89 datasets found
  1. a

    Hiring And Retention (Quarterly)

    • strategic-performance-cccd-gis.hub.arcgis.com
    Updated May 1, 2024
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    Clayton County GIS (2024). Hiring And Retention (Quarterly) [Dataset]. https://strategic-performance-cccd-gis.hub.arcgis.com/datasets/hiring-and-retention-quarterly
    Explore at:
    Dataset updated
    May 1, 2024
    Dataset authored and provided by
    Clayton County GIS
    Description

    Departmental-Specific Goals - Hiring and Retention KPIs for Clayton County Police Department.

  2. PhD Origin and Current Affiliation of Global GIS Faculty

    • figshare.com
    xlsx
    Updated Sep 16, 2024
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    Yanbing Chen (2024). PhD Origin and Current Affiliation of Global GIS Faculty [Dataset]. http://doi.org/10.6084/m9.figshare.27023800.v1
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    xlsxAvailable download formats
    Dataset updated
    Sep 16, 2024
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Yanbing Chen
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    It includes data that support the findings of the study (GIScience Faculty Mobility)

  3. D

    Rent Burden Greater than 50%

    • data.seattle.gov
    • catalog.data.gov
    application/rdfxml +5
    Updated Oct 22, 2024
    + more versions
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    (2024). Rent Burden Greater than 50% [Dataset]. https://data.seattle.gov/dataset/Rent-Burden-Greater-than-50-/v29m-enhz
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    csv, json, xml, application/rdfxml, tsv, application/rssxmlAvailable download formats
    Dataset updated
    Oct 22, 2024
    Description
    Displacement risk indicator showing how many households within the specified groups are facing severely housing cost burden (contributing more than 50% of monthly income toward housing costs).
  4. a

    Short Term Rental Districts

    • hub.arcgis.com
    • data.virginia.gov
    • +3more
    Updated Oct 5, 2022
    + more versions
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    City of Virginia Beach - Online Mapping (2022). Short Term Rental Districts [Dataset]. https://hub.arcgis.com/datasets/dc2b4943aa2d4e8b91411f65a0f0ead0
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    Dataset updated
    Oct 5, 2022
    Dataset authored and provided by
    City of Virginia Beach - Online Mapping
    Area covered
    Description

    Special Service Districts are created to provide financing for city services specific to a particular geographic area. The geographic areas and purpose are determined and identified in the Virginia Beach Code or Ordinances. They are associated with the levy of additional taxes. The data is maintained in the Cadastral system and published to the publication database weekly on Saturday.

  5. D

    Rent Burden Greater than 30%

    • data.seattle.gov
    • catalog.data.gov
    • +2more
    application/rdfxml +5
    Updated Oct 22, 2024
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    (2024). Rent Burden Greater than 30% [Dataset]. https://data.seattle.gov/dataset/Rent-Burden-Greater-than-30-/w4qi-sry8
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    csv, application/rdfxml, application/rssxml, tsv, xml, jsonAvailable download formats
    Dataset updated
    Oct 22, 2024
    Description

    Displacement risk indicator showing how many households within the specified groups are facing housing cost burden (contributing more than 30% of monthly income toward housing costs).

  6. s

    Licensed Short-term Rental

    • data.scottsdaleaz.gov
    • azgeo-data-hub-agic.hub.arcgis.com
    • +3more
    Updated Sep 14, 2023
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    City of Scottsdale GIS (2023). Licensed Short-term Rental [Dataset]. https://data.scottsdaleaz.gov/datasets/licensed-short-term-rental-
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    Dataset updated
    Sep 14, 2023
    Dataset authored and provided by
    City of Scottsdale GIS
    License

    https://www.scottsdaleaz.gov/AssetFactory.aspx?did=69351https://www.scottsdaleaz.gov/AssetFactory.aspx?did=69351

    Area covered
    Description

    City of Scottsdale Short-term rental approved licenses. This data is updated daily. Please view the Data Dictionary for a detailed explanation of the data available on this map and the connected table.

  7. D

    Rental Units by Affordability Category

    • data.seattle.gov
    • catalog.data.gov
    • +2more
    application/rdfxml +5
    Updated Oct 22, 2024
    + more versions
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    (2024). Rental Units by Affordability Category [Dataset]. https://data.seattle.gov/dataset/Rental-Units-by-Affordability-Category/cyf7-3ujh
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    xml, tsv, csv, json, application/rssxml, application/rdfxmlAvailable download formats
    Dataset updated
    Oct 22, 2024
    Description
    Displacement risk indicator showing the distribution of renter households and renter units between different income brackets, covering the entire city from 2006 to the most recent year of data available.
  8. D

    Apartment Market Rent Prices by Census Tract

    • data.seattle.gov
    • catalog.data.gov
    • +2more
    application/rdfxml +5
    Updated Feb 3, 2025
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    (2025). Apartment Market Rent Prices by Census Tract [Dataset]. https://data.seattle.gov/dataset/Apartment-Market-Rent-Prices-by-Census-Tract/h27p-5k3i
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    application/rssxml, tsv, csv, json, application/rdfxml, xmlAvailable download formats
    Dataset updated
    Feb 3, 2025
    Description

    Displacement risk indicator classifying census tracts according to apartment rent prices in census tracts. We classify apartment rent along two dimensions:

    1. The median rents within the census tract for the specified year, balancing between nominal rental price and rental price per square foot.
    2. The change in median rent price (again balanced between nominal rent price and price per square foot) from the previous year.
    Note: Median rent calculations include market-rate and mixed-income multifamily apartment properties with 5 or more rental units in Seattle, excluding special types like student, senior, corporate or military housing.

    Source: Data from CoStar Group, www.costar.com, prepared by City of Seattle, Office of Planning and Community Development

  9. s

    Syracuse Rental Registry

    • data.syr.gov
    • data-syr.opendata.arcgis.com
    • +1more
    Updated Jun 17, 2024
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    admin_syr (2024). Syracuse Rental Registry [Dataset]. https://data.syr.gov/maps/4d07491bc81b4d248f22de5da2641021_0/about
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    Dataset updated
    Jun 17, 2024
    Dataset authored and provided by
    admin_syr
    License

    https://data.syrgov.net/pages/termsofusehttps://data.syrgov.net/pages/termsofuse

    Area covered
    Description

    The Rental Registry is a Periodic Inspection Program of the Division of Code Enforcement (DOCE). Owners of any one- or two-family non-owner occupied dwellings rented or leased within the City of Syracuse are required to apply for and obtain a Rental Registry Certificate every 3 years. Please note that the data displayed here are limited and may not fully represent all Rental Registry status details. Please refer to our FAQ page for additional help, and contact the DOCE Periodic Inspection Coordinator at mhigh@syrgov.net or (315) 448-4700 to: Request an eligible property be added to the programRequest application, status, or inspection detailsIf you cannot find the information you’re looking for here Data Dictionary:Label: PropertyAddressDefinition: Parcel street number and street address.Label: parcel_idDefinition: Unique parcel identifier.Label: ZIPDefinition: Zip code of parcels street addressLabel: SBLDefinition: Stands for Section, Block, Lot. This is a property tax number unique to this property.Label: VacantDefinition: Y = Determined vacant by the Division of Code EnforcementLabel: NeedsRRDefinition: Whether a parcel is assigned the user defined "Rental Registry Required," this is added to properties that are 1-2 family non-owner occupiedLabel: transaction_to_property_idDefinition: Unique number that joins this property to its owner information.Label: completion_dateDefinition: The date that the completion type was entered on a Rental Registry caseLabel: completion_type_nameDefinition: The two completion types associated with the Rental Registry are "Rental Registry Card Issued" and "Family Based Exemption Granted"Label: inpsection_periodDefinition: The date associated with the Rental Registry case (this is updated manually at different periods for different reasons -- it could be the date a new case/inspection period was created, the date of an upcoming scheduled inspection, or the date that the property is coming due to reapply -- may not be hugely useful for the public without access to all of the case details)Label: insp_comp_is_activeDefinition: Whether or not the Rental Registry of any given property is currently valid.Label: valid_untilDefinition: The date that the current Rental Registry certificate will expire - this is 3 years from the date that the last completion (RR card or family based exemption) was issued.Label: rr_is_validDefinition: Whether or not the Rental Registry of any given property is currently valid.Label: LatitudeDefinition: Latitude of this parcel.Label: LongitudeDefinition: Longitude of this parcel.Label: ObjectIDDefinition: Unique identifier, assigned by ArcGIS to the dataset.Dataset Contact Information:Organization: City of Syracuse - Office of Accountability, Performance, and Innovation (API)Position: Data Program ManagerCity: Syracuse, NYE-Mail Address: opendata@syrgov.net

  10. Spatial distribution of housing rental value in Amsterdam 1647-1652

    • zenodo.org
    • data.niaid.nih.gov
    bin, csv, jpeg, png +1
    Updated Apr 24, 2025
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    Weixuan Li; Weixuan Li (2025). Spatial distribution of housing rental value in Amsterdam 1647-1652 [Dataset]. http://doi.org/10.5281/zenodo.7473120
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    txt, csv, png, bin, jpegAvailable download formats
    Dataset updated
    Apr 24, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Weixuan Li; Weixuan Li
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Amsterdam
    Description

    This dataset visualises the spatial distribution of the rental value in Amsterdam between 1647 and 1652. The source of rental value comes from the Verponding registration in Amsterdam. The verponding or the ‘Verpondings-quohieren van den 8sten penning’ was a tax in the Netherlands on the 8th penny of the rental value of immovable property that had to be paid annually. In Amsterdam, the citywide verponding registration started in 1647 and continued into the early 19th century. With the introduction of the cadastre system in 1810, the verponding came to an end.

    The original tax registration is kept in the Amsterdam City Archives (Archief nr. 5044) and the four registration books transcribed in this dataset are Archief 5044, inventory 255, 273, 281, 284. The verponding was collected by districts (wijken). The tax collectors documented their collecting route by writing down the street or street-section names as they proceed. For each property, the collector wrote down the names of the owner and, if applicable, the renter (after ‘per’), and the estimated rental value of the property (in guilders). Next to the rental value was the tax charged (in guilders and stuivers). Below the owner/renter names and rental value were the records of tax payments by year.

    This dataset digitises four registration books of the verponding between 1647 and 1652 in two ways. First, it transcribes the rental value of all real estate properties listed in the registrations. The names of the owners/renters are transcribed only selectively, focusing on the properties that exceeded an annual rental value of 300 guilders. These transcriptions can be found in Verponding1647-1652.csv. For a detailed introduction to the data, see Verponding1647-1652_data_introduction.txt.

    Second, it geo-references the registrations based on the street names and the reconstruction of tax collectors’ travel routes in the verponding. The tax records are then plotted on the historical map of Amsterdam using the first cadaster of 1832 as a reference. Since the geo-reference is based on the street or street sections, the location of each record/house may not be the exact location but rather a close proximation of the possible locations based on the street names and the sequence of the records on the same street or street section. Therefore, this geo-referenced verponding can be used to visualise the rental value distribution in Amsterdam between 1647 and 1652. The preview below shows an extrapolation of rental values in Amsterdam. And for the geo-referenced GIS files, see Verponding_wijken.shp.

    GIS specifications:

    Coordination Reference System (CRS): Amersfoort/RD New (ESPG:28992)

    Historical map tiles URL (From Amsterdam Time Machine)

    NB: This verponding dataset is a provisional version. The georeferenced points and the name transcriptions might contain errors and need to be treated with caution.

    Contributors

    • Historical and archival research: Weixuan Li, Bart Reuvekamp
    • Plotting of geo-referenced points: Bart Reuvekamp
    • Spatial analysis: Weixuan Li
    • Mapping software: QGIS
    • Acknowledgements: Virtual Interiors project, Daan de Groot

  11. S

    Affordable Rental Housing

    • data.sanjoseca.gov
    • gisdata-csj.opendata.arcgis.com
    Updated Apr 28, 2025
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    Enterprise GIS (2025). Affordable Rental Housing [Dataset]. https://data.sanjoseca.gov/dataset/affordable-rental-housing
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    html, arcgis geoservices rest api, zip, kml, geojson, csvAvailable download formats
    Dataset updated
    Apr 28, 2025
    Dataset provided by
    City of San José
    Authors
    Enterprise GIS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This layer contains information on deed restricted affordable rental housing projects in the City of San Jose - with funding committed to, under construction or completed. Data to be edited on a quarterly basis.

    Data is published on Mondays on a weekly basis.

  12. c

    Yearly Catch Per Unit Angler - Private and Rental Boats - California...

    • map.dfg.ca.gov
    Updated Jan 22, 2024
    + more versions
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    (2024). Yearly Catch Per Unit Angler - Private and Rental Boats - California Recreational Fisheries Survey - 2015 [ds2649] GIS Dataset [Dataset]. https://map.dfg.ca.gov/metadata/ds2649.html
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    Dataset updated
    Jan 22, 2024
    Area covered
    California
    Description

    CDFW BIOS GIS Dataset, Contact: Paulo Serpa, Description: Private and rental boat survey data, are summarized by yearly catch per unit angler.

  13. d

    Annual Public Space Rental Permits

    • catalog.data.gov
    • opendata.dc.gov
    • +2more
    Updated Jun 11, 2025
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    City of Washington, DC (2025). Annual Public Space Rental Permits [Dataset]. https://catalog.data.gov/dataset/annual-public-space-rental-permits
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    Dataset updated
    Jun 11, 2025
    Dataset provided by
    City of Washington, DC
    Description

    Annual Public Space Rental Permits

  14. d

    Loudoun SMD Service Area

    • catalog.data.gov
    • data.virginia.gov
    • +9more
    Updated Nov 22, 2024
    + more versions
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    Loudoun County GIS (2024). Loudoun SMD Service Area [Dataset]. https://catalog.data.gov/dataset/loudoun-smd-service-area-ca081
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    Dataset updated
    Nov 22, 2024
    Dataset provided by
    Loudoun County GIS
    Area covered
    Loudoun County
    Description

    Beginning January 1, 2020, the Shared Mobility Device (SMD) Pilot Program restricts the operation of SMDs for hire to the designated SMD Service Area. SMDs include motorized skateboards and scooters (e-scooters), electric-assist bikes (e-bikes) and pedal bicycles for hire. For more information about the pilot program, visit www.Loudoun.gov/SharedMobilityDevice.

  15. m

    Milwaukee County Workforce Demographics 07/12/2023

    • data.county.milwaukee.gov
    Updated Aug 1, 2023
    + more versions
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    Milwaukee County GIS & Land Information (2023). Milwaukee County Workforce Demographics 07/12/2023 [Dataset]. https://data.county.milwaukee.gov/datasets/2e8299ee7dc3486581b5a1ad6d56680c
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    Dataset updated
    Aug 1, 2023
    Dataset authored and provided by
    Milwaukee County GIS & Land Information
    Area covered
    Milwaukee County
    Description

    Data updated quarterly.

    Data Attributes and Definitions - 
    -  Department: The department the employee works in.
    -  Department ID: The numeric identifier for the department (typically 4
    

    digits). - Job: The name for the job assigned to the employee. - Category: Grouping of employees in similar jobs/leadership roles. - Sub Category: Secondary grouping of employees within a category. - Race/Ethnicity: The race/ethnicity category which the employee identifies with (self-identified).

    -  Gender: Designates the employee's
    

    gender (self-identified). - Age: The chronological number (age) assigned to the employee based on date of birth. - Age Group: Grouping of employees having approximately the same age or age range. - Original Hire Date: Date upon which the employee was originally hired.

    -  Last Hire Date: Date upon which an
    

    employee was hired; may be a rehire date. - Pay Class: Defines how the employee gets paid for hours worked based on defined rules (full-time, part-time, hourly, etc.) - Data As of: The date to which the given data applies to.

  16. d

    2.20 Employee Vertical Diversity (summary)

    • catalog.data.gov
    • open.tempe.gov
    • +8more
    Updated Jun 28, 2025
    + more versions
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    City of Tempe (2025). 2.20 Employee Vertical Diversity (summary) [Dataset]. https://catalog.data.gov/dataset/2-20-employee-vertical-diversity-summary-0c1c3
    Explore at:
    Dataset updated
    Jun 28, 2025
    Dataset provided by
    City of Tempe
    Description

    It is important to identify any barriers in recruitment, hiring, and employee retention practices that might discourage any segment of our population from applying for positions or continuing employment at the City of Tempe. This information will provide better awareness for outreach efforts and other strategies to attract, hire, and retain a diverse workforce.This page provides data for the Employee Vertical Diversity performance measure. The performance measure dashboard is available at 2.20 Employee Vertical Diversity. Additional InformationSource:PeopleSoft HCM, Maricopa County Labor Market Census DataContact: Lawrence LaVictoireContact E-Mail: lawrence_lavicotoire@tempe.govData Source Type: Excel, PDFPreparation Method: PeopleSoft query and PDF are moved to a pre-formatted Excel spreadsheet.Publish Frequency: Every six monthsPublish Method: ManualData Dictionary

  17. c

    Geographic Information System Software Market was valued at USD 8.5 billion...

    • cognitivemarketresearch.com
    pdf,excel,csv,ppt
    Updated Jan 1, 2023
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    Cognitive Market Research (2023). Geographic Information System Software Market was valued at USD 8.5 billion in 2022! [Dataset]. https://www.cognitivemarketresearch.com/geographic-information-system-software-market-report
    Explore at:
    pdf,excel,csv,pptAvailable download formats
    Dataset updated
    Jan 1, 2023
    Dataset authored and provided by
    Cognitive Market Research
    License

    https://www.cognitivemarketresearch.com/privacy-policyhttps://www.cognitivemarketresearch.com/privacy-policy

    Time period covered
    2021 - 2033
    Area covered
    Global
    Description

    Global Geographic Information System Software Market was valued at USD 8.5 billion in 2022 and will reach USD 21.0 billion by 2030, registering a CAGR of 12.1% for the forecast period 2023-2030. Factor Impacting the Geographic Information System Software Market:

    The development of smart cities and Modern urban Planning is expected to drive the Geographic Information System Software Market
    

    The process of site selection, land acquisition, planning, designing, visualizing, building, project management, operations, and reporting are all aided by geographic information system (GIS) software for smart cities. Moreover, geographic information system (GIS) solutions are used in urban planning by experts to better properly analyze, model, and visualize places. By processing geospatial data from satellite imaging, aerial photography, and remote sensors, geographic information system (GIS) software systems offer a comprehensive perspective of the land and infrastructure. Additionally, the industry for geographic information system software is growing over the forecast period as a result of such geographic information system (GIS) software applications.

    Restraining factor for Geographic Information System Software Market

    The high cost of the system has impacted the Geographic Information System Software Market
    

    The pricey geographic information system will further derail the overall market’s growth. The geographic information system (GIS) is expensive because, in addition to the technology and software, it is necessary to have a properly qualified human workforce. Moreover, Specialized knowledge is needed to comprehend and interpret the information gathered by a geographic information system (GIS) system, which is expensive to hire and train. This factor will therefore obstruct market growth over the forecast period. What is Geographic Information System Software?

    Geographic Information System Software is used to develop, hold, retrieve, organize, display, and perform analyses on many kinds of spatial and geographic data. The geographic information system (GIS) Industry is majorly driven by infrastructural developments, such as smart cities, water and land management, utility, and urban planning. The services segment provides various applications such as location-based services and, thus, is one of the prominent contributors to the market share. Advancements in GIS technologies, such as geo-analytics and integrated location-based data services, are also boosting the adoption of GIS in various regional markets, thereby driving the market demand over the forecast period.

  18. a

    Rental Inspection Districts

    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    • opendata.winchesterva.gov
    • +3more
    Updated Feb 6, 2024
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    City of Winchester, Virginia (2024). Rental Inspection Districts [Dataset]. https://arc-gis-hub-home-arcgishub.hub.arcgis.com/maps/winchestercity::rental-inspection-districts
    Explore at:
    Dataset updated
    Feb 6, 2024
    Dataset authored and provided by
    City of Winchester, Virginia
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    This data shows the rental inspection districts located in the City of Winchester, Virginia.

  19. W

    A GIS Story

    • cloud.csiss.gmu.edu
    esri rest, html
    Updated Apr 12, 2019
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    United States (2019). A GIS Story [Dataset]. https://cloud.csiss.gmu.edu/uddi/dataset/a-gis-story
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    esri rest, htmlAvailable download formats
    Dataset updated
    Apr 12, 2019
    Dataset provided by
    United States
    License

    https://opendata.cityofboise.org/datasets/9cd5de5b07864fb6987efd1d550c8089/license.jsonhttps://opendata.cityofboise.org/datasets/9cd5de5b07864fb6987efd1d550c8089/license.json

    Description

    This Story Map portrays the evolution of GIS at the City of Boise. Boise City GIS was initiated in 1989 when the Public Works Department hired a senior GIS analyst to create and maintain sewer data in a GIS. Over the years, in response to ever increasing demands for GIS services, Boise City GIS continued to grow and evolve. In 2006 GIS was reorganized, placing the GIS manager and GIS systems analysts under the Information & Technology Department to support the GIS infrastructure and develop a full-fledged enterprise GIS. At the same time, several GIS analysts and technicians were placed in other departments to support specific business functions


    For more information please visit City of Boise GIS & Mapping.

  20. H

    Affordable Rental Housing Inventory - HHFDC

    • opendata.hawaii.gov
    Updated Mar 21, 2023
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    Office of Planning (2023). Affordable Rental Housing Inventory - HHFDC [Dataset]. https://opendata.hawaii.gov/dataset/affordable-rental-housing-inventory-hhfdc
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    arcgis geoservices rest api, ogc wfs, ogc wms, pdf, html, csv, kml, zip, geojsonAvailable download formats
    Dataset updated
    Mar 21, 2023
    Dataset provided by
    Hawaii Statewide GIS Program
    Authors
    Office of Planning
    Description

    [Metadata] Inventory of the State of Hawaii’s affordable housing projects as of February 2023. The list includes affordable housing projects owned by private, non-profit, or governmental entities, developed with funding or support from federal, state or county resources. Data was downloaded from the HHFDC website (https://dbedt.hawaii.gov/hhfdc/affordable-housing-inventory/affordable-rental-housing-inventory/) in PDF format by Hawaii Statewide GIS Program staff, converted to Excel and geocoded in ArcGIS Pro. Projects with no addresses were not included. Data updates are posted periodically on the HHFDC website; users should check the site for the latest copy of the PDF file. For more information, please refer to metadata at https://files.hawaii.gov/dbedt/op/gis/data/Afford_Rent_Hsng_Inv_HHFDC.pdf or contact Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, Hi. 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

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Clayton County GIS (2024). Hiring And Retention (Quarterly) [Dataset]. https://strategic-performance-cccd-gis.hub.arcgis.com/datasets/hiring-and-retention-quarterly

Hiring And Retention (Quarterly)

Explore at:
Dataset updated
May 1, 2024
Dataset authored and provided by
Clayton County GIS
Description

Departmental-Specific Goals - Hiring and Retention KPIs for Clayton County Police Department.

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