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 purpose of this web map is to provide an opportunity to fine tune the locations of Roadrunner food pantries.The Agencies and MFP csv file was downloaded and geocoded on ArcGIS desktop with the ESRI ArcGIS Online World Geocode Service address locator to include details about the way points were placed on the map. These details are left out when geocoding from ArcGIS online. The details can be helpful in editing the placement of points on the map.

There were 5 types of ways addresses were used which influences the accuracy of the placement of points on the map.

There were 142 agencies geocoded using ‘Point Address’ type

100 agencies geocoded using ‘Street Address’ type

30 agencies geocoded using ‘Postal’ type

26 agencies geocoded using ‘Street Name’ type

1 agency geocoded using ‘Locality’ type (this point was placed in El Dátil, Mulegé, Baja California Sur, Mexico)

The ‘addr_type’ field recorded the address type used for each agency point on the map.

The ‘match_addr’ field recorded the address used for geocoding.

The ‘Point Address’ and ‘Street Address’ types indicate that the point was placed in front of a street address or building. The points are offset from the street. They may appear as if they are on the street centerline. However, if you zoom in, you will find that the points are on the correct side of the street (maybe in the street image from the basemap but not on the Census Tiger centerline). That level of detail is probably not of concern for Roadrunner, but it may become an issue when data is aggregated to areas like census tracts or counties. For that purpose, another geocoding address locator called GSAF may be used later.

Anyway, Point Address and Street Address points should be accurate for our purposes for now.

Agencies with ‘Postal’ address types were located in the center of a zip code area. These points should be moved to a more appropriate location.

Agencies with ‘Locality’ address types were placed in town or city centers. These points could be moved to a more accurate location.

Agencies with ‘Street name’ address types were placed on a street but not necessarily at the correct address. Usually these points are placed in the center of the street length. These points could be moved to a more accurate location.

I’ve created a web mapping application that will enable us to move points to better locations depending on the level of detail you wish to use or share. It would benefit those NMCDC users and map makers using Roadrunner data to have as many points moved to close to the actual location as possible.

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. 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.

Estimated Cohen’s Kappa and percent disagreement [95% confidence interval] of census tract and block group Federal Information Processing Standards (FIPS) assignments resulting from DeGAUSS and vendor tool geocoding process, by geographic census unit.

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.

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!

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/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Public Housing Developments Date Updated: Q1 2025

This data includes counts of deaths by race (total, Black, white) by age grouping and cause of death by Census Tract aggregated over a five-year period (2014-18). Data extracted from Pennsylvania's Electronic Death Registry System (EDRS) with the following disclaimer: "These data were provided by the Pennsylvania Department of Health. The Department specifically disclaims responsibility for any analyses, interpretations, or conclusions." Census tract of residence was determined using address-level data. Records were excluded from analysis if address was missing or unmatched to a census tract (≈1% records). Census tracts starting with 980x.xx, 981x.xx, and 982x.xx were also excluded due to a geocoding error. For cause of death by census tract, counts were assessed using census tract by age and race; records were excluded if age, race, or location data were missing. Census-tract level counts < 5 are censored and displayed as NULL. Census-tract-level counts may not equal county-level counts when summed due to censored data or missing data.

Poverty Area MeasuresThis data product provides poverty area measures for counties across 50 States and Washington DC. The measures include indicators of high poverty areas, extreme poverty areas, persistent poverty areas, and enduring poverty areas for Decennial Census years 1960–2000 and for American Community Survey (ACS) 5-year periods spanning both 2007–11 and 2015–19.HighlightsThis data product uniquely provides poverty area measures at the census-tract level for decennial years 1970 through 2000 and 5-year periods spanning 2007–11 and 2015–19.The poverty area measure—enduring poverty—is introduced, which captures the entrenchment of high poverty in counties for Decennial Census years 1960–2000 and for ACS 5-year periods spanning 2007–11 and 2015–19. The same is available for census tracts beginning in 1970.High and extreme poverty area measures are provided for various data years, offering end-users the flexibility to adjust persistent poverty area measures to meet their unique needs.All measures are geographically standardized to allow for direct comparison over time and for census tracts within county analysis.Diverse geocoding is provided, which can be used for mapping/GIS applications, to link to supplemental data (e.g., USDA, Economic Research Service’s Atlas of Rural and Small-Town America), and to explore various spatial categories (e.g., regions and metro/nonmetro status). DefinitionsHigh poverty: areas with a poverty rate of 20.0 percent or more in a single time period.Extreme poverty: areas with a poverty rate of 40.0 percent or more in a single time period.Persistent poverty: areas with a poverty rate of 20.0 percent or more for 4 consecutive time periods, about 10 years apart, spanning approximately 30 years (baseline time period plus 3 evaluation time periods).Enduring poverty: areas with a poverty rate of 20.0 percent or more for at least 5 consecutive time periods, about 10 years apart, spanning approximately 40 years or more (baseline time period plus four or more evaluation time periods).Additional information about the measures can be found in the downloadable Excel file, which includes the documentation, data, and codebook for the poverty area measures (county and tract).The next update to this data product—planned for early 2023—is expected to include the addition of poverty area measures for the 5-year period 2017–21.Data SetLast UpdatedNext UpdatePoverty area measures (in CSV format)11/10/2022Poverty area measures11/10/2022Poverty Area MeasuresOverviewBackground and UsesERS's Legacy of Poverty Area MeasurementDocumentationDescriptions and MapsLast updated: Thursday, November 10, 2022For more information, contact: Tracey Farrigan and Austin SandersRecommended CitationU.S. Department of Agriculture, Economic Research Service. Poverty Area Measures, November 2022.

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

Public Housing was established to provide decent and safe rental housing for eligible low-income families, the elderly, and persons with disabilities. Public housing comes in all sizes and types, from scattered single family houses to high-rise apartments for elderly families. There are approximately 1.2 million households living in public housing units, managed by over 3,300 housing agencies (HAs). HUD administers Federal aid to local housing agencies (HAs) that manage the housing for low-income residents at rents they can afford. HUD furnishes technical and professional assistance in planning, developing and managing these developments. 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. To learn more about Public Housing visit: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Public Housing Authorities Date Updated: Q1 2025

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: Q1 2025

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.

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.

Cleveland Department of Public Health (CDPH) complaint intake records. These are reported problems from the public about food safety, air issues, or residential nuisances within City of Cleveland limits. Complaints outside of City limits and CDPH jurisidiction are handled by the Cuyahoga County Board of Health (CCBH).Official complaints from the public are an important way for the Cleveland Department of Public Health (CDPH) to track problems in the area. Complaints received by the office are forwarded to an inspection staff member for investigation.These complaints are reported via phone or online form.Data GlossaryRecords are from 2014 to present.id: System ID for complaintcomplaint_number: Primary number for tracking this complaintsubmit_date: Submission daysubmit_time: Submission time HH:MMcomplaint_type: Type of complaint, from drop-down list in formcomplaint_input: Source of the submission, from a staff member or website formcomplaint_inspector: Inspector assigned to complaintcomplaint_status: Status of complaint in the systemcomplaint_outcome: Listed outcome of investigationfood_complaint: Food type complaint detailfarm_animal: Animal type complaint detailinsect_vermin: Insect type complaint detailodor_type: Odor type complaint detailodor_strength: Odor type complaint strength detailproblem_location_name: Listed name of location by submitterproblem_address: Problem location addressproblem_street_direction: Problem location street direcitonproblem_street_name: Problem location streetproblem_street_type: Problem location street typeproblem_unit_number: Problem location unit numberproblem_city: Problem location cityproblem_zip_code: Problem location zip codeproblem_date_time: Submitted time details of the problemmac_complaint_id: Parent complaint if stemming from prior submissionpermanent_parcel_number: Parcel number as submitted. Note this is user-entered info for the complaint.census_tract: Census Tract numberward_number: City ward numberdw_ward: The city ward number, as a result of geocoding the complaint address.dw_neighborhood: The neighborhood, as a result of geocoding the complaint address.dw_census_tract: The census tract number, as a result of geocoding the complaint address.dw_parcel: The parcel number, as a result of geocoding the complaint address.Update FrequencyWeekly on Sunday at 1:00 AM ESTContactCleveland Department of Public Health - Website

The dataset contains a subset of locations and attributes of incidents reported in the ASAP (Analytical Services Application) crime report database by the District of Columbia Metropolitan Police Department (MPD). Visit crimecards.dc.gov for more information. This data is shared via an automated process where addresses are geocoded to the District's Master Address Repository and assigned to the appropriate street block. Block locations for some crime points could not be automatically assigned resulting in 0,0 for x,y coordinates. These can be interactively assigned using the MAR Geocoder.On February 1 2020, the methodology of geography assignments of crime data was modified to increase accuracy. From January 1 2020 going forward, all crime data will have Ward, ANC, SMD, BID, Neighborhood Cluster, Voting Precinct, Block Group and Census Tract values calculated prior to, rather than after, anonymization to the block level. This change impacts approximately one percent of Ward assignments.

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. New to the June 2022 version, a separate data file is available for retired postal codes. The retired file uses the same record layout as the PCCF file. The same syntax file can be used for both the PCCF data file and the retired data file. 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.

SummaryThis data set shows building permits for the Baltimore metropolitan region. The data goes back to 2000 and is updated approximately once every two months. Expanded building permit data can be found at https://www.baltometro.org/community/data-maps/building-permit-data.DescriptionThe permits include any permit that is use code 40-48 (most new residential), 60-65 (mixed use), or is greater than or equal to $50,000. Historically, BMC receives the permits from participating jurisdictions and geocodes them. In recent years, some jurisdictions have started geocoding their own permits. When this is the case, BMC incorporates the geocoded points as given, and does not include them in its own geocoding process.Expanded building permit data can be found at https://www.baltometro.org/community/data-maps/building-permit-data.Layers:BPDS_Residential_New_ConstructionBPDS_Residential_AlterationsBPDS_Non_Residential_New_ConstructionBPDS_ Non_Residential _AlterationsBPDS_Mixed_Use_New_ConstructionThere is no layer for Mixed Use alterations; alterations to Mixed Use always get classified as Residential or Non-Residential.Field NamesField Name (alias)Descriptionpermit_no (County Permit ID)Original permit ID provided by the jurisdictionissue_dt (Date Permit Was Issued)Date the permit was issuedxcoord (X Coordinate)Longitude, in NAD 1983 decimal degreesycoord (Y Coordinate)Latitude, in NAD 1983 decimal degreessite_addr (Site Address)Address of the constructionzipcode (Site Zipcode)Zipcode of the constructionoffice (Office Number)This number corresponds to a jurisdiction and is used for BMC administrative recordspmt_use (Permit Use)Permit use code. A list of the values can be found at https://gis.baltometro.org/Application/BPDS/docs/BPDS_Permit_Use_Codes.pdfpmt_type (Permit Type)Permit type code. A list of the values can be found at https://gis.baltometro.org/Application/BPDS/docs/BPDS_Permit_Use_Codes.pdfdevelopment_name (Development Name / Subdivision)Subdivision name, if providedunit_count (Number of Units)Number of units, if provided. Only found in residential recordstenure (Tenure)If provided, indicates whether building is expected to be for rent or for sale after construction is complete. 1=For Rent, 2=For Saleamount (Amount)Estimated cost of constructionpmt_cat (Permit Category)Simplified classification of the pmt_use and pmt_type fieldsdescrip (Description)Description of construction, if providedJurisdiction (Jurisdiction)Jurisdiction (a county or city)Update CycleThe data is updated approximately once every three months.User NoteOver the years, building permit points were geocoded using a variety of software and reference data. The Baltimore Metropolitan Council made every effort to ensure accurate geocoding however there may be inaccuracies or inconsistencies in how the points were placed. For best results, the Baltimore Metropolitan Council recommends aggregating the building permit points to a larger geography (ex. Census tract, zip code) when analyzing the data.Data Access InstructionsTo download the data or access it via API, visit https://gisdata.baltometro.org/.Technical ContactFor questions or comments, contact Erin Bolton, GIS Coordinator, at ebolton@baltometro.org or 410-732-0500.

The dataset contains a subset of locations and attributes of incidents reported in the ASAP (Analytical Services Application) crime report database by the District of Columbia Metropolitan Police Department (MPD). Visit https://crimecards.dc.gov for more information. This data is shared via an automated process where addresses are geocoded to the District's Master Address Repository and assigned to the appropriate street block. Block locations for some crime points could not be automatically assigned resulting in 0,0 for x,y coordinates. These can be interactively assigned using the MAR Geocoder.On February 1 2020, the methodology of geography assignments of crime data was modified to increase accuracy. From January 1 2020 going forward, all crime data will have Ward, ANC, SMD, BID, Neighborhood Cluster, Voting Precinct, Block Group and Census Tract values calculated prior to, rather than after, anonymization to the block level. This change impacts approximately one percent of Ward assignments.

The dataset contains a subset of locations and attributes of incidents reported in the ASAP (Analytical Services Application) crime report database by the District of Columbia Metropolitan Police Department (MPD). Visit crimecards.dc.gov for more information. This data is shared via an automated process where addresses are geocoded to the District's Master Address Repository and assigned to the appropriate street block. Block locations for some crime points could not be automatically assigned resulting in 0,0 for x,y coordinates. These can be interactively assigned using the MAR Geocoder.On February 1 2020, the methodology of geography assignments of crime data was modified to increase accuracy. From January 1 2020 going forward, all crime data will have Ward, ANC, SMD, BID, Neighborhood Cluster, Voting Precinct, Block Group and Census Tract values calculated prior to, rather than after, anonymization to the block level. This change impacts approximately one percent of Ward assignments.