSDHHI Households with income between zero and $30,000 in Commissioner Precinct 1 P1E0_30KUS
P1M0_30KUSDHHI Margin of Error for Households with income between zero and $30,000 in Commissioner Precinct 1 P1M0_30KUS
P1P0_30KUSDHHI Percent Households with income between zero and $30,000 in Commissioner Precinct 1 in Commissioner Precinct 1 P1P0_30KUS
P1E30_MAHHI Households with income between $30,000 and median area household income in Commissioner Precinct 1 P1E30_MAHH
P1M30_MAHHI Margin of Error for Households with income between $30,000 and median area household income in Commissioner Precinct 1 P1M30_MAHH
P1P30_MAHHI Percent Households with income between $30,000 and median area household income in Commissioner Precinct 1 P1P30_MAHH
P1PBelMedHHInc Percent of households with income at or below median area household income in Commissioner Precinct 1 P1PBelMedH
P1ESFHU Single Family housing units in Commissioner Precinct 1 P1ESFHU
P1MSFHU Margin of Error for Single Family housing units in Commissioner Precinct 1 P1MSFHU
P1PSFHU Percent Single Family housing units in Commissioner Precinct 1 P1PSFHU
P1EMFHU Multifamily housing units in Commissioner Precinct 1 P1EMFHU
P1MMFHU Margin of Error for Multifamily housing units in Commissioner Precinct 1 P1MMFHU
P1PMFHU Percent Multifamily housing units in Commissioner Precinct 1 P1PMFHU
P1EMHHU Mobile home housing units in Commissioner Precinct 1 P1EMHHU
P1MMHHU Margin of Error for Mobile home housing units in Commissioner Precinct 1 P1MMHHU
P1PMHHU Percent Mobile home housing units in Commissioner Precinct 1 P1PMHHU
P2Tot Total Population in Commissioner Precinct 2 P2Tot
P2STotPop2010 Total Population in 2010 Commissioner Precinct 2 P2STotPop2
P2ETotPop2014 Total Population in 2014 Commissioner Precinct 2 P2ETotPop2
P2ETotPopChg2010_2014 Population Change 2010 to 2014 in Commission Precinct 2 P2ETotPopC
P2ETot60Plus Persons aged 60 plus in Commissioner Precinct 2 P2ETot60Pl
P2MTot60Plus Margin of Error for Persons aged 60 plus in Commissioner Precinct 2 P2MTot60Pl
P2PTot60Plus Percent Persons aged 60 plus in Commissioner Precinct 2 P2PTot60Pl
P2MPTot60Plus Margin of Error for Percent Persons aged 60 plus in Commissioner Precinct 2 P2MPTot60P
P2PDisability Percent Persons with disability in Commission Precinct 2 P2PDisabil
P2MPDisability Margin of Error for Percent Persons with disability in Commission Precinct 2 P2MPDisabi
P2EDisability Persons with disability in Commission Precinct 2 P2EDisabil
P2MDisability Margin of Error for Persons with disability in Commission Precinct 2 P2MDisabil
P2EMedAge Median age of persons in Commission Precinct 2 P2EMedAge
P2MMedAge Margin of Error for Median age of persons in Commission Precinct 2 P2MMedAge
P2EFemPop Female population in Commission Precinct 2 P2EFemPop
P2MFemPop Margin of Error for Female population in Commission Precinct 2 P2MFemPop
P2PFemPop Percent Female population in Commission Precinct 2 P2PFemPop
P2EMalPop Male population in Commission Precinct 2 P2EMalPop
P2MMalPop Margin of Error for Male population in Commission Precinct 2 P2MMalPop
P2PMalPop Percent Male population in Commission Precinct 2 P2PMalPop
P2EMinority Minority population in Commission Precinct 2 P2EMinorit
P2MMinority Margin of Error for Minority population in Commission Precinct 2 P2MMinorit
P2PMinority Percent Minority population in Commission Precinct 2 P2PMinorit
P2EAvgHHSz Average household size in Commission Precinct 2 P2EAvgHHSz
P2MAvgHHSz Margin of Error for Average household size in Commission Precinct 2 P2MAvgHHSz
P2PBelowPoverty Percent Persons below poverty level in Commissioners Precinct 2 P2PBelowPo
P2MPBelowPoverty Margin of Error for Percent Persons below poverty level in Commissioners Precinct 2 P2MPBelowP
P2EBelowPoverty Persons below poverty level in Commissioners Precinct 2 P2EBelowPo
P2MBelowPoverty Margin of Error for Persons below poverty level in Commissioners Precinct 2 P2MBelowPo
P2EOver25Yrs Education universe, people age 25 years and older in Commissioner Precinct 2 P2EOver25Y
P2MOver25Yrs Margin of Error for Education universe, people age 25 years and older in Commissioner Precinct 2 P2MOver25Y
P2EEduAtLTHS Education less than High School, people age 25 years and older in Commissioner Precinct 2 P2EEduAtLT
P2MEduAtLTHS Margin of Error for Education less than High School, people age 25 years and older in Commissioner Precinct 2 P2MEduAtLT
P2PEduAtLTHS Percent Education less than High School, people age 25 years and older in Commissioner Precinct 2 P2PEduAtLT
P2EEdAtHSGED Education High School or GED, people age 25 years and older in Commissioner Precinct 2 P2EEdAtHSG
P2MEdAtHSGED Margin of Error for Education High School or GED, people age 25 years and older in Commissioner Precinct 2 P2MEdAtHSG
P2PEdAtHSGED Percent Education High School or GED, people age 25 years and older in Commissioner Precinct 2 P2PEdAtHSG
P2EEduAtSCPlus Education some college or more, people age 25 years and older in Commissioner Precinct 2 P2EEduAtSC
P2MEduAtSCPlus Margin of Error for Education some college or more, people age 25 years and older in Commissioner Precinct 2 P2MEduAtSC
P2PEduAtSCPlus Percent Education some college or more, people age 25 years and older in Commissioner Precinct 2 P2PEduAtSC
P2EHispLat Hispanic or Latino Population in Commissioner Precinct 2 P2EHispLat
P2MHispLat Margin of Error for Hispanic or Latino Population in Commissioner Precinct 2 P2MHispLat
P2PHispLat Percent Hispanic or Latino Population in Commissioner Precinct 2 P2PHispLat
P2ENHLWhA White alone, not of Hispanic or Latino origin in Commission Precinct 2 P2ENHLWhA
P2MNHLWhA Margin of Error for White alone, not of Hispanic or Latino origin in Commission Precinct 2 P2MNHLWhA
P2PNHLWhA Percent White alone, not of Hispanic or Latino origin in Commission Precinct 2 P2PNHLWhA
P2ENHLBlA Black or African American alone, not of Hispanic or Latino origin in Commission Precinct 2 P2ENHLBlA
P2MNHLBlA Margin of Error for Black or African American alone, not of Hispanic or Latino origin in Commission Precinct 2 P2MNHLBlA
P2PNHLBlA Percent Black or African American alone, not of Hispanic or Latino origin in Commission Precinct 2 P2PNHLBlA
P2ENHLAsA Asian alone, not of Hispanic or Latino origin in Commission Precinct 2 P2ENHLAsA
P2MNHLAsA Margin of Error for Asian alone, not of Hispanic or Latino origin in Commission Precinct 2 P2MNHLAsA
P2PNHLAsA Percent Asian alone, not of Hispanic or Latino origin in Commission Precinct 2 P2PNHLAsA
P2ENHLOther American Indian and Alaska Native alone, or Native Hawaiian and Other Pacific Islander alone, not of Hispanic or Latino origin, some other race or 2 or more races in Commission Precinct 2 P2ENHLOthe
P2MNHLOther Margin of Error for American Indian and Alaska Native alone or Native Hawaiian and Other Pacific Islander alone, not of Hispanic or Latino origin, some other race or 2 or more races in Commission Precinct 2 P2MNHLOthe
P2PNHLOther Percent American Indian and Alaska Native alone or Native Hawaiian and Other Pacific Islander alone, not of Hispanic or Latino origin, some other race or 2 or more races in Commission Precinct 2 P2PNHLOthe
P2ETotHH Households, or Occupied Housing Units in Commissioner Precinct 2 P2ETotHH
P2MTotHH Margin of Error for Households, or Occupied Housing Units in Commissioner Precinct 2 P2MTotHH
P2EHHwU18 Households with persons aged under 18 years P2EHHwU18
P2MHHwU18 Margin of Error for Households with persons aged under 18 years P2MHHwU18
P2PHHwU18 Percent Households with persons aged under 18 years in Commissioner Precinct 2 P2PHHwU18
P2E0_30KUSDHHI Households with income between zero and $30,000 in Commissioner Precinct 2 P2E0_30KUS
P2M0_30KUSDHHI Margin of Error for Households with income between zero and $30,000 in Commissioner Precinct 2 P2M0_30KUS
P2P0_30KUSDHHI Percent Households with income between zero and $30,000 in Commissioner Precinct 2 in Commissioner Precinct 2 P2P0_30KUS
P2E30_MAHHI Households with income between $30,000 and median area household income in Commissioner Precinct 2 P2E30_MAHH
P2M30_MAHHI Margin of Error for Households with income between $30,000 and median area household income in Commissioner Precinct 2 P2M30_MAHH
P2P30_MAHHI Percent Households with income between $30,000 and median area household income in Commissioner Precinct 2 P2P30_MAHH
P2PBelMedHHInc Percent of households with income at or below median area household income in Commissioner Precinct 2 P2PBelMedH
P2ESFHU Single Family housing units in Commissioner Precinct 2 P2ESFHU
P2MSFHU Margin of Error for Single Family housing units in Commissioner Precinct 2 P2MSFHU
P2PSFHU Percent Single Family housing units in Commissioner Precinct 2 P2PSFHU
P2EMFHU Multifamily housing units in Commissioner Precinct 2 P2EMFHU
P2MMFHU Margin of Error for Multifamily housing units in Commissioner Precinct 2 P2MMFHU
P2PMFHU Percent Multifamily housing units in Commissioner Precinct 2 P2PMFHU
P2EMHHU Mobile home housing units in Commissioner Precinct 2 P2EMHHU
P2MMHHU Margin of Error for Mobile home housing units in Commissioner Precinct 2 P2MMHHU
P2PMHHU Percent Mobile home housing units in Commissioner Precinct 2 P2PMHHU
P3Tot Total Population in Commissioner Precinct 3 P3Tot
P3STotPop2010 Total Population in 2010 Commissioner Precinct 3 P3STotPop2
P3ETotPop2014 Total Population in 2014 Commissioner Precinct 3 P3ETotPop2
P3ETotPopChg2010_2014 Population Change 2010 to 2014 in Commission Precinct 3 P3ETotPopC
P3ETot60Plus Persons aged 60 plus in Commissioner Precinct 3 P3ETot60Pl
P3MTot60Plus Margin of Error for Persons aged 60 plus in Commissioner Precinct 3 P3MTot60Pl
P3PTot60Plus Percent Persons aged 60 plus in Commissioner Precinct 3 P3PTot60Pl
P3MPTot60Plus Margin of Error for Percent Persons aged 60 plus in Commissioner Precinct 3 P3MPTot60P
P3PDisability Percent Persons with disability in Commission Precinct 3 P3PDisabil
P3MPDisability Margin of Error for Percent Persons with disability in Commission Precinct 3 P3MPDisabi
P3EDisability Persons with disability in Commission Precinct 3 P3EDisabil
P3MDisability Margin of Error for Persons with disability in Commission Precinct 3 P3MDisabil
P3EMedAge Median age of persons in Commission Precinct 3 P3EMedAge
P3MMedAge Margin of Error for Median age of persons in Commission Precinct 3 P3MMedAge
P3EFemPop Female population in Commission Precinct 3 P3EFemPop
P3MFemPop Margin of Error for Female population in Commission Precinct 3 P3MFemPop
P3PFemPop Percent Female population in Commission Precinct 3 P3PFemPop
P3EMalPop Male population in Commission Precinct 3 P3EMalPop
P3MMalPop Margin of Error for Male population in Commission Precinct 3 P3MMalPop
P3PMalPop Percent Male population in Commission Precinct 3 P3PMalPop
P3EMinority Minority population in Commission Precinct 3 P3EMinorit
P3MMinority Margin of Error for Minority population in Commission Precinct 3 P3MMinorit
P3PMinority Percent Minority population in Commission Precinct 3 P3PMinorit
P3EAvgHHSz Average household size in Commission Precinct 3 P3EAvgHHSz
P3MAvgHHSz Margin of Error for Average household size in Commission Precinct 3 P3MAvgHHSz
P3PBelowPoverty Percent Persons below poverty level in Commissioners Precinct 3 P3PBelowPo
P3MPBelowPoverty Margin of Error for Percent Persons below poverty level in Commissioners Precinct 3 P3MPBelowP
P3EBelowPoverty Persons below poverty level in Commissioners Precinct 3 P3EBelowPo
P3MBelowPoverty Margin of Error for Persons below poverty level in Commissioners Precinct 3 P3MBelowPo
P3EOver25Yrs Education universe, people age 25 years and older in Commissioner Precinct 3 P3EOver25Y
P3MOver25Yrs Margin of Error for Education universe, people age 25 years and older in Commissioner Precinct 3 P3MOver25Y
P3EEduAtLTHS Education less than High School, people age 25 years and older in Commissioner Precinct 3 P3EEduAtLT
P3MEduAtLTHS Margin of Error for Education less than High School, people age 25 years and older in Commissioner Precinct 3 P3MEduAtLT
P3PEduAtLTHS Percent Education less than High School, people age 25 years and older in Commissioner Precinct 3 P3PEduAtLT
P3EEdAtHSGED Education High School or GED, people age 25 years and older in Commissioner Precinct 3 P3EEdAtHSG
P3MEdAtHSGED Margin of Error for Education High School or GED, people age 25 years and older in Commissioner Precinct 3 P3MEdAtHSG
P3PEdAtHSGED Percent Education High School or GED, people age 25 years and older in Commissioner Precinct 3 P3PEdAtHSG
P3EEduAtSCPlus Education some college or more, people age 25 years and older in Commissioner Precinct 3 P3EEduAtSC
P3MEduAtSCPlus Margin of Error for Education some college or more, people age 25 years and older in Commissioner Precinct 3 P3MEduAtSC
P3PEduAtSCPlus Percent Education some college or more, people age 25 years and older in Commissioner Precinct 3 P3PEduAtSC
P3EHispLat Hispanic or Latino Population in Commissioner Precinct 3 P3EHispLat
P3MHispLat Margin of Error for Hispanic or Latino Population in Commissioner Precinct 3 P3MHispLat
P3PHispLat Percent Hispanic or Latino Population in Commissioner Precinct 3 P3PHispLat
P3ENHLWhA White alone, not of Hispanic or Latino origin in Commission Precinct 3 P3ENHLWhA
P3MNHLWhA Margin of Error for White alone, not of Hispanic or Latino origin in Commission Precinct 3 P3MNHLWhA
P3PNHLWhA Percent White alone, not of Hispanic or Latino origin in Commission Precinct 3 P3PNHLWhA
P3ENHLBlA Black or African American alone, not of Hispanic or Latino origin in Commission Precinct 3 P3ENHLBlA
P3MNHLBlA Margin of Error for Black or African American alone, not of Hispanic or Latino origin in Commission Precinct 3 P3MNHLBlA
P3PNHLBlA Percent Black or African American alone, not of Hispanic or Latino origin in Commission Precinct 3 P3PNHLBlA
P3ENHLAsA Asian alone, not of Hispanic or Latino origin in Commission Precinct 3 P3ENHLAsA
P3MNHLAsA Margin of Error for Asian alone, not of Hispanic or Latino origin in Commission Precinct 3 P3MNHLAsA
P3PNHLAsA Percent Asian alone, not of Hispanic or Latino origin in Commission Precinct 3 P3PNHLAsA
P3ENHLOther American Indian and Alaska Native alone, or Native Hawaiian and Other Pacific Islander alone, not of Hispanic or Latino origin, some other race or 2 or more races in Commission Precinct 3 P3ENHLOthe
P3MNHLOther Margin of Error for American Indian and Alaska Native alone or Native Hawaiian and Other Pacific Islander alone, not of Hispanic or Latino origin, some other race or 2 or more races in Commission Precinct 3 P3MNHLOthe
P3PNHLOther Percent American Indian and Alaska Native alone or Native Hawaiian and Other Pacific Islander alone, not of Hispanic or Latino origin, some other race or 2 or more races in Commission Precinct 3 P3PNHLOthe
P3ETotHH Households, or Occupied Housing Units in Commissioner Precinct 3 P3ETotHH
P3MTotHH Margin of Error for Households, or Occupied Housing Units in Commissioner Precinct 3 P3MTotHH
P3EHHwU18 Households with persons aged under 18 years P3EHHwU18
P3MHHwU18 Margin of Error for Households with persons aged under 18 years P3MHHwU18
P3PHHwU18 Percent Households with persons aged under 18 years in Commissioner Precinct 3 P3PHHwU18
P3E0_30KUSDHHI Households with income between zero and $30,000 in Commissioner Precinct 3 P3E0_30KUS
P3M0_30KUSDHHI Margin of Error for Households with income between zero and $30,000 in Commissioner Precinct 3 P3M0_30KUS
P3P0_30KUSDHHI Percent Households with income between zero and $30,000 in Commissioner Precinct 3 in Commissioner Precinct 3 P3P0_30KUS
P3E30_MAHHI Households with income between $30,000 and median area household income in Commissioner Precinct 3 P3E30_MAHH
P3M30_MAHHI Margin of Error for Households with income between $30,000 and median area household income in Commissioner Precinct 3 P3M30_MAHH
P3P30_MAHHI Percent Households with income between $30,000 and median area household income in Commissioner Precinct 3 P3P30_MAHH
P3PBelMedHHInc Percent of households with income at or below median area household income in Commissioner Precinct 3 P3PBelMedH
P3ESFHU Single Family housing units in Commissioner Precinct 3 P3ESFHU
P3MSFHU Margin of Error for Single Family housing units in Commissioner Precinct 3 P3MSFHU
P3PSFHU Percent Single Family housing units in Commissioner Precinct 3 P3PSFHU
P3EMFHU Multifamily housing units in Commissioner Precinct 3 P3EMFHU
P3MMFHU Margin of Error for Multifamily housing units in Commissioner Precinct 3 P3MMFHU
P3PMFHU Percent Multifamily housing units in Commissioner Precinct 3 P3PMFHU
P3EMHHU Mobile home housing units in Commissioner Precinct 3 P3EMHHU
P3MMHHU Margin of Error for Mobile home housing units in Commissioner Precinct 3 P3MMHHU
P3PMHHU Percent Mobile home housing units in Commissioner Precinct 3 P3PMHHU
P4Tot Total Population in Commission Precinct 4 P4Tot
P4STotPop2010 Total Population in 2010 Commission Precinct 4 P4STotPop2
P4ETotPop2014 Total Population in 2014 Commission Precinct 4 P4ETotPop2
P4ETotPopChg2010_2014 Population Change 2010 to 2014 in Commission Precinct 4 P4ETotPopC
P4ETot60Plus Persons aged 60 plus in Commission Precinct 4 P4ETot60Pl
P4MTot60Plus Margin of Error for Persons aged 60 plus in Commission Precinct 4 P4MTot60Pl
P4PTot60Plus Percent Persons aged 60 plus in Commission Precinct 4 P4PTot60Pl
P4MPTot60Plus Margin of Error for Percent Persons aged 60 plus in Commission Precinct 4 P4MPTot60P
P4PDisability Percent Persons with disability in Commission Precinct 4 P4PDisabil
P4MPDisability Margin of Error for Percent Persons with disability in Commission Precinct 4 P4MPDisabi
P4EDisability Persons with disability in Commission Precinct 4 P4EDisabil
P4MDisability Margin of Error for Persons with disability in Commission Precinct 4 P4MDisabil
P4EMedAge Median age of persons in Commission Precinct 4 P4EMedAge
P4MMedAge Margin of Error for Median age of persons in Commission Precinct 4 P4MMedAge
P4EFemPop Female population in Commission Precinct 4 P4EFemPop
P4MFemPop Margin of Error for Female population in Commission Precinct 4 P4MFemPop
P4PFemPop Percent Female population in Commission Precinct 4 P4PFemPop
P4EMalPop Male population in Commission Precinct 4 P4EMalPop
P4MMalPop Margin of Error for Male population in Commission Precinct 4 P4MMalPop
P4PMalPop Percent Male population in Commission Precinct 4 P4PMalPop
P4EMinority Minority population in Commission Precinct 4 P4EMinorit
P4MMinority Margin of Error for Minority population in Commission Precinct 4 P4MMinorit
P4PMinority Percent Minority population in Commission Precinct 4 P4PMinorit
P4EAvgHHSz Average household size in Commission Precinct 4 P4EAvgHHSz
P4MAvgHHSz Margin of Error for Average household size in Commission Precinct 4 P4MAvgHHSz
P4PBelowPoverty Percent Persons below poverty level in Commission Precinct 4 P4PBelowPo
P4MPBelowPoverty Margin of Error for Percent Persons below poverty level in Commissioners Precinct 4 P4MPBelowP
P4EBelowPoverty Persons below poverty level in Commissioners Precinct 4 P4EBelowPo
P4MBelowPoverty Margin of Error for Persons below poverty level in Commission Precinct 4 P4MBelowPo
P4EOver25Yrs Education universe, people age 25 years and older in Commission Precinct 4 P4EOver25Y
P4MOver25Yrs Margin of Error for Education universe, people age 25 years and older in Commission Precinct 4 P4MOver25Y
P4EEduAtLTHS Education less than High School, people age 25 years and older in Commission Precinct 4 P4EEduAtLT
P4MEduAtLTHS Margin of Error for Education less than High School, people age 25 years and older in Commission Precinct 4 P4MEduAtLT
P4PEduAtLTHS Percent Education less than High School, people age 25 years and older in Commission Precinct 4 P4PEduAtLT
P4EEdAtHSGED Education High School or GED, people age 25 years and older in Commission Precinct 4 P4EEdAtHSG
P4MEdAtHSGED Margin of Error for Education High School or GED, people age 25 years and older in Commission Precinct 4 P4MEdAtHSG
P4PEdAtHSGED Percent Education High School or GED, people age 25 years and older in Commission Precinct 4 P4PEdAtHSG
P4EEduAtSCPlus Education some college or more, people age 25 years and older in Commission Precinct 4 P4EEduAtSC
P4MEduAtSCPlus Margin of Error for Education some college or more, people age 25 years and older in Commission Precinct 4 P4MEduAtSC
P4PEduAtSCPlus Percent Education some college or more, people age 25 years and older in Commission Precinct 4 P4PEduAtSC
P4EHispLat Hispanic or Latino Population in Commission Precinct 4 P4EHispLat
P4MHispLat Margin of Error for Hispanic or Latino Population in Commission Precinct 4 P4MHispLat
P4PHispLat Percent Hispanic or Latino Population in Commission Precinct 4 P4PHispLat
P4ENHLWhA White alone, not of Hispanic or Latino origin in Commission Precinct 4 P4ENHLWhA
P4MNHLWhA Margin of Error for White alone, not of Hispanic or Latino origin in Commission Precinct 4 P4MNHLWhA
P4PNHLWhA Percent White alone, not of Hispanic or Latino origin in Commission Precinct 4 P4PNHLWhA
P4ENHLBlA Black or African American alone, not of Hispanic or Latino origin in Commission Precinct 4 P4ENHLBlA
P4MNHLBlA Margin of Error for Black or African American alone, not of Hispanic or Latino origin in Commission Precinct 4 P4MNHLBlA
P4PNHLBlA Percent Black or African American alone, not of Hispanic or Latino origin in Commission Precinct 4 P4PNHLBlA
P4ENHLAsA Asian alone, not of Hispanic or Latino origin in Commission Precinct 4 P4ENHLAsA
P4MNHLAsA Margin of Error for Asian alone, not of Hispanic or Latino origin in Commission Precinct 4 P4MNHLAsA
P4PNHLAsA Percent Asian alone, not of Hispanic or Latino origin in Commission Precinct 4 P4PNHLAsA
P4ENHLOther American Indian and Alaska Native alone, or Native Hawaiian and Other Pacific Islander alone, not of Hispanic or Latino origin, some other race or 2 or more races in Commission Precinct 4 P4ENHLOthe
P4MNHLOther Margin of Error for American Indian and Alaska Native alone or Native Hawaiian and Other Pacific Islander alone, not of Hispanic or Latino origin, some other race or 2 or more races in Commission Precinct 4 P4MNHLOthe
P4PNHLOther Percent American Indian and Alaska Native alone or Native Hawaiian and Other Pacific Islander alone, of Hispanic or Latino origin, some other race or 2 or more races in Commission Precinct 4 P4PNHLOthe
P4ETotHH Households, or Occupied Housing Units in Commission Precinct 4 P4ETotHH
P4MTotHH Margin of Error for Households, or Occupied Housing Units in Commission Precinct 4 P4MTotHH
P4EHHwU18 Households with persons aged under 18 years in Commission Precinct 4 P4EHHwU18
P4MHHwU18 Margin of Error for Households with persons aged under 18 years in Commission Precinct 4 P4MHHwU18
P4PHHwU18 Percent Households with persons aged under 18 years in Commission Precinct 4 P4PHHwU18
P4E0_30KUSDHHI Households with income between zero and $30,000 in Commission Precinct 4 P4E0_30KUS
P4M0_30KUSDHHI Margin of Error for Households with income between zero and $30,000 in Commission Precinct 4 P4M0_30KUS
P4P0_30KUSDHHI Percent Households with income between zero and $30,000 in Commission Precinct 4 in Commission Precinct 4 P4P0_30KUS
P4E30_MAHHI Households with income between $30,000 and median area household income in Commission Precinct 4 P4E30_MAHH
P4M30_MAHHI Margin of Error for Households with income between $30,000 and median area household income in Commission Precinct 4 P4M30_MAHH
P4P30_MAHHI Percent Households with income between $30,000 and median area household income in Commission Precinct 4 P4P30_MAHH
P4PBelMedHHInc Percent of households with income at or below median area household income in Commission Precinct 4 P4PBelMedH
P4ESFHU Single Family housing units in Commission Precinct 4 P4ESFHU
P4MSFHU Margin of Error for Single Family housing units in Commission Precinct 4 P4MSFHU
P4PSFHU Percent Single Family housing units in Commission Precinct 4 P4PSFHU
P4EMFHU Multifamily housing units in Commission Precinct 4 P4EMFHU
P4MMFHU Margin of Error for Multifamily housing units in Commission Precinct 4 P4MMFHU
P4PMFHU Percent Multifamily housing units in Commission Precinct 4 P4PMFHU
P4EMHHU Mobile home housing units in Commission Precinct 4 P4EMHHU
P4MMHHU Margin of Error for Mobile home housing units in Commission Precinct 4 P4MMHHU
P4PMHHU Percent Mobile home housing units in Commission Precinct 4 P4PMHHU
GeoID Geographic ID concatenating a 7-character numeral with “US” to identify the Census Bureau and then a FIPS code identifier that zooms to the unique geography to which the figure applies. GeoID
GeoID2 Geographic ID 2 – this is the FIPS code portion of the GeoID GeoID2
GeoDesc Geographic verbal description GeoDesc
S2010Pop Total Population in 2010 S2010Pop
E2014Pop Total Population in 2014 E2014Pop
E10_14PopChg Population Change 2010 to 2014 E10_14PopC
ETot60Plus Persons aged 60 plus ETot60Plus
MTot60Plus Margin of Error for Persons aged 60 plus MTot60Plus
PTot60Plus Percent Persons aged 60 plus PTot60Plus
MPTot60Plus Margin of Error for Percent Persons aged 60 plus MPTot60Plu
PDisability Percent Persons with disability PDisabilit
MPDisability Margin of Error for Percent Persons with disability MPDisabili
EDisability Persons with disability EDisabilit
MDisability Margin of Error for Persons with disability MDisabilit
EMedAge Median age of persons EMedAge
MMedAge Margin of Error for Median age of persons MMedAge
EFemPop Female population EFemPop
MFemPop Margin of Error for Female population MFemPop
PFemPop Percent Female population PFemPop
EMalPop Male population EMalPop
MMalPop Margin of Error for Male population MMalPop
PMalPop Percent Male population PMalPop
EMinority Minority population EMinority
MMinority Margin of Error for Minority population MMinority
PMinority Percent Minority population PMinority
EAvgHHSz Average household size EAvgHHSz
MAvgHHSz Margin of Error for Average household size MAvgHHSz
PBelowPoverty Percent Persons below poverty level PBelowPove
MPBelowPoverty Margin of Error for Percent Persons below poverty level MPBelowPov
EBelowPoverty Persons below poverty level EBelowPove
MBelowPoverty Margin of Error for Persons below poverty level MBelowPove
EOver25Yrs Education universe, people age 25 years and older EOver25Yrs
MOver25Yrs Margin of Error for Education universe, people age 25 years and older MOver25Yrs
EEduAtLTHS Education less than High School, people age 25 years and older EEduAtLTHS
MEduAtLTHS Margin of Error for Education less than High School, people age 25 years and older MEduAtLTHS
PEduAtLTHS Percent Education less than High School, people age 25 years and older PEduAtLTHS
EEdAtHSGED Education High School or GED, people age 25 years and older EEdAtHSGED
MEdAtHSGED Margin of Error for Education High School or GED, people age 25 years and older MEdAtHSGED
PEdAtHSGED Percent Education High School or GED, people age 25 years and older PEdAtHSGED
EEduAtSCPlus Education some college or more, people age 25 years and older EEduAtSCPl
MEduAtSCPlus Margin of Error for Education some college or more, people age 25 years and older MEduAtSCPl
PEduAtSCPlus Percent Education some college or more, people age 25 years and older PEduAtSCPl
EHispLat Hispanic or Latino Population EHispLat
MHispLat Margin of Error for Hispanic or Latino Population MHispLat
PHispLat Percent Hispanic or Latino Population PHispLat
ENHLWhA White alone, not of Hispanic or Latino origin ENHLWhA
MNHLWhA Margin of Error for White alone, not of Hispanic or Latino origin MNHLWhA
PNHLWhA Percent White alone, not of Hispanic or Latino origin PNHLWhA
ENHLBlA Black or African American alone, not of Hispanic or Latino origin ENHLBlA
MNHLBlA Margin of Error for Black or African American alone, not of Hispanic or Latino origin MNHLBlA
PNHLBlA Percent Black or African American alone, not of Hispanic or Latino origin PNHLBlA
ENHLAsA Asian alone, not of Hispanic or Latino origin ENHLAsA
MNHLAsA Margin of Error for Asian alone, not of Hispanic or Latino origin MNHLAsA
PNHLAsA Percent Asian alone, not of Hispanic or Latino origin PNHLAsA
ENHLOther American Indian and Alaska Native alone, or Native Hawaiian and Other Pacific Islander alone, not of Hispanic or Latino origin, some other race or 2 or more races ENHLOther
MNHLOther Margin of Error for American Indian and Alaska Native alone or Native Hawaiian and Other Pacific Islander alone, not of Hispanic or Latino origin, some other race or 2 or more races MNHLOther
PNHLOther Percent American Indian and Alaska Native alone or Native Hawaiian and Other Pacific Islander alone, not of Hispanic or Latino origin, some other race or 2 or more races PNHLOther
ETotHH Households, or Occupied Housing Units ETotHH
MTotHH Margin of Error for Households, or Occupied Housing Units MTotHH
EHHwU18 Households with persons aged under 18 years EHHwU18
MHHwU18 Margin of Error for Households with persons aged under 18 years MHHwU18
PHHwU18 Percent Households with persons aged under 18 years PHHwU18
E0_30KUSDHHI Households with income between zero and $30,000 E0_30KUSDH
M0_30KUSDHHI Margin of Error for Households with income between zero and $30,000 M0_30KUSDH
P0_30KUSDHHI Percent Households with income between zero and $30,000 P0_30KUSDH
E30_MAHHI Households with income between $30,000 and median area household income E30_MAHHI
M30_MAHHI Margin of Error for Households with income between $30,000 and median area household income M30_MAHHI
P30_MAHHI Percent Households with income between $30,000 and median area household income P30_MAHHI
PBelMedHHInc Percent of households with income at or below median area household income PBelMedHHI
ESFHU Single Family housing units ESFHU
MSFHU Margin of Error for Single Family housing units MSFHU
PSFHU Percent Single Family housing units PSFHU
EMFHU Multifamily housing units EMFHU
MMFHU Margin of Error for Multifamily housing units MMFHU
PMFHU Percent Multifamily housing units PMFHU
EMHHU Mobile home housing units EMHHU
MMHHU Margin of Error for Mobile home housing units MMHHU
PMHHU Percent Mobile home housing units PMHHU
Shape_Length Machine-calculated shape perimeter Shape_Leng
Shape_Area Machine-calculated shape area Shape_Area

H-GAC field names:

follow this format: P_A_B_C, example P_1_1_1 where

P abbreviates Population Count from the 2010 Decennial U. S. Census,

A is a number from 1 to 3 which stands for 1 = All Ages, 2 = Age Under 18, and 3 = Age 18 and Older,

B is a number from 1 to 7 which stands for 1 = Population, 2 = White Alone, 3 = Black Alone, 4 = American Indian or Native Alone, 5 = Asian Alone, 6 = Some Other Race Alone, 7 = Two or More Races, and

C is a number from 1 to 3 which stands for 1 = Total, 2 = Not Hispanic or Latino Origin, and 3 = Hispanic or Latino Origin,

Then Housing data are described as H_1 = All Housing Units, H_2 = Occupied Housing Units (same as number of households), H_3 = Vacant Housing Units, HP = Population in Occupied Housing Units, and AHS = Average Household Size,

And finally Group Quarters fields GQ_1 = GQ Population (all those persons not living in occupied housing units), GQ_2 = Institutionalized population (101-106, 201-203, 301, 401-405), GQ_3 = Correctional facilities for adults (101-106), GQ_4 = Juvenile facilities (201-203), GQ_5 = Nursing facilities/Skilled-nursing facilities (301), GQ_6 = Other institutional facilities (401-405), GQ_7 = Noninstitutionalized population (501, 601-602, 701-702, 704, 706, 801-802, 900-901, 903-904):, GQ_8 = College/University student housing (501), GQ_9 = Military quarters (601-602), GQ_10 = Other noninstitutional facilities (701-702, 704, 706, 801-802, 900-901, 903-904).

Here is an example based on the above:

H-GAC Field Name H-GAC Description Paul’s Mnemoniker Paul’s Shapefile Abbreviation Census PL Table and Field Name (or calculation from these) Calculation or Source Note GeodataMemnoniker ShapefileAbbreviation
P_1_1_1 Population–All Ages–All Races–Total Population Population P1D001 TotPop TotPop
P_1_1_2 Population–All Ages–All Races–Not Hispanic NotHL NotHL P2D003 TotNHL TotNHL
P_1_1_3 Population–All Ages–All Races–Hispanic HispLat HispLat P2D002 TotHL TotHL
P_1_2_1 Population–All Ages–White–Total WhiteAlone WhiteAlone P1D003 WhiteAlone WhiteAlone
P_1_2_2 Population–All Ages–White–Not Hispanic NHLWhiteAlone NHLWhiteAl P2D005 NHLWhiteAlone NHLWhiteAl
P_1_2_3 Population–All Ages–White–Hispanic HLWhiteAlone HLWhiteAlo P1D003 – P2D005 HLWhiteAlone HLWhiteAlo
P_1_3_1 Population–All Ages–Black–Total BlackAlone BlackAlone P1D004 BlackAlone BlackAlone
P_1_3_2 Population–All Ages–Black–Not Hispanic NHLBlackAlone NHLBlackAl P2D006 NHLBlackAlone NHLBlackAl
P_1_3_3 Population–All Ages–Black–Hispanic HLBlackAlone HLBlackAlo P1D004 – P2D006 HLBlackAlone HLBlackAlo
P_1_4_1 Population–All Ages–American Indian–Total AIANNHOPIAlone AIANNHOPIA P1D005 + P1D007 AIANOrNHOPIAlone AIANOrNHOP
P_1_4_2 Population–All Ages–American Indian–Not Hispanic NHLAIANNHOPIAlone NHLAIANNHO P2D007 + P2D009 NHLAIANOrNHOPIAlone NHLAIANOrN
P_1_4_3 Population–All Ages–American Indian–Hispanic HLAIANNHOPIAlone HLAIANNHOP P1D005 + P1D007 – P2D007 – P2D009 HLAIANOrNHOPIAlone HLAIANOrNH
P_1_5_1 Population–All Ages–Asian–Total AsianAlone AsianAlone P1D006 AsianAlone AsianAlone
P_1_5_2 Population–All Ages–Asian–Not Hispanic NHLAsianAlone NHLAsianAl P2D008 NHLAsianAlone NHLAsianAl
P_1_5_3 Population–All Ages–Asian–Hispanic HLAsianAlone HLAsianAlo P1D006 – P2D008 HLAsianAlone HLAsianAlo
P_1_6_1 Population–All Ages–Some Other Race–Total OtherAlone OtherAlone P1D008 OtherAlone OtherAlone
P_1_6_2 Population–All Ages–Some Other Race–Not Hispanic NHLOtherAlone NHLOtherAl P2D010 NHLOtherAlone NHLOtherAl
P_1_6_3 Population–All Ages–Some Other Race–Hispanic HLOtherAlone HLOtherAlo P1D008 – P2D010 HLOtherAlone HLOtherAlo
P_1_7_1 Population–All Ages–Two or More Races–Total Multiracial Multiracia P1D009 TotMultiRc TotMultiRc
P_1_7_2 Population–All Ages–Two or More Races–Not Hispanic NHLMultiracial NHLMultira P2D011 NHLMultiRc NHLMultiRc
P_1_7_3 Population–All Ages–Two or More Races–Hispanic HLMultiracial HLMultirac P1D009 – P2D011 HLMultiRc HLMultiRc
P_2_1_1 Population–Under 18–All Races–Total U18Population U18Populat P1D001 – P3D001 U18Pop U18Pop
P_2_1_2 Population–Under 18–All Races–Not Hispanic U18NotHL U18NotHL P2D003 – P4D003 U18NHL U18NHL
P_2_1_3 Population–Under 18–All Races–Hispanic U18HispLat U18HispLat P2D002 – P4D002 U18HL U18HL
P_2_2_1 Population–Under 18–White–Total U18WhiteAlone U18WhiteAl P1D003 – P3D003 U18WhiteAlone U18WhiteAl
P_2_2_2 Population–Under 18–White–Not Hispanic U18NHLWhiteAlone U18NHLWhit P2D005 – P4D005 U18NHLWhiteAlone U18NHLWhit
P_2_2_3 Population–Under 18–White–Hispanic U18HLWhiteAlone U18HLWhite P1D003 – P2D005 – P3D003 + P4D005 U18HLWhiteAlone U18HLWhite
P_2_3_1 Population–Under 18–Black–Total U18BlackAlone U18BlackAl P1D004 – P3D004 U18BlackAlone U18BlackAl
P_2_3_2 Population–Under 18–Black–Not Hispanic U18NHLBlackAlone U18NHLBlac P2D006 – P4D006 U18NHLBlackAlone U18NHLBlac
P_2_3_3 Population–Under 18–Black–Hispanic U18HLBlackAlone U18HLBlack P1D004 – P2D006 – P3D004 + P4D006 U18HLBlackAlone U18HLBlack
P_2_4_1 Population–Under 18–American Indian–Total U18AIANNHOPIAlone U18AIANNHO P1D005 + P1D007 – P3D005 – P3D007 U18AIANOrNHOPIAlone U18AIANOrN
P_2_4_2 Population–Under 18–American Indian–Not Hispanic U18NHLAIANNHOPIAlone U18NHLAIAN P2D007 + P2D009 – P4D007 – P4D009 U18NHLAIANOrNHOPIAlone U18NHLAIAN
P_2_4_3 Population–Under 18–American Indian–Hispanic U18HLAIANNHOPIAlone U18HLAIANN P1D005 + P1D007 – P3D005 – P3D007 – P2D007 – P2D009 + P4D007 + P4D009 U18HLAIANOrNHOPIAlone U18HLAIANO
P_2_5_1 Population–Under 18–Asian–Total U18AsianAlone U18AsianAl P1D006 – P3D006 U18AsianAlone U18AsianAl
P_2_5_2 Population–Under 18–Asian–Not Hispanic U18NHLAsianAlone U18NHLAsia P2D008 – P4D008 U18NHLAsianAlone U18NHLAsia
P_2_5_3 Population–Under 18–Asian–Hispanic U18HLAsianAlone U18HLAsian P1D006 – P2D008 – P3D006 + P4D008 U18HLAsianAlone U18HLAsian
P_2_6_1 Population–Under 18–Some Other Race–Total U18OtherAlone U18OtherAl P1D008 – P3D008 U18OtherAlone U18OtherAl
P_2_6_2 Population–Under 18–Some Other Race–Not Hispanic U18NHLOtherAlone U18NHLOthe P2D010 – P4D010 U18NHLOtherAlone U18NHLOthe
P_2_6_3 Population–Under 18–Some Other Race–Hispanic U18HLOtherAlone U18HLOther P1D008 – P3D008 – P2D010 + P4D010 U18HLOtherAlone U18HLOther
P_2_7_1 Population–Under 18–Two or More Races–Total U18Multiracial U18Multira P1D009 – P3D009 U18MultiRc U18MultiRc
P_2_7_2 Population–Under 18–Two or More Races–Not Hispanic U18NHLMultiracial U18NHLMult P2D011 – P4D011 U18NHLMultiRc U18NHLMult
P_2_7_3 Population–Under 18–Two or More Races–Hispanic U18HLMultiracial U18HLMulti P1D009 – P3D009 – P2D011 + P4D011 U18HLMultiRc U18HLMulti
P_3_1_1 Population–18 and Over–All Races–Total VotPopulation VotPopulat P3D001 VotPop VotPop
P_3_1_2 Population–18 and Over–All Races–Not Hispanic VotNotHL VotNotHL P4D003 VotNHL VotNHL
P_3_1_3 Population–18 and Over–All Races–Hispanic VotHispLat VotHispLat P4D002 VotHL VotHL
P_3_2_1 Population–18 and Over–White–Total VotWhiteAlone VotWhiteAl P3D003 VotWhiteAlone VotWhiteAl
P_3_2_2 Population–18 and Over–White–Not Hispanic VotNHLWhiteAlone VotNHLWhit P4D005 VotNHLWhiteAlone VotNHLWhit
P_3_2_3 Population–18 and Over–White–Hispanic VotHLWhiteAlone VotHLWhite P3D003 – P4D005 VotHLWhiteAlone VotHLWhite
P_3_3_1 Population–18 and Over–Black–Total VotBlackAlone VotBlackAl P3D004 VotBlackAlone VotBlackAl
P_3_3_2 Population–18 and Over–Black–Not Hispanic VotNHLBlackAlone VotNHLBlac P4D006 VotNHLBlackAlone VotNHLBlac
P_3_3_3 Population–18 and Over–Black–Hispanic VotHLBlackAlone VotHLBlack P3D004 – P4D006 VotHLBlackAlone VotHLBlack
P_3_4_1 Population–18 and Over–American Indian–Total VotAIANNHOPIAlone VotAIANNHO P3D005 + P3D007 VotAIANOrNHOPIAlone VotAIANOrN
P_3_4_2 Population–18 and Over–American Indian–Not Hispanic VotNHLAIANNHOPIAlone VotNHLAIAN P4D007 + P4D009 VotNHLAIANOrNHOPIAlone VotNHLAIAN
P_3_4_3 Population–18 and Over–American Indian–Hispanic VotHLAIANNHOPIAlone VotHLAIANN P3D005 + P3D007 – P4D007 – P4D009 VotHLAIANOrNHOPIAlone VotHLAIANO
P_3_5_1 Population–18 and Over–Asian–Total VotAsianAlone VotAsianAl P3D006 VotAsianAlone VotAsianAl
P_3_5_2 Population–18 and Over–Asian–Not Hispanic VotNHLAsianAlone VotNHLAsia P4D008 VotNHLAsianAlone VotNHLAsia
P_3_5_3 Population–18 and Over–Asian–Hispanic VotHLAsianAlone VotHLAsian P3D006 – P4D008 VotHLAsianAlone VotHLAsian
P_3_6_1 Population–18 and Over–Some Other Race–Total VotOtherAlone VotOtherAl P3D008 VotOtherAlone VotOtherAl
P_3_6_2 Population–18 and Over–Some Other Race–Not Hispanic VotNHLOtherAlone VotNHLOthe P4D010 VotNHLOtherAlone VotNHLOthe
P_3_6_3 Population–18 and Over–Some Other Race–Hispanic VotHLOtherAlone VotHLOther P3D008 – P4D010 VotHLOtherAlone VotHLOther
P_3_7_1 Population–18 and Over–Two or More Races–Total VotMultiracial VotMultira P3D009 VotMultiRc VotMultiRc
P_3_7_2 Population–18 and Over–Two or More Races–Not Hispanic VotNHLMultiracial VotNHLMult P4D011 VotNHLMultiRc VotNHLMult
P_3_7_3 Population–18 and Over–Two or More Races–Hispanic VotHLMultiracial VotHLMulti P3D009 – P4D011 VotHLMultiRc VotHLMulti
H_1 Housing Units–All HousingUnits HousingUni H1D001 TotHU TotHU
H_2 Housing Units–Occupied OccupiedHU OccupiedHU H1D002 OccHU OccHU
H_3 Housing Units–Vacant VacantHU VacantHU H1D003 VacHU VacHU
HP Housing Units–Population in Occupied Units HouseholdPopulation HouseholdP Added to H-GAC tables after April 2011. HHPop HHPop
AHS Housing Units – Average Size of Occupied Unit AvgHHSize AvgHHSize Added to H-GAC tables after April 2011. AvgHHSiz AvgHHSiz
GQ_1 Total Group Quarters Population GQPopulation GQPopulati Added to H-GAC tables after April 2011. GQPop GQPop
GQ_2 Institutionalized population (101-106, 201-203, 301, 401-405): GQInstitutionalized GQInstitut Added to H-GAC tables after April 2011. GQInst GQInst
GQ_3 Correctional facilities for adults (101-106) GQCorrectionalFacilities GQCorrecti Added to H-GAC tables after April 2011. GQCorr GQCorr
GQ_4 Juvenile facilities (201-203) GQJuvenileFacilities GQJuvenile Added to H-GAC tables after April 2011. GQJuve GQJuve
GQ_5 Nursing facilities/Skilled-nursing facilities (301) GQNursingSNIF GQNursingS Added to H-GAC tables after April 2011. GQNurs GQNurs
GQ_6 Other institutional facilities (401-405) GQOtherInstitutional GQOtherIns Added to H-GAC tables after April 2011. GQOthInst GQOthInst
GQ_7 Noninstitutionalized population (501, 601-602, 701-702, 704, 706, 801-802, 900-901, 903-904): GQNoninstitutionalized GQNoninsti Added to H-GAC tables after April 2011. GQNnInst GQNnInst
GQ_8 College/University student housing (501) GQCollegeUniversity GQCollegeU Added to H-GAC tables after April 2011. GQCollUniv GQCollUniv
GQ_9 Military quarters (601-602) GQMilitaryQuarters GQMilitary Added to H-GAC tables after April 2011. GQMilQuarters GQMilQuart
GQ_10 Other noninstitutional facilities (701-702, 704, 706, 801-802, 900-901, 903-904) GQOtherNoninstitutional GQOtherNon Added to H-GAC tables after April 2011. GQOthNnInst GQOthNnIns
NEW SUMMARY FIELDS:
MinorityPop Aggregate non- Not Hispanic White Alone MinorityPopulation MinorityPo P1D001 – P2D005 Population – NHLWhiteAlone
PercentMin Percentage non- Not Hispanic White Alone PercentMinority PercentMin (P1D001 – P2D005)/P1D001 ( Population – NHLWhiteAlone ) / Population
NatMultiOt Aggregate non- White, Black or Asian Alone NativeMultiracialOrOther NativeMult P1D005 + P1D007 + P1D008 + P1D009 AIANNHOPIAlone + OtherAlone + Multiracial
NHNatMultiOt Aggregate non- White, Black or Asian Alone, not Hispanic NHLNativeMultiracialOrOther NHLNativeM P2D007 + P2D009 + P2D010 + P2D011 NHLAIANNHOPIAlone + NHLOtherAlone + NHLMultiracial
HispNatMultiOt Aggregate non- White, Black or Asian Alone, not Hispanic HLNativeMultiracialOrOther HLNativeMu P1D005 + P1D007 – P2D007 – P2D009 + P1D008 – P2D010 + P1D009 – P2D011 HLAIANNHOPIAlone + HLOtherAlone + HLMultiracial
AnnGro11 Annual Growth Rate Expressed in percent for mid 2010-2011 AnnGroRt11 AnnGroRt11 Controlled to State Demographer’s Office County Population Estimate
AnnGro12 Annual Growth Rate Expressed in percent for mid 2011-2012 AnnGroRt12 AnnGroRt12 Controlled to State Demographer’s Office County Population Estimate
AnnGro13 Annual Growth Rate Expressed in percent for mid 2012-2013 AnnGroRt13 AnnGroRt13 Controlled to State Demographer’s Office County Population Estimate
AnnGro14 Annual Growth Rate Expressed in percent for mid 2013-2014 AnnGroRt14 AnnGroRt14 Controlled to State Demographer’s Office County Population Estimate
AnnGro15 Annual Growth Rate Expressed in percent for mid 2014-2015 AnnGroRt15 AnnGroRt15 Controlled to State Demographer’s Office County Population Estimate
AnnGro16 Annual Growth Rate Expressed in percent for mid 2015-2016 AnnGroRt16 AnnGroRt16 Controlled to State Demographer’s Office County Population Estimate
AnnGro17 Annual Growth Rate Expressed in percent for mid 2016-2017 AnnGroRt17 AnnGroRt17 Controlled to State Demographer’s Office County Population Estimate
AnnGro18 Annual Growth Rate Expressed in percent for mid 2017-2018 AnnGroRt18 AnnGroRt18 Controlled to State Demographer’s Office County Population Estimate
AnnGro19 Annual Growth Rate Expressed in percent for mid 2018-2019 AnnGroRt19 AnnGroRt19 Controlled to State Demographer’s Office County Population Estimate
AnnGro20 Annual Growth Rate Expressed in percent for mid 2019-2020 AnnGroRt20 AnnGroRt20 Controlled to State Demographer’s Office County Population Estimate
Pop2011 Population in Mid-2011 Pop2011 Pop2011 Controlled to State Demographer’s Office County Population Estimate
Pop2012 Population in Mid-2012 Pop2012 Pop2012 Controlled to State Demographer’s Office County Population Estimate
Pop2013 Population in Mid-2013 Pop2013 Pop2013 Controlled to State Demographer’s Office County Population Estimate
Pop2014 Population in Mid-2014 Pop2014 Pop2014 Controlled to State Demographer’s Office County Population Estimate
Pop2015 Population in Mid-2015 Pop2015 Pop2015 Controlled to State Demographer’s Office County Population Estimate
Pop2016 Population in Mid-2016 Pop2016 Pop2016 Controlled to State Demographer’s Office County Population Estimate
Pop2017 Population in Mid-2017 Pop2017 Pop2017 Controlled to State Demographer’s Office County Population Estimate
Pop2018 Population in Mid-2018 Pop2018 Pop2018 Controlled to State Demographer’s Office County Population Estimate
Pop2019 Population in Mid-2019 Pop2019 Pop2019 Controlled to State Demographer’s Office County Population Estimate
Pop2020 Population in Mid-2020 Pop2020 Pop2020 Controlled to State Demographer’s Office County Population Estimate
Pct2002 County Commissioner Precinct that this geographic unit fell under 2002-2011 Pct2002 Pct2002 Harris County Tax Office
PropoPct County Commissioner Precinct proposed to Federal Court as Revised A-1 Map PropoPct PropoPct
Pct2012 Interim Court-Ordered County Commissioner Precinct that this geographic unit fell under for the elections of 2012. InterimPct InterimPct Harris County Public Infrastructure Department CAMS
Pct2013 County Commissioner Precinct that this geographic unit fell under 2013-2021 Pct2013 Pct2013 reserved for future use
Pct2022 County Commissioner Precinct that this geographic unit fell under 2022-2032 Pct2022 Pct2022 reserved for future use
HCSA Boolean +1 for geographic units that fall under the Harris County Service Area HCSA HCSA Harris County Service Area consists of unincorporated land and cities with which Harris County maintains a cooperating cities agreement.
CDTA Community Development Target Area CDTgtArea CDTgtArea Community Development Target Area is designated by HUD as eligible for the low to moderate income area benefit National Priority – per jurisdiction.
Place Census defined place that this geographic unit fell under in 2010 Place Place US Census Bureau
Jurisdiction Incorporated city or unincorporated county commissioner precinct that this geographic unit fell under in 2010 City City HCAD
SUD1 Special government unit district that this geographic unit fell under in 2010 SUD1MUD SUD1MUD Example: MUD
SUD2 Special government unit district that this geographic unit fell under in 2010 SUD2FWSD SUD2FWSD Example: FWSD
SUD3 Special government unit district that this geographic unit fell under in 2010 SUD3TIRZ SUD3TIRZ Example: TIRZ
SUD4 Special government unit district that this geographic unit fell under in 2010 SUD4ManagementDistrict SUD4Manage Example: MgtDistrict
SUD5 Special government unit district that this geographic unit fell under in 2010 SUD5Transit SUD5Transi Example: MetroBound
SUD6 Special government unit district that this geographic unit fell under in 2010 SUD6Education SUD6Educat Example: Education District
SUD7 Special government unit district that this geographic unit fell under in 2010 SUD7College SUD7Colleg Example: Community College District
SUD8 Special government unit district that this geographic unit fell under in 2010 SUD8Hospital SUD8Hospit Example: Hospital District
SUD9 Special government unit district that this geographic unit fell under in 2010 SUD9Emergency Services SUD9Emerge Example: Emergency Management District
SUDHE Special environment unit district that this geographic unit fell under in 2010 SUDHurricane Evacuation Priority SUDHurrica Example: Hurricane Evacuation District
SUDHI Special environment unit district that this geographic unit fell under in 2010 SUDHistoric SUDHistori Example: Historic District
SUDFP Special environment unit district that this geographic unit fell under in 2010 SUDFloodplain SUDFloodpl Example: SFHA, CHHA or FLWY
SUDAP Special environment unit district that this geographic unit fell under in 2010 SUDRunway Protection Zone SUDRunway Example: Airport Clear Zone (Military Accident Potential Zone APZ, Civil Runway Protection Zone RPZ)
SUDET Special environment unit district that this geographic unit fell under in 2010 SUDExlosiveThermal SUDExlosiv Example: Explosive or Thermal Safety Zone
SUDWL Special environment unit district that this geographic unit fell under in 2010 SUDWetlands of ecological importance SUDWetland Example: Ecologically Important Wetlands Zone
SUDWS Special environment unit district that this geographic unit fell under in 2010 SUDWatershed SUDWatersh Example: Watershed
SUDHP Special environment unit district that this geographic unit fell under in 2010 SUDNoiseProtection SUDNoisePr Example: Hearing Protection Zone
SUDCM Special environment unit district that this geographic unit fell under in 2010 SUDCoastalMgt SUDCoastal Example: Coastal Management Program Zone
SUDST Special environment unit district that this geographic unit fell under in 2010 SUDSoilType SUDSoilTyp Example: Predominant type from 1976 Harris County Soil Survey results
SUDLU Special environment unit district that this geographic unit fell under in 2010 SUDLandUse SUDLandUse Example: Planning Land Use Classification
SUDLC Special environment unit district that this geographic unit fell under in 2010 SUDLandCover SUDLandCov Example: Predominant Remotely Sensed Parcel-Based Ground Cover Classification
P_IC Percent of Remotely Sensed Parcel-Based Impervious Ground Cover Sum P_ImperviousCover P_Impervio H-GAC? HCAD?
Pict URL of photograph(s) of this geographic unit Picture Picture
KeyMG KeyMap quadrangle into which this geographic unit fit in 2010 KeyMapGrid KeyMapGrid Example: 429H
KeyMPg KeyMap quadrangle into which this geographic unit fit in 2010 KeyMapPage KeyMapPage Example: 429
FEMAFIRM FEMA Flood Insurance Rate Panel into which this geographic unit fit in 2010 FEMAFIRM FEMAFIRM Example: 48201C0495L FEMA

FEMA IA data:

Count_ – the total number of registrants who filtered to this category.

Remaining AI data fields include the Sum_, Avg_ (Average or mean), and SD_ (Standard deviation) for all numeric fields in the IA data, whether it is useful analytically or not:

DR – DR Number (4332)

HH_Mem – Household members in the count of households

Gross_ – Gross Household Income (self-reported)

DD_Zip – the designated disaster location Zip Code of this block group (sum is useless and mean will only indicate whether the BG is split or not between 2 or more zips)

Lat – Latitude in decimal degrees

Lon – Longitude in decimal degrees

FVL_SB – FEMA Verified Loss (FVL_Am) plus any SBA Loan Amount (SBA_Lo), generally what FEMA decided was legitimate damage beyond that covered by flood insurance

AWARDE – FEMA assistance awarded and/or SBA loan amount loans (sum of HA_Awa, ONA_Aw, SBA_Lo), generally what FEMA had already paid out as of the report pull date of December 5, 2017

FVL_Am – FEMA Verified Loss Amount credited toward a damaged real or personal property

HA_Awa – Housing Assistance Award, mostly hotel stays

ONA_Aw – Other Needs Award, usually the $500 handed out just aftert the disaster

Unmet_ – Unmet Needs calculated by subtracting awarded amounts from FVL. Although FEMA hopes this amount to be supplied by bevelolent non-profits (Red Cross, etc.), most often it’s the disaster victims

FIP – Flood Insurance Pre, usually $600 forwarded from the insured’s expected insurance settlement, rarely applied and not added into the award summary fields (AWARDE)

SBA_Lo – SBA Loan Amount to assist homeowners rebuilding

COH – each IA record located within City of Houston = 1

Apt – each IA record located at an apartment rental = 1 (this is a bit out of order, really the next 2 fields should come first)

Own – each IA record that self-reported Owner in the Tenure field = 1

Rent – each IA record that self-reported Renter in the Tenure field = 1

Condo – each IA record located in an owned or rented condominium multi-family unit = 1

MH – each IA record located in an owned or rented Mobile Home unit = 1 (determined when possible by visual inspection of aerial imagery at the parcel level, many especially in COH are not captured)

Oth – each IA record located in another type of unit = 1 (storage unit, business location, determined when possible by visual inspection of aerial imagery at the parcel, many especially in COH are not captured)

NonZer – each IA record either awarded any amount by FEMA or assigned any FVL amount = 1

Pct – each record’s Harris County Commissioner’s Court Precinct number (not useful for analysis because many precinct lines bisect block groups)

BAOI – unsupported field, please ignore

Finally, an additional IA Harris County block group dataset containing Total, Owner and Renter tables of the following three data fields is attached courtesy of Harris County Emergency Management Office:

AFN, or Access and Functional Needs (persons who overcome disabilities),

60P, or Age 60 Plus, and

IHPEligible, or FEMA Individuals and Households Program-Eligible

(resource: https://www.fema.gov/media-library-data/1483567080828-1201b6eebf9fbbd7c8a070fddb308971/FEMAIHPUG_CoverEdit_December2016.pdf).

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Tricks and Traditional Whiz-dim

One night I dreamed a dream.

One night I dreamed a dream after my spouse’s passing.

As I walked along a deserted beach alone with my thoughts,

The big sky flashed memories from our life. Beautiful ones, biter ones, one after the next.

Upon each memory, I imagined our two sets of footprints accompanied me along the way,

One belonging to me and one to my dear departed love.

 

After a lengthy reverie of our life’s times had passed before my eyes,

I looked back toward that long trail of footprints that marked our path together.

And obliterating our partnered steps along the journey of life,

And especially at those most difficult and saddest times,

A billion footprints could be found all blurred together.

 

This really troubled me now, so I questioned myself about it.

“How can it be, that as I’ve reviewed the times we’d lived,

That special path I thought we’d left in life’s sands,

I noticed that during the saddest and most burdensome times of our life,

Billions of footprints stepped all over our own.

I’d considered my life’s path far more special, precious and unique.”

 

Then answered the wind, “My precious child, you are so loved!

The trials and testing life offered brought your focus necessarily to yourself.

But those billions of footprints that you see,

Belong to a billion unknown far and wide who creatively walked with you and carried you through.

Be sure to thank those you meet along your path today and every day.”

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Memo to File

The accumulation of human impacts on the environment continues apace. Most of these impacts are minor and unintentional but lead to global changes being observed in the ocean and atmosphere all around the planet. The most important of such minor impacts related to the proposed demolition project is refrigerant management. On his site visit near the end of 2011, Harris County Senior Planner Paul Suckow found rapid and pervasive unsanctioned salvaging of all kinds of materials from Brandywood, a doomed 698-unit apartment complex which closed only 12 days before having experienced severe repetitive flooding already at the time of Hurricane Ike, including looting of iron storm drain inlet covers, plumbing fixtures and appliances, and exterior fixtures. With the large number of central air conditioning condenser units in a facility of 698 dwelling units, a serious threat of refrigerant release existed on the site. Ad-hoc salvaging crews appeared interested in copper and other metals for their recycling value and may not have been aware of the risks involved with releasing Freon to the atmosphere.

Health risks of direct contact: Exposure to refrigerant can cause refrigerant poisoning. Something as mild as breathing near an open container or a small spill on your hand is generally harmless, however minor symptoms such as dizziness, headache, coughing and eye, nose and throat irritation may occur. When refrigerant is deeply inhaled, it can cut of oxygen to your lungs and cells. Direct exposure within a confined space, or abuse of the substance as a drug, can cause vomiting, chemical burn, heart palpitation, difficulty breathing and even loss of consciousness.

Health risks of release: Refrigerant chemicals such as Freon accumulate in the air and most air conditioners use a chemical called Freon as a refrigerant. Freon is a stable, nonflammable, moderately toxic gas that is tasteless and mostly odorless. Freon contains chlorofluorocarbons (CFCs), which are known to deplete the ozone high in the atmosphere. The ozone that is formed or destroyed high in the atmosphere is essential to shield all life on the planet from the harmful ultraviolet radiation which would cause cell tissue destruction if it reached the biosphere in higher doses. Particular danger is interference with genetic transmission in living things.

Even a small amount of refrigerant release is dangerous to the environment because chemicals like Freon are man-made, not natural, and its components perform very efficiently as persistent catalysts in reactions which destroy ozone gas many times its weight and volume. CFCs are also more than ten thousand times more heat-trapping than carbon dioxide, the main gas creating vast and multiple human and environmental threats due to global heating and climate changes. It is essential that unprofessional salvagers take care not to release any refrigerant gas from refrigerators or air conditioners in attempts to gain salvageable material. Rapid deployment of trained and licensed demolition professionals may prevent inadvertent and cumulatively dangerous releases of refrigerant gases.

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Scientipic Whiz-Dim

Hydrofluorocarbon (HFC)

FROM AN ARTICLE ORIGINALLY WRITTEN FOR BRITANNICA BY:

 Kara Rogers

Alternative Title: HFC

Hydrofluorocarbon (HFC), any of several organic compounds composed of hydrogenfluorine, and carbon. HFCs are produced synthetically and are used primarily as refrigerants. They became widely used for this purpose beginning in the late 1980s, with the introduction of the Montreal Protocol, which phased out the use of chemicals such as halons and chlorofluorocarbons (CFCs) that contribute to the depletion of Earth’s ozone layer. However, while HFCs have an ozone depletion potential of zero, they are potent greenhouse gases, and thus their manufacture and use became increasingly regulated in the 21st century.

In general, HFCs are relatively nonflammable, chemically stable, and nonreactive. Many are colorless, odorless gases, but some—such as HFC-365mfc (1,1,1,3,3-pentafluorobutane)—are liquids at room temperature. As refrigerants, HFCs are used in a wide variety of cooling systems, from refrigerators and freezers to automotive air-conditioning units. HFCs are also used as blowing agents in the production of polymer foams; as firefighting agents (having replaced halons); as solvents in cleaning products for plastics and metals and in plasma etching for semiconductor technology; and as propellants in metered-dose inhalers prescribed for the treatment of asthma.

There are different routes to the synthesis of HFCs. For example, HFC-134a (1,1,1,2-tetrafluoroethane, C2H2For R134a), one of the most widely used HFCs, can be prepared from trichloroethylene or tetrachloroethylene through halogen exchange and hydrofluorination, in which chlorine is replaced by hydrogen and fluorine, or through isomerization followed by hydrogenolysis, in which hydrogen is used to split an isomer into the desired reaction products. Other HFCs may be prepared through the fluorination of olefins (unsaturated hydrocarbons containing at least one carbon-carbon double bond) with hydrogen fluoride.

Once released into the atmosphere, HFCs decompose relatively quickly; for example, the atmospheric lifetime for HFC-134a is about 14 years. (CFCs, by comparison, can remain in the atmosphere for 100 years.) The breakdown of HFCs occurs in the troposphere (the lowest portion of the atmosphere), where they are split by reactions with hydroxyl radicals (∙OH). Within the troposphere, the carbon-fluorine bonds in HFCs are highly effective at trapping solar radiation (specifically, infrared radiation) and redirecting that radiant energy toward Earth’s surface. This so-called positive radiative forcing effect contributes to global warming (about 14% of future warming in a low-carbon world).

In 2007 the average 100-year global warming potential (GWP) of HFCs was estimated to be 3,770 times that of carbon dioxide (the standard reference chemical for GWP calculations); weighted averaging (based on HFC consumption) predicted a 100-year GWP of 2,400 by 2040. Warming potential, however, varies widely for the individual HFCs. The GWPs of HFCs range from 53 to almost 15,000. The most commonly used is HFC 134a with a 100-year GWP of over 100.

HFC-23 (trifluoromethane, CHF3), which is generated as a by-product in the production of the hydrochlorofluorocarbon HCFC-22 (chlorodifluoromethane, CHClF2), has an atmospheric lifetime of 270 years and a 100-year GWP of 11,700, which surpasses known GWPs for some of the most environmentally harmful CFCs. HCFC-22 has been banned in Europe because of this, and a substitute HFC with GWP of only 3 found. That alternative should be investigated by the United States as a much less harmful mobile refrigerant in a carbon-constrained world.

HFCs have become increasingly abundant in Earth’s atmosphere. For example, between 1978 and 2005, atmospheric concentrations of HFC-23 increased from about 3 to around 18 parts per trillion (ppt). Likewise, concentrations of HFC-134a increased from levels that were undetectable prior to the 1990s to about 35 ppt in 2005. Because they are anthropogenic (human-generated) sources of positive radiative forcing, HFC emissions have been targeted for reduction by the Kyoto Protocol. Every molecule of HFC production can be expected to eventually escape to the atmosphere. Their use as refrigerants is virtually assured in a planet that is heating up.

Kara Rogers, supplemented with information from Greenpeace and IPCC by Paul Suckow

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Tricks and Traditional Whiz-dim

Pet Loss Hotline

Pet Loss Hotline at (877) GRIEF-10.

That’s 877-474-3310.

There are many forms of grief that are completely normal in the wake of the loss of a beloved pet. For support dealing with the loss of a pet, call the ASPCA Pet Loss Hotline.

How can I tell if the pet is still alive?

Use a small mirror. If it fogs up next to the nose, the pet is still breathing. If not, it has likely died. Your nearby vet can help you if you are not sure. For a short time you may be able to revive it using full-breath, human-mouth to pet-nose resuscitation. However if your pet does not revive after three inflations, your pet has passed on.

Cats: Normally the eyes open at death, with wide, dilated pupils. The eyeballs will be soft to the touch, and she won’t blink after death.

How long before the body starts decomposing?

Rigor mortis (body stiffening) will set in within about 3 hours, so time is of the essence. When you hear flies gathering, it’s about time you do something. For a small body, this may happen within hours of death, so don’t delay. If the body is warm it will decompose faster, so find a way to cool the body such as laying it on cool concrete. At most you may store the wrapped body in a refrigerator for 24 hours. Longer than this will require placing in a large enough freezer, and freezing will stop a vet from examining the body to determine cause of death.

What do I do with the body?

You may have your sanitation/waste people haul the covered body away from a fully closed trash receptacle (cheapest), group cremate or individually cremate your pet’s body, inter it in a pet cemetery as group or single burial (each option is more expensive). Find out from your local vet, humane society or police department (not 911) whether you may bury your pet’s body in your back yard (rear garden) before you attempt this. Some jurisdictions have laws about this. It is not possible to bury the body in land that you do not personally own, such as a public space.

If you choose to bury your pet, call 1-800 Miss Dig (1-800-647-7344) before excavating the hole. Plan to put it in the highest and driest ground in your yard, to assist decomposition. However, stay downhill from any water well, in fact 50 to 100 feet away from any source of water, including drainage ditches. If you hit bedrock at the bottom of your pet’s grave, don’t use that spot because water can leach over the rock and become contaminated. Note that roots run seemingly everywhere underground. Consider carefully the size of a root before you attempt to spade through it. You can bury a pet above or below roots of typical trees, shrubs and ground cover, no problem. Just realize that being anywhere within the root zone of a plant means you will run into a root or three.

If your pet died of natural causes and was not diseased, you can let the body decompose into the earth naturally. For this the top of your pet’s body should be under 1-1/2 feet (1/2 meter) of soil, and some mounding is OK.

If your pet was diseased or euthanized with anesthetic chemicals, you need to enclose the pet’s body in a sturdy plastic bag before burial. A box or simple coffin is optional. A larger pet requires a hole at least three to four feet deep (1 meter or more deep) and large enough to bed the body or its container yet leave at least a couple feet (half meter) of compacted soil above.

Bury the pet with some of its favorite things, as you may wish. I thought a plastic food/water bowl he favored as a kitten would last longer than the bones and puzzle future archeologists, so I buried my cat with that.

You can conduct a funeral for those that knew the pet, if you like. Treat it like the family member that it was. This may be important for closure, especially for grieving children. A memorial service can be just as effective as a burial ceremony.

You may want to permanently memorialize your pet’s grave in a way that is special to you. If you have buried the body, especially if the grave is shallow, placing stone or brick markers on top is a good way to prevent scavengers from disturbing the body. I placed a heavy potted plant on the grave I dug for my pet cat of 17 years early this morning (7/20/2017 – RIP).


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Tricks and Traditional Whiz-dim

Basic Training on Where Veterans Live (Trulia on Forbes)

NOV 10, 2014 @ 12:39 PM 5,949 2 FREE Issues of Forbes

Where Veterans Live

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