Postal Code Point FSA Zip Post Codes Data
Demographic Data Spending Expenditures Business List
GIS Topographic Maps Points Street Vector Point Boundary MNR OBM Data
Telcommunication Telco Telcom Exchange Data Wire Rate Center LEC CLEC ILEC NPA NXX Solutions
Geografx Digital Mapping Services Custom GIS Maps


Call us now!

416.949.9196 / 905.681.0910
sales@geografx.com

Demographic Census Data GIS
Demographic Data

US Census Bureau Summary File
The US Census SF3 (Summary File 3) data covers social, economic and housing characteristics compiled from a sample of approximately 19 million households in the United States (1 in 6 households) that received the Census 2000 long-form questionnaire. There are two US Census databases released for PCensus: SF3 and SF3+:

1) US 2010 Census SF3 data:
SF3 data has 5,300 variables, in hierarchical sequence, down to the Block Group level. Also included are Zip Code Tabulation Areas (ZCTAs) that are approximate representations of United States Postal Service (USPS) Zip Code service areas.

Key GIS Variables Levels of Geography Geographic Units of Sale Geographic Formats Vintage Geographic Scale Resolution
  • Over 5,300 Variables
  • Population (Total, Urban, Rural)
  • Income
  • Households / Families / Household Size
  • Dwelling Characteristics / Home Values
  • Shelter Costs
  • Number of Vehicles
  • Heating / Telephone
  • Marital Status / Grandparents as Caregivers
  • Language / Mother Tongue / Ancestry
  • Place of Birth / Citizenship / Year of Entry
  • Migration / Place of Work / Commuting
  • Education / Employment Status / Occupation
  • Industy / Class of Worker / Poverty Status
  • Block Group
  • Census Tract / CT
  • County / MSA
  • County / MSA
  • State
  • National / USA
  • .dbf
  • .csv
  • .mdb
  • .tab
  • .shp
  • 2010
  • Flat File / Database

2) US 2010 Census SF3+ data:
SF3+ data has 16,000 variables, in hierarchical sequence, down to the Census Tract level, Zip Code and County level. To assist users in dealing with 16,000 sf3+ variables, there are two PCensus templates available:

  • 2000 SF3+ “Standard” template - the most popular variables compiled into 22 tables.
  • 2000 SF3+ “Detailed” template - all SF3 data in 404 tables.
Key GIS Variables Levels of Geography Geographic Units of Sale Geographic Formats Vintage Geographic Scale Resolution
  • Over 16,000 Variables, organized into 404 tables
  • 196 Population tables and 208 Housing tables
  • Population Counts
  • Income by Housesholder Age
  • Income by House / Rent Value
  • Real Estate Taxes
  • Households with Vehicles by Race
  • Rent by Race
  • Housing Values by Race
  • Census Tract / CT
  • Zip Code Tabulation Area / ZCTA
  • County / MSA
  • County / MSA
  • State
  • National / USA
  • .dbf
  • .csv
  • .mdb
  • .tab
  • .shp
  • 2010
  • Flat File / Database

Block level data (*only available for use in PCensus, v7.0):
Block's are "a subdivision of a Census Tract", which is the smallest geographic unit for which the census bureau tabulates 100% data. Many Blocks correspond to individual city Blocks bounded by streets, but Blocks, especially in rural areas, may include many square miles and may have some boundaries that are not streets. The US Census Bureau established Blocks covering the entire nation for the first time in 1990. Over 8 million Blocks versus 208,000 Block Groups are identified for Census 2000.

The data were collected and tabulated by the US Census Bureau for each block. Pcensus databases use the block population and dwelling counts as factors to compute values “on-the-fly” for all census variables at the block level and higher. Having a factor of 40 times more census data points allows much greater resolution for calculating profiles of user defined areas (circles, polygons, drive times). This 2000 census data must be mapped with 2000 census geography / boundaries / Zip Codes.

Typical Applications: Statistical Census Analysis, Ethnicity, Age, Sex, Religion Studies, Trade Area and Market Analysis, Service Studies and Resource Allocation, Urban and Development Planning, Store and Site Location Research, Direct Target Marketing and Mail Distribution Campaigns, Household, Population and Income Studies, Demographic Profiling and Trend Analysis.

Related GIS Data
Related GIS Software
Demographic Data

Core Demographics
The Core Demographics database includes a wide range of demographic variables for the current year and 5- year projections, covering five broad topic areas: population, households, income, labor force and dwellings. With a foundation of the Experian household level databases and over fifteen years of experience in demographic forecasting, AGS offers the highest quality demographic estimates in the marketplace today.

Methodology: Core Demographics has been built using a variety of Data Sources including:

  • Census tabulations from 1980, 1990 and most recently, the release of the 2000 Census, USPS and commercial source ZIP+4 level delivery statistics. Census Bureau estimates and projections of population characteristics at various levels of geographic detail, including the latest estimates of population at the city level.
  • Bureau of Labor Statistics estimates and projections of employment by industry and occupation at the county level.
  • Medicare eligible population counts at the ZIP code level, including population by sex and 5-year age cohorts, provided by the Health Care Financing Administration of Social Security. These counts provide a very accurate local count of the population aged 65 and higher.
  • Internal Revenue Service statistics on tax filers and year-to-year migration.
  • The Census Bureau’s Current Population Survey, which provides detailed demographic breakdowns and enables a thorough longitudinal analysis of demographic trends.
  • Experian’s INSOURCE database, a household level credit and demographic database which covers the vast majority of households.
Key GIS Variables Levels of Geography Geographic Units of Sale Geographic Formats Vintage Geographic Scale Resolution
  • Over 200 variables, organized into 45 Parent Categories
  • Population
  • Households
  • Income
  • Labor Force
  • Dwellings
  • Block Group
  • Census Tract
  • Zip Code/ ZCTA
  • County / MSA
  • County / MSA
  • State
  • National / USA
  • .dbf
  • .csv
  • .mdb
  • .tab
  • .shp
  • 2012
  • Flat File / Database

Typical Applications: Statistical Census Analysis, Ethnicity, Age, Sex, Religion Studies, Trade Area and Market Analysis, Service Studies and Resource Allocation, Urban and Development Planning, Store and Site Location Research, Direct Target Marketing and Mail Distribution Campaigns, Household, Population and Income Studies, Demographic Profiling and Trend Analysis.

Estimates and Projections Canada

Estimates and Projections: 2015
The Estimates and Projections database is the highest quality and most comprehensive demographic update available for the US Census 2010. The dataset includes a wide range of demographic variables for the current year and 5- year projections, covering Population, Households, Income, Labor Force and Dwellings. The dataset is developed from Census tabulations from 1980, 1990, 2000 and 2010 Census, USPS and commercial source ZIP+4 level delivery statistics, latest estimates of population at the city level, Bureau of Labor Statistics estimates and projections of employment by industry and occupation at the county level, Medicare eligible population counts at the ZIP code level, including population by sex and 5-year age cohorts, provided by the Health Care Financing Administration of Social Security for local counts of the population aged 65 and higher, Internal Revenue Service statistics on tax filers and year-to-year migration, Census Bureau’s Current Population Survey, which provides detailed demographic breakdowns and enables a thorough longitudinal analysis of demographic trends and Experian’s INSOURCE database, a household level credit and demographic database.

Key GIS Variables Levels of Geography Geographic Units of Sale Geographic Formats Vintage Geographic Scale Resolution
  • Population
  • Population by Household Type / Population by Age and Sex
  • Population by Race / Ethnic Origin / Nationality
  • Population by Marital Status
  • Population by Educational Achievement
  • Household Income / Aggregate Income / Family Income
  • Households / Households by Type
  • Households by Size / Households by Age of Maintainer
  • Disposable Income
  • Age of Household Maintainer by Income
  • Dwellings / Vacancy / Tenure
  • Vehicles Available
  • Labor Force Occupation / Industry
  • Zip Code/ ZCTA
  • State
  • National / USA
  • .dbf
  • .csv
  • .mdb
  • .tab
  • .shp
  • 2015
  • Flat File / Database

Typical Applications: Population and Income Forecasts, Forecasting New Trade Areas and Sales Regions, New, Proposed and Potential Developments, New Store and Site Locations Research, Public and Institutional Facility Planning, Service and Urban Planning, Transportation and Facility Planning, Demographic Profiling and Trend Analysis.

CSP HEP Consumer Spending Potentials Household Expenditures

Consumer Spending Data / Household Expenditures: 2015
The Consumer Spending Data / Household Expenditures lets you identify trends in consumer spending and buying habits, to identify the best areas to market your products and services. The dataset provides estimates of Total Expenditures, Average Spending per Household and Spending Potential Indicies (SPI) on hundreds of consumer goods and services. The database is built / derived from the lastest release of annualized data from Consumer Expenditure Surveys (CEX) from the Bureau of Labor Statistics and ESRI's Community Tapestry Segmentation System. Data is extracted from quarterly interview surveys and weekly diary surveys. Data is reported and organized by product or service. A conditional probability model links spending by consumers surveyed to all households with similar socioeconomic characteristics. Spending patterns are developed by Community Tapestry segments and updated by adjusting to current levels of income. Expenditures represent the 2007 annual averages and totals. ESRI revises the average expenditure to reflect the average amount spent per household. Total expenditure is the aggregate amount spent by all households in an area. The SPI compares the average expenditure for a product locally to the average amount spent nationally. An index / benchmark of 100 is average. An SPI of 120 shows that average spending by local consumers is 20 percent above the national average. Consumer Spending Categories Line items are summarized into parent product and service categories.

Key GIS Variables Levels of Geography Geographic Units of Sale Geographic Formats Vintage Geographic Scale Resolution
  • Over 100 variables, organized into 17 parent categories
  • Food at Home / Food Away from Home
  • Alcoholic Beverages
  • Housing
  • Household Goods and Services
  • Apparel / Retail Goods
  • Transportation / Travel
  • Health Care
  • Entertainment / Recreation
  • Personal Care
  • Education
  • Life Insurance / Pensions / Financial
  • Block Group
  • Census Tract / CT
  • Zip Code / ZCTA
  • County
  • State
  • National / USA
  • .dbf
  • .csv
  • .mdb
  • .tab
  • .shp
  • 2015
  • Flat File / Database

Typical Applications: Market Share / Potential and Trade Area Analysis, Market Research Data for Goods and Services, Market Demand, Store Location Research, Site Selection, Market Assessment, Development and Network Planning, Planning Lifestyle Profiling, Franchising and Expansion, Sales Forecasting and Analysis, Direct Target Marketing and Mail Distribution Campaigns, Market Penetration and Market Share Analysis, Competitor and Huff Modelling, Retail Location Analysis, Demographic Profiling and Trend Analysis.

CSP HEP Consumer Spending Potentials Household Expenditures

AGS Consumer Spending Data
The Consumer Spending database covers most major household expenditures in a multi-level hierarchical classification. The database consists of a total of 493 base variables, which are aggregated in up to four levels of detail. A hierarchical structure is utilized throughout, so that it is possible to aggregate or disaggregate categories as required for analysis. Expenditures can be expressed either as aggregate expenditure or per household expenditure for any geographic level from the block group to national. The major categories represented are: Total Expenditure; Food and Beverages; Shelter Utilities Household Operations Household Furnishings/Equipment Apparel Transportation Health Care Entertainment Personal Care Reading Education Tobacco Products Miscellaneous Expenses Cash Contributions Personal Insurance Gifts

Most of these categories include two or three levels of sub-category detail. For example, a typical classification for an item in the food group is:

  • TOTAL Total Expenditure
  • FB Food and Beverage
  • FB1 Food At Home
  • FB102 Dairy Products
  • FB10201 Cheese

This structure permits ready analysis of expenditures at any level of detail and between levels of detail. It is possible to analyze any individual category within the context of its parent category (e.g. cheese expenditures as a share of total dairy product expenditures or total food at home expenditures).

Methodology: The consumer spending database consists of a multi-level hierarchical classification of household expenditures, which covers the majority of annual household expenditures. It is derived from an extensive modeling effort using the 1998, 1999 and 2000 Consumer Expenditure Survey data from the Bureau of Labor Statistics. The BLS survey is a comprehensive survey that averages over 5,000 households four times a year using a rotating sampling frame. The use of several consecutive years of data provides a rich base of expenditure data from which to build expenditure models based on household demographics. The database consists of a total of 493 base variables, which are aggregated in up to four levels of detail. A hierarchical structure is utilized throughout, so that it is possible to aggregate or disaggregate categories as required for analysis.

The survey includes a wide range of demographic attributes related to “consuming units” (generally households), which have been modeled separately for each discrete expenditure category. The older surveys were first inflated to the 1997 price levels using the detailed consumer price index series. For each individual expenditure category in the survey, summary statistics were calculated for each separate element in the list below. In several cases, it was possible to utilize cross tabulation data (e.g. income by age of head of household). These variables are listed below:

  • Geographic region (Northeast, South, Midwest, West)
  • Metropolitan status (metropolitan, non-metropolitan) and size (e.g. > 4 million)
  • Housing tenure (owner or renter)
  • Age of head of household (<25 years, 25-34 years, 35-44 years, 45-54 years, 55-64 years, 65-74 years, and 75+ years)
  • Size of household (1 person, 2 persons, 3 persons, 4 persons, 5 persons, 6+ persons)
  • Household income (<5000, 5-10000, 10-15000, 15-20000, 20-30000, 30-40000, 40-50000, 50-70000, 70000+)
  • Race (White, Black, American Indian, Asian)
  • Number of vehicles (none, 1, 2+ vehicles per household)

The total sample was utilized to obtain an average expenditure for each item. For each expenditure item, a series of adjustment factors were derived for each unique demographic attribute. These adjustment factors were then applied to the block group level using the same demographic variables in order to create estimates at the local level, which are consistent with local characteristics. Consistency checks were undertaken in order to ensure that the results at the block group level were consistent in the aggregate with overall income levels and published expenditures. Finally, the 1998 estimates were inflated using detailed consumer price indexes to current year levels. In addition to providing average household expenditures, AGS also provides total market estimates for use in market share and demand analysis.

Key GIS Variables Levels of Geography Geographic Units of Sale Geographic Formats Vintage Geographic Scale Resolution
  • Over 493 variables, organized into 17 parent categories
  • Total Expenditure
  • Food and Beverages
  • Shelter
  • Utilities
  • Household Operations
  • Household Furnishings/Equipment
  • Apparel
  • Transportation
  • Health Care
  • Entertainment
  • Personal Care
  • Reading
  • Education
  • Tobacco Products
  • Miscellaneous Expenses
  • Cash Contributions
  • Personal Insurance
  • Gifts
  • Block Group
  • Census Tract / CT
  • Zip Code / ZCTA
  • County / MSA
  • County
  • State
  • National / USA
  • .dbf
  • .csv
  • .mdb
  • .tab
  • .shp
  • 2012
  • Flat File / Database

Typical Applications: Market Share / Potential and Trade Area Analysis, Market Research Data for Goods and Services, Market Demand, Store Location Research, Site Selection, Market Assessment, Development and Network Planning, Planning Lifestyle Profiling, Franchising and Expansion, Sales Forecasting and Analysis, Direct Target Marketing and Mail Distribution Campaigns, Market Penetration and Market Share Analysis, Competitor and Huff Modelling, Retail Location Analysis, Demographic Profiling and Trend Analysis.

Health Care Ambulance Hospital Data

Health Utilization Data
Treo Solutions is pleased to provide the Health Utilization Demographic Dataset. This dataset contains over 100 variables, containing unique information regarding several important aspects of outpatient and physician services. The dataset is built / developed from National Ambulatory Medical Care Survey, National Hospital Ambulatory Medical Care Survey, National Health Interview Survey, Persistent Regional Differences Database, Medical Management Index, Trend Database and Applied Geographic Systems. The Datasets can be used for a number of strategic planning, physician recruiting and programmatic development initiatives.

Key GIS Variables Levels of Geography Geographic Units of Sale Geographic Formats Vintage Geographic Scale Resolution
  • Ambulatory Care: Volumes / Estimates of physician office visits, by specialty, generated by the service area.
  • Behavioral Medicine: Project future behavioral health demand for physician services and inpatient beds.
  • Cancer: Determine if demand exists in the service area for a dedicated cancer care program.
  • Cardiovascular Care: Project future cardiovascular demand for physician services, surgeries and inpatient beds.
  • Diagnosis Related Groups: Determine inpatient volumes by MDC and individual DRG. Eye Care Project future eye care demand for physician services and outpatient surgeries.
  • Pediatric: Determine needed physician resources, ER visits, inpatient bed requirements and ambulatory surgery needs for pediatric population.
  • Rehabilitation: Project future rehabilitation demand for physician services by specialty and determine the need for inpatient beds.
  • Renal Care: Determine the physician needs, inpatient bed need and outpatient services needed for end-stage renal disease.
  • Respiratory: Project future respiratory demand for physician services by specialty, determine ER need and determine the need for inpatient beds.
  • Senior Care: Determine future number of Medicare eligibles in the service area, the number of physician office visits by specialty, required ambulatory services, the prevalence of chronic conditions and general disease incidence for seniors in the service area over 65 years of age.
  • Women’s Care: Determine future number of physician office visits by specialty, required ambulatory services, and required inpatient and outpatient services for women 15 and older.
  • Key County Demographics: Determine number of M.D.’s, D.O.’s, Physician Assistants, Nurse Practitioners, Chiropractors and Medicaid enrollees by county
  • Zip Code / ZCTA
  • County
  • State
  • State
  • National / USA
  • .dbf
  • .csv
  • .mdb
  • .tab
  • .shp
  • 2000 / 2015
  • Flat File / Database

Typical Applications: Strategic planning, Physician Recruiting and Health Care Program Development. Service and Demand / Needs Analysis for Health Care, Programs, Physicians, Inpatient Beds / Requirements, Ambulatory Services / Surgery, Diagnosis Clinics, Outpatient Surgeries, R Visits, End-Stage Renal Disease, Senior Care, Medicare Eligibles, Physician Office visits by Specialty, General Disease Incidence, Women's Care, M.D.’s, .O.’s, Assistants, Nurse Practitioners, Chiropractors and Medicaid.

Canada Business Summary

Business Points Data
The Business Points Data is MapInfo's Comprehensive Business Database for Building Sales and Marketing Strategies. The database contains more than 15 million geographic points of business locations throughout the U.S. This data allows you to see business locations in a given geographic area, and gather valuable information on those businesses such as business name, address, SIC code, employee sizes for both the business location and parent company family, sales volumes for both the location and parent company family, ownership structure, and more.

This level of detail makes Business Points Data ideal for: Analyzing market opportunities; Analyzing competitive threats; Building sales and marketing strategies; Assigning sales territories Records / entries are geocoded to the street level accuracy. Data is updated / assessed for accuracy, by first looking at businesses which belong to a corporate family (Wal-Mart, McDonalds, Home Depot, etc.), then at single-site businesses which belong to a generic profile (barber shops, dry cleaners, florists, etc.), then at single-site businesses which don't meet a generic profile but belong to a specific industry as determined by their 8-digit SIC code (lawyers, general hospitals, post offices, etc.). Once the employee value outliers are identified, they are shifted back into an appropriate position along the employee distribution curve. This methodolgy maximizes the reliability / reasonableness of employee values throughout the entire national dataset.

Key GIS Variables Levels of Geography Geographic Units of Sale Geographic Formats Vintage Geographic Scale Resolution
  • Business Name
  • Address
  • Standard Industrial Classification Code / SIC / NAICS
  • Description
  • Sales Revenue Physical Location
  • Sales Revenue Parent Location / Company Total
  • Employee Size Physical Location
  • Employee Size Parent Location / Company Total
  • Ownership Structure
  • Point
  • State
  • Region
  • National / USA
  • .tab
  • .shp
  • 2015
  • 1:2,000

Typical Applications: Direct Target Marketing and Mail Distribution Campaigns, Sales Leads and Lists, Telecommunications, Grid and Fibre Optic Network Planning, Service and Rate Assessments, Broadband and Internet Service Planning, B2B Sales Territory Allocation, Competitive Threats, Territory Allocation / Assignments, Sales and Marketing Strategies, B2B Market Penetration and Market Share Analysis, Site Location Models, Business to Business Marketing, Relocation Studies, Location Analysis, Rate and Service Area Assessment, Franchising, Business and Industry Analysis, Client Searches, Geocoding, Contact Information, Competitor Analysis, NAICS and SIC Profiling, Business Location and Profiling.

Crime Risk Data Canada

CrimeRisk Data
CrimeRisk is a block group and higher level geographic database consisting of a series of standardized indexes for a range of serious crimes against both persons and property. It is derived from an extensive analysis of several years of crime reports from the vast majority of law enforcement jurisdictions nationwide. The crimes include murder, rape, robbery, assault, burglary, larceny, and motor vehicle theft. These categories are the primary reporting categories used by the FBI in its Uniform Crime Report (UCR), with the exception of Arson, for which data is very inconsistently reported at the jurisdictional level.

In accordance with the reporting procedures using in the UCR reports, aggregate indexes have been prepared for personal and property crimes separately, as well as a total index. While this provides a useful measure of the relative “overall” crime rate in an area, it must be recognized that these are unweighted indexes, in that a murder is weighted no more heavily than a purse snatching in the computation. For this reason, caution is advised when using any of the aggregate index values.

Methodology: The primary source of CrimeRisk was a careful compilation and analysis of the FBI Uniform Crime Report databases. On an annual basis, the FBI collects data from each of about 16,000 separate law enforcement jurisdictions at the city, county, and state levels and compiles these into its annual Uniform Crime Report (UCR). The latest national crime report can be obtained either from the FBI web site in Adobe Portable Document (PDF) format or can be ordered directly from the FBI. While useful, the UCR provides detailed data only for the largest cities, counties, and metropolitan areas. Virtually all jurisdictions nationwide participate in the UCR program. In 1996, the overall coverage rate was 95%, with 97% coverage in metropolitan areas. Rural coverage is somewhat lower at 87% and non-metropolitan area cities at 90%. In order to undertake the analysis, AGS obtained detailed jurisdictional level data for the years 1990 - 1996 (the latest year currently available) and supplemented these detailed statistics with 1999 preliminary UCR statistics at the State level and for cities and metropolitan areas where those have been released. We are now using UCR data from 1996-2003. A considerable effort was made to correct a number of problems that are prevalent within the FBI databases, including:

  • The standardization of jurisdictional names: the FBI does not employ Census bureau codes in its databases and the jurisdictional names contain numerous typographical errors and format discrepancies which needed to be manually corrected
  • Reporting by individual jurisdictions can be inconsistent from year to year, in that data for some jurisdictions is missing for one or more years and required handling
  • Reporting for some crime types is inconsistent between jurisdictions. The FBI handles this by simply suppressing the statistics entirely for those areas. This primarily affects the rape category for Illinois, where statistics are suppressed for all but the largest jurisdictions. These missing values were handled via the modeling process, in which rape estimates were prepared for these jurisdictions by using a model which related rape incidence to other crime types
  • The standardization of the database to account for jurisdictional overlaps. For example, the California Highway Patrol has jurisdiction over only state and Interstate highways in urban areas
  • Crime rates in general have been declining over the past several years, so it was necessary to adjust the historical data to reflect current crime rates.

Once this correction and standardization effort was completed, the database consisted of a time series of six years of data covering:

  • All cities and towns which have their own police agency
  • All cities and towns where policing for the local jurisdiction is contracted to a higher level agency but which tracks statistics separately.
  • A record for each county, which covers the population not covered by either of the two cases above. This is normally either a County Sheriff (or equivalent) or a State level jurisdiction, which reports incidence of crime by county (e.g. in New York, the State Trooper).

The initial models were undertaken using a subset of this database. In the smallest cities, a single murder will have a profound effect on the crime rate per 100,000 population that would severely distort the resulting models. Cities with less than 2,500 people were reassigned to their parent counties for the purpose of the analysis. A wide range of 1990 Census and current year demographic attributes was extracted from AGS’ databases for the remaining areas (approximately 8,500 separate “jurisdictions”). This database was then used as the primary modeling database and was used later for scaling purposes.

Each of the seven crime types was modeled separately, using an initial range of about 65 socio-economic characteristics taken from the 2000 Census and AGS’ current year estimates. Separate models were constructed for each of the nine Census regions (e.g. New England, East North Central, Pacific) in order to account for regional differences in crime rates and the demographic characteristics, which underlay them. The models constructed typically accounted for over 85% of the variance in crime rates at this “jurisdiction” level, although it should be noted that the results for property crimes were generally more reliable than for personal crimes.

The results of these models were then applied to the block group level using the same demographic attributes compiled at the block group level. The resulting estimates were then scaled to match the master database of 8,500 jurisdictions. For cities, the block groups within each city were scaled to match the city total. For areas outside of these cities (or for smaller centers), results were scaled to match the county total after adjusting for those cities scaled separately.

The final crime rate estimates were then weighted by population and aggregated to the national totals. CrimeRisk Variables Current Year Estimates -------------------------------------------------------------------------------- Total Crime Index Personal Crime Index Murder Rape Robbery Assault Property Crimes Burglary Larceny Motor Vehicle Theft t.

Key GIS Variables Levels of Geography Geographic Units of Sale Geographic Formats Vintage Geographic Scale Resolution
  • Total Crime Index
  • Personal Crime Index
  • Murder
  • Rape
  • Robbery
  • Assault
  • Property Crimes
  • Burglary
  • Larceny
  • Motor Vehicle Theft
  • Block Group
  • Census Tract
  • Zip Code
  • County / MSA
  • County
  • State
  • National / USA
  • .tab
  • .shp
  • 2015
  • Flatfile

Typical Applications: Crime Area Analysis, Crime, Loss and Risk Prevention, VClass and Area Profiling, Dispatch and Service Planning, Police and Security Planning, Patrol Route Planning, Civil and Municipal Planning, Real Estate Analysis, Insurance Rate Assessments and Premiums.


Copyright © 2015 Geografx Digital Mapping Services.
About us    |    Home Page    |    Maps and GIS Data    |    GIS Services    |    Contact us    |    Privacy policy |    Terms of Use Legal Notice