The Postal Code Conversion File (PCCF) is a digital file which provides a correspondence between the Canada Post Corporation (CPC) six-character postal code and Statistics Canada's standard geographic areas for which census data and other statistics are produced. Through the link between postal codes and standard geographic areas, the PCCF permits the integration of data from various sources. The Single Link Indicator provides one best link for every postal code, as there are multiple records for many postal codes. To obtain the postal code conversion file or for questions, consult the DLI contact at your educational institution. The geographic coordinates attached to each postal code on the PCCF are commonly used to map the distribution of data for spatial analysis (e.g., clients, activities). The location information is a powerful tool for planning, or research purposes. The geographic coordinates, which represent the standard geostatistical areas linked to each postal codeOM on the PCCF, are commonly used to map the distribution of data for spatial analysis (e.g., clients, activities). The location information is a powerful tool for marketing, planning, or research purposes. In April 1983, the Statistical Registers and Geography Division released the first version of the PCCF, which linked postal codesOM to 1981 Census geographic areas and included geographic coordinates. Since then, the file has been updated on a regular basis to reflect changes. For this release of the PCCF, the vast majority of the postal codesOM are directly geocoded to 2016 Census geography while others are linked via various conversion processes. A quality indicator for the confidence of this linkage is available in the PCCF.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Face refers to the areal (polygon) topological primitives that make up MTDB. A face is bounded by one or more edges; its boundary includes only the edges that separate it from other faces, not any interior edges contained within the area of the face. The Topological Faces Shapefile contains the attributes of each topological primitive face. Each face has a unique topological face identifier (TFID) value. Each face in the shapefile includes the key geographic area codes for all geographic areas for which the Census Bureau tabulates data for both the 2020 Census and the annual estimates and surveys. The geometries of each of these geographic areas can then be built by dissolving the face geometries on the appropriate key geographic area codes in the Topological Faces Shapefile.

NOTE: As of 2/16/2023, this page is not being updated. For data on updated (bivalent) COVID-19 booster vaccination click here: https://app.powerbigov.us/view?r=eyJrIjoiODNhYzVkNGYtMzZkMy00YzA3LWJhYzUtYTVkOWFlZjllYTVjIiwidCI6IjExOGI3Y2ZhLWEzZGQtNDhiOS1iMDI2LTMxZmY2OWJiNzM4YiJ9

This table shows the number and percent of people that have initiated COVID-19 vaccination and are fully vaccinated by CT census tract (including residents of all ages). It also shows the number of people who have not received vaccine and who are not yet fully vaccinated.

All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected.

A person who has received at least one dose of any vaccine is considered to have initiated vaccination. A person is considered fully vaccinated if they have completed a primary series by receiving 2 doses of the Pfizer, Novavax or Moderna vaccines or 1 dose of the Johnson & Johnson vaccine. The fully vaccinated are a subset of the number who have received at least one dose.

The percent with at least one dose many be over-estimated and the percent fully vaccinated may be under-estimated because of vaccine administration records for individuals that cannot be linked because of differences in how names or date of birth are reported.

Population data obtained from the 2019 Census ACS (www.census.gov) Geocoding is used to determine the census tract in which a person lives. People for who a census tract cannot be determined based on available address data are not included in this table. DPH recommends that these data are primarily used to identify areas that require additional attention rather than to establish and track the exact level of vaccine coverage. Census tract coverage estimates can play an important role in planning and evaluating vaccination strategies. However, inaccuracies in the data that are inherent to population surveillance may be magnified when analyses are performed down to the census tract level. We make every effort to provide accurate data, but inaccuracies may result from things like incomplete or inaccurate addresses, duplicate records, and sampling error in the American Community Survey that is used to estimate census tract population size and composition. These things may result in overestimates or underestimates of vaccine coverage.

Some census tracts are suppressed. This is done if the number of people vaccinated is less than 5 or if the census population estimate is considered unreliable (coefficient of variance > 30%). Coverage estimates over 100% are shown as 100%.
Connecticut COVID-19 Vaccine Program providers are required to report information on all COVID-19 vaccine doses administered to CT WiZ, the Connecticut Immunization Information System. Data on doses administered to CT residents out-of-state are being added to CT WiZ jurisdiction-by-jurisdiction. Doses administered by some Federal entities (including Department of Defense, Department of Correction, Department of Veteran’s Affairs, Indian Health Service) are not yet reported to CT WiZ. Data reported here reflect the vaccination records currently reported to CT WiZ. Caution should be used when interpreting coverage estimates in towns with large college/university populations since coverage may be underestimated. In the census, college/university students who live on or just off campus would be counted in the college/university town. However, if a student was vaccinated while studying remotely in his/her hometown, the student may be counted as a vaccine recipient in that town. As part of continuous data quality improvement efforts, duplicate records were removed from the COVID-19 vaccination data during the weeks of 4/19/2021 and 4/26/2021.

As of 1/13/2021, census tract level data are provider by town for all ages. Data by age group is no longer available.

The Postal Code Conversion File (PCCF) is a digital file which provides a correspondence between the Canada Post Corporation (CPC) six-character postal code and Statistics Canada's standard geographic areas for which census data and other statistics are produced. Through the link between postal codes and standard geographic areas, the PCCF permits the integration of data from various sources. The Single Link Indicator provides one best link for every postal code, as there are multiple records for many postal codes. Getting started guide To obtain the postal code conversion file or for questions, consult the DLI contact at your educational institution. The geographic coordinates attached to each postal code on the PCCF are commonly used to map the distribution of data for spatial analysis (e.g., clients, activities). The location information is a powerful tool for planning, or research purposes. The geographic coordinates, which represent the standard geostatistical areas linked to each postal codeOM on the PCCF, are commonly used to map the distribution of data for spatial analysis (e.g., clients, activities). The location information is a powerful tool for marketing, planning, or research purposes. In April 1983, the Statistical Registers and Geography Division released the first version of the PCCF, which linked postal codesOM to 1981 Census geographic areas and included geographic coordinates. Since then, the file has been updated on a regular basis to reflect changes. For this release of the PCCF, the vast majority of the postal codesOM are directly geocoded to 2011 Census geography while others are linked via various conversion processes. A quality indicator for the confidence of this linkage is available in the PCCF.

Estimated Cohen’s Kappa and percent disagreement of census tract and block group Federal Information Processing Standards (FIPS) assignments resulting from DeGAUSS and vendor tool geocoding process, stratified by urban/rural category.

The annual inventory of licences from sports federations approved by the Ministry responsible for sports makes it possible to measure the level and evolution over time of supervised sports practice. These statistics shed light on public policies for the development of sport, both at national and territorial level. This is a census at the person's place of residence and not at the place of practice. The data from the census are then geocoded by INSEE for metropolis + DROM (excluding Mayotte), in order to be able to communicate these files at the municipal level. Data are not available for all federations. A number of them did not have fully geolocatable data to the municipality allowing exhaustive exploitation. The geocoded data have therefore been processed in order to be able to provide a estimate of the number of licences per municipality and federation. The data for vintage N correspond to season N-1/N or calendar year N depending on the functioning of the federations (e.g. lic-data-2021 is a distribution of licenses for the 2020/2021 season or the year 2021). The 2019 data have been revised (2nd geocoding operation required). From 2019, some changes have been made in the files transmitted: -Common precision level-QPV and no longer common -Age steps of the licence census and not of the five-year census -Population data not included in the file -Distinction of out-of-field data (Mayotte, Monaco, COM, Foreign) vs. undistributed data -Data for the municipalities of Mayotte not included (excluding geocoding) -Addition of licenses not distributed in the file (sum of licenses corresponds to the result of the census) -The distribution for 3 federations is limited to the department level (FF Maccabi, FS of the National Police, F of the defense clubs)

HOLC Frequency in real and virtual enumeration districts.

Results of likelihood ratio tests for differences in percent disagreement among strata of census tract and block group assignments between DeGAUSS and vendor tool geocoding.

Context

Federal Superfund sites are some of the most polluted in the United States. This dataset contains a multifaceted view of Superfunds, including free-form text descriptions, geography, demographics and socioeconomics.

Content

The core data was scraped from the National Priorities List (NPL) provided by the U.S. Environmental Protection Agency (EPA). This table provides basic information such as site name, site score, date added, and links to a site description and current status. Apache Tika was used to extract text from the site description pdfs. The addresses were scraped from site status pages, and used to geocode to latitude and longitude and Census block group. The block group assignment was used to join with the Census Bureau's planning database, a rich source of nationwide demographic and socioeconomic data. The full source code used to generate the data can be found here, on github.

I have provided three separate downloads to explore:

• priorities_list_full.json: the NPL containing all geographic, site information, text descriptions, and Census Bureau data from the relevant block groups.
• pdb_tract.csv: the planning database aggregated on the tract level with an additional indicator (has_superfund) noting whether or not the tract contains the address of a Superfund site.
• pdb_block_group.csv: the planning database aggregated on the block group level with an additional indicator (has_superfund) noting whether or not the block group contains the address of a Superfund site.

Some caveats:

1. The planning database contains 300+ columns. For a full description of these columns, please see the documentation here.
2. Since the Google geocoder is relatively aggressive in providing address matches, geocoding was done through a hierarchy of queries (full address, city-state-zip, and zipcode only) to prevent gross errors. The address string used to geocode is noted through the 'geocode_source' column.
3. While this data is linked to demographic and socioeconomic data based on either the block group (tract for pdb_tract.csv), the impacts of a particular site's pollution may extend beyond these geographic regions.

Acknowledgements

I would like to thank the EPA and the Census Bureau for making such detailed information publicly available. For relevant academic work, please see Burwell-Naney et al. (2013) and references, both to and therein.

Please let me know if you have any suggestions for improving the dataset!

This dataset represents all 827,933 block-faces in Canada for the 1996 census. The dataset was designed for geocoding and census data extraction and it covers 43 urban centres in Canada.

A block-face represents one side of a street between two consecutive features intersecting that street. The dataset includes attribute information for street names (including street types and direction), address ranges, geographic codes for linkages with other census boundaries, geographic coordinates, and population and dwelling counts from the 1996 Census. They are displayed on a map via their representative point, which is the geographic coordinate located at the mid-point of the block-face, set back a perpendicular distance of 22, 11, 5, or 1 metre from the street centre line.

The original dataset is available from Statistics Canada as a text file (.txt). For viewing in Scholars GeoPortal, the dataset was converted from this original format into a Shapefile using the point coordinates available for each record. Each point is the population centre of an Enumeration Area.

The original data, and other supporting files and documentation, are available as additional downloads from Scholars GeoPortal.

The Postal Code Conversion File (PCCF) is a digital file which provides a correspondence between the Canada Post Corporation (CPC) six-character postal code and Statistics Canada's standard geographic areas for which census data and other statistics are produced. Through the link between postal codes and standard geographic areas, the PCCF permits the integration of data from various sources. The Single Link Indicator provides one best link for every postal code, as there are multiple records for many postal codes. Getting started guide To obtain the postal code conversion file or for questions, consult the DLI contact at your educational institution. The geographic coordinates attached to each postal code on the PCCF are commonly used to map the distribution of data for spatial analysis (e.g., clients, activities). The location information is a powerful tool for planning, or research purposes. In April 1983, the Geography Division released the first version of the PCCF, which linked postal codes to 1981 Census geographic areas and included geographic coordinates. Since then, the file has been updated on a regular basis to reflect changes. For this release of the PCCF, the vast majority of the postal codes are directly geocoded to 2006 Census geography. This improves precision of the file over the previous conversion process used to align postal code linkages to new geographic areas after each census. About 94% of the postal codes were linked to geographic areas using the new automated process. A quality indicator for the confidence of this linkage is available in the PCCF.

Racial identification is a critical factor in understanding a multitude of important outcomes in many fields. However, inferring an individual’s race from ecological data is prone to bias and error. This process was only recently improved via Bayesian Improved Surname Geocoding (BISG). With surname and geographic-based demographic data, it is possible to more accurately estimate individual racial identification than ever before. However, the level of geography used in this process varies widely. Whereas some existing work makes use of geocoding to place individuals in precise census blocks, a substantial portion either skips geocoding altogether or relies on estimation using surname or county-level analyses. Presently, the tradeoffs of such variation are unknown. In this letter we quantify those tradeoffs through a validation of BISG on Georgia’s voter file using both geocoded and non-geocoded processes and introduce a new level of geography--ZIP codes--to this method. We find that when estimating the racial identification of White and Black voters, non-geocoded ZIP code-based estimates are acceptable alternatives. However, census blocks provide the most accurate estimations when imputing racial identification for Asian and Hispanic voters. Our results document the most efficient means to sequentially conduct BISG analysis to maximize racial identification estimation while simultaneously minimizing data missingness and bias.

HUD furnishes technical and professional assistance in planning, developing and managing these developments. Public Housing Developments are depicted as a distinct address chosen to represent the general location of an entire Public Housing Development, which may be comprised of several buildings scattered across a community. The building with the largest number of units is selected to represent the location of the development. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes: ‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) ‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) ‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) ‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) ‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) ‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) Null - Could not be geocoded (does not appear on the map) For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. In an effort to protect Personally Identifiable Information (PII), the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10. To learn more about Public Housing visit: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/ Data Dictionary: DD_Public Housing Developments

This dataset denotes ZIP Code centroid locations weighted by population. Population weighted centroids are a common tool for spatial analysis, particularly when more granular data is unavailable or researchers lack sophisticated geocoding tools. The ZIP Code Population Weighted Centroids allows researchers and analysts to estimate the center of population in a given geography rather than the geometric center.

Characteristics of adult patients (≥18 years old) hospitalized with COVID-19 and available geocoding information—COVID-NET catchment areas in 14 states, March 1–April 30, 2020.

Context

Incidents responded to by the Baton Rouge Animal Control and Rescue Center (ACRC).

ACRC is responsible for carrying out duties related to animal-related situations, including: administering the anti-rabies vaccination, licensing, and tag program; investigating animal cruelty incidents; investigating dog fighting; resolving dangerous animal situations; rescuing injured animals; investigating abandoned animal cases; investigating occult, animal sacrifice, and bestiality cases; resolving stray animal situations; enforcing the leash law and owned animal problems; assisting law enforcement with narcotics, evictions, and DWI cases; enforcing barking dog cases; inspecting dog yards/pens; chaining or tethering compliance; assisting animal welfare groups with feral interventions; and conducting educational programs.

As many of the incidents included within this data set involve active cases that are currently under investigation and computerized system limitations do not allow for automated screening of open/closed cases, the identity of animal owners is redacted to protect the privacy of the animal owner. Members of the public interested in the identity of a specific incident may contact ACRC directly to inquire about the incident and, if it is closed, ACRC will release a copy of the file to the person requesting it. However, location data regarding where the incident was reported or occurred is included within this data set, which may or may not be the same location as the animal owner's home or property.

In addition, to protect the identity of the complainant (person filing the complaint or alerting ACRC to a potential incident), only the complainant's street name is included as part of this data set.

Finally, while all incidents are updated on a daily basis, incidents involving animal cruelty are updated based on a rolling 30-day delay to allow for ACRC to investigate the incident

Content

The data was pulled from the City of Baton Rouge Open Data website on 2019-11-18. A lot of the older calls from when they began recording data did not have much, if any, information attached to them so I filtered those out. The remaining data was cleaned up with various Python scripts and R then the addresses were ran through the Census.gov geocoding service. Some addresses would not work on there, so the remaining were ran through Google's geocoding service. There appears to be a bug somewhere in ggmap or Google (I haven't looked into it further) as it fails to process addresses with the "#" symbol in them (101 Main St. Apt. #200). I then created another Python script to format addresses with "#" in them and ran those through Google's geocoding again. Some addresses that were in their database simply do not exist (or used to exist at one time) and those failed geocoding completely. In total, about 20,000 rows had to be discarded leaving us with the below data, which is still quite a bit of calls.

Two columns to be aware of:

• Breed: Some of them have an X in front of the breed. This is not explained on their website and I could not find any solid correlation with the rest of the data for this.
• Disposition: TRANS TO CAA means the animal was brought to a shelter (CAA).

Acknowledgements

D. Kahle and H. Wickham. ggmap: Spatial Visualization with ggplot2. The R Journal, 5(1), 144-161.

http://journal.r-project.org/archive/2013-1/kahle-wickham.pdf

Inspiration

This data details what kind of animals they responded to, including breed, sex, size, age, condition, and temperament. It would be interesting to see if a trend can be identified by a certain type of animal based on the area in the city. What about the vast waterways and woodlands in the area? Louisiana has lots of hunting areas close to the city, could these be tied in and connected with wildlife calls?

HUD administers Federal aid to local Housing Agencies (HAs) that manage housing for low-income residents at rents they can afford. Likewise, HUD furnishes technical and professional assistance in planning, developing, and managing the buildings that comprise low-income housing developments. This dataset provides the location and resident characteristics of public housing development buildings. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes: ‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) ‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) ‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) ‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) ‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) ‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) Null - Could not be geocoded (does not appear on the map) For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. In an effort to protect Personally Identifiable Information (PII), the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10. To learn more about Public Housing visit: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/ Development FAQs - IMS/PIC | HUD.gov / U.S. Department of Housing and Urban Development (HUD), for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Public Housing Buildings Date Updated: Q2 2025

The data demonstrates the location of CDBG-DR-funded buyout activities as part of the Office of Community Planning and Development's (CPD) Disaster Recovery Buyout Program.The data is derived from an extract of HUD CPD’s Disaster Recovery Grants Reporting (DRGR) System, an address-level dataset that includes Community Development Block Grant – Disaster Recovery activities for certain grantees and over a limited span of time during which grantees were required to report addresses of certain funded activities. Buyouts are a unique disaster-related activity made eligible through a waiver in the allocation of CDBG-DR grants following a natural hazard disaster. Under the waiver, grantees are permitted to use CDBG-DR funds to pay the pre-disaster or post-disaster value to acquire properties impacted by a natural hazard, usually flooding, for the purpose of risk reduction. The offer creates an incentive for impacted homeowners to relocate to a residence outside of a high hazard risk area. The property must be maintained by the local jurisdiction as open space indefinitely to eliminate future disaster liability. Each observation in the address-level dataset is a standardized, geocoded address at which a residential buyout took place. The buyouts were reported by grantees through March 31, 2020. The data extract was drawn, geocoded, processed, and aggregated to the census tract-level following the close of 2020 Q1. Only addresses that were geocoded to a moderate to high level of accuracy were included (LVL2KX = "R" (rooftop) or "4" (Zip+4 centroid)). The addresses extracted from DRGR were geocoded using the HUD Batch Geocoder which matches geocoordinates with standard Census geographies. The data contains buyouts completed through March 31, 2020. An activity is reported as “completed” once an end-use is met; for example, buyouts are complete upon legal acquisition of a property. All activities are aggregated to the 2010 Decennial Census Tract geography. Note: The data are not a comprehensive record of all buyouts funded with CDBG-DR. The activities were completed between October 2009 and March 2020. Grantees were required to enter addresses for these activities beginning in 2015. Early reporting of the address information is voluntary.The data being displayed are census tract level counts of CDBG-DR-assisted addresses. In order to protect privacy, census tracts where there were fewer than 11 buyouts display a value of -4.To learn more about the Disaster Recovery Buyout Program, please visit: https://www.hudexchange.info/programs/cdbg-dr/disaster-recovery-buyout-program/#buyout-program-overview-considerations-and-strategies, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_HUD CPD CDBG-DR BuyoutsDate of Coverage: Cumulative through 2020 Q1

The Historical Urban Population, 3700 BC - AD 2000, originally developed by the Yale School of Forestry & Environmental Studies, is the first spatially explicit global data set containing location and size of urban populations over the last 6,000 years. The data set was created by digitizing, transcribing, and geocoding historical, archaeological, and census-based urban population data. Each data point consists of a city name, latitude, longitude, year, population, and a reliability ranking to assess the geographic uncertainty of each data point. Despite spatial and temporal gaps, no other geocoded data set at this resolution exists. It can therefore be used to investigate long-term historical urbanization trends and patterns, evaluate the current era of urbanization, and build a richer record of urban population through history. To provide spatially explicit, historic, city-level population data from 3700 BC to AD 2000 for improved understanding of contemporary and historical urbanization trends.