Tempe is among Arizona's most educated cities, lending to a creative, smart atmosphere. With more than a dozen colleges, trade schools, and universities, about 40 percent of our residents over the age of 25 have Bachelor's degrees or higher. Having such an educated and accessible workforce is a driving factor in attracting and growing jobs for residents in the region.The City of Tempe is a member of the Greater Phoenix Economic Council (GPEC), and with the membership, staff tracks collaborative efforts to recruit business prospects and locations. The Greater Phoenix Economic Council (GPEC) is a performance-driven, public-private partnership. GPEC partners with the City of Tempe, Maricopa County, 22 other communities, and more than 170 private-sector investors to promote the region’s competitive position and attract quality jobs that enable strategic economic growth and provide increased tax revenue for Tempe. This dataset provides the target and actual job creation numbers for the City of Tempe and the Greater Phoenix Economic Council (GPEC). The job creation target for Tempe is calculated by multiplying GPEC's target by twice Tempe's proportion of the population. This page provides data for the New Jobs Created performance measure.The performance measure dashboard is available at 5.02 New Jobs Created. Additional Information Source: Extracted from GPEC monthly and annual reports and proprietary excel filesContact: Madalaine McConvilleContact Phone: 480-350-2927Data Source Type: Excel filesPreparation Method: Extracted from GPEC monthly and annual reports and proprietary Excel filesPublish Frequency: AnnuallyPublish Method: ManualData Dictionary

JOBS PROXIMITY INDEXSummaryThe jobs proximity index quantifies the accessibility of a given residential neighborhood as a function of its distance to all job locations within a CBSA, with larger employment centers weighted more heavily. Specifically, a gravity model is used, where the accessibility (Ai) of a given residential block- group is a summary description of the distance to all job locations, with the distance from any single job location positively weighted by the size of employment (job opportunities) at that location and inversely weighted by the labor supply (competition) to that location. More formally, the model has the following specification: Where i indexes a given residential block-group, and j indexes all n block groups within a CBSA. Distance, d, is measured as “as the crow flies” between block-groups i and j, with distances less than 1 mile set equal to 1. E represents the number of jobs in block-group j, and L is the number of workers in block-group j. The Longitudinal Employer-Household Dynamics (LEHD) has missing jobs data in all of Puerto Rico and a concentration of missing records in Massachusetts. InterpretationValues are percentile ranked with values ranging from 0 to 100. The higher the index value, the better the access to employment opportunities for residents in a neighborhood. Data Source: Longitudinal Employer-Household Dynamics (LEHD) data, 2017. Related AFFH-T Local Government, PHA and State Tables/Maps: Table 12; Map 8. To learn more about the Jobs Proximity Index visit: https://www.hud.gov/program_offices/fair_housing_equal_opp/affh ; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 07/2020

The Saxon water network (flowing and standing waters) is continuously maintained in the GIS specialist database of the LfULG and updated on weekends for the GIS services, iDA and the geodata download. This dataset is referred to as the working status. The dataset is originally based on the ATKIS data of the GeoSN around 2007/2008. The data were professionally processed by the LfULG (e.g. flow direction, coding with area and body of water indicators). The primary basis is the work of the state dam administration and the lower water authorities of the districts and district-free towns in cooperation with the municipalities.

This GIS (Geographic Information System) layer provides detailed information about the sidewalk infrastructure across the city. The layer represents individual sidewalk segments, which are divided at property boundaries and street intersections. Each segment is mapped with precise location data to help understand the layout of sidewalks in the city.PurposeThe dataset supports the City's commitment to maintaining a safe, accessible pedestrian network. It is used by City staff across departments for ADA assessments, maintenance planning, infrastructure upgrades, public inquiries, and capital improvement prioritization.Maintenance & UpdatesThe dataset is maintained by the Transportation Maintenance GIS Specialist. Updates occur as sidewalks change — for example, after new construction, sidewalk repairs, or when previously buried sidewalks are restored. All updates are based on field inspections conducted by trained personnel to ensure data accuracy.While the data was comprehensively reviewed and published in April 2025, it is updated iteratively as changes are reported or observed. There is currently no standardized citywide procedure for notifying the GIS team when sidewalk conditions change, which may result in temporary data gaps.To help keep the dataset accurate, users are encouraged to report discrepancies or recent sidewalk changes using the Cityworks “Street Asset GIS Update” Work Order Template. Known LimitationsThe dataset may not immediately reflect recent sidewalk changes due to delays in reporting and inspection. Coordination with other departments is ongoing to improve update workflows.ContactFor questions about this dataset, please contact the Transportation Maintenance GIS Specialist at isabella.buckley@raleighnc.gov.Key AttributesSidewalk Type – Classification of the sidewalkStreet Name – Name of the adjacent streetFeature Length & Width – Dimensions of the sidewalk segmentSidewalk Material – Surface material

ACEC_ARC: This data set shows the boundary lines for Areas of Critical Environmental Concern under BLM management in Oregon and Washington. The district Data Steward will define the ACEC boundary and work with the GIS specialist to ensure that the appropriate GIS coordinate sources are used and that only federal land is included.

Created in July 2019 from a spreadsheet maintained by the Senior Planner responsible for land conservation. Maintained by the Planning GIS Specialist as properties are added to conservation programs.

This map shows the geography and density of employment in Ferndale.

This data set consists of digital mapping data describing polygon geometry and linked attribute data for BLM Colorado's Quarterly Oil and Gas Lease Sale Parcels in the State of Colorado, BLM. The polygons in this data set were generated using the legal land descriptions of the identified Oil and Gas Lease Sale parcels and the Geographic Coordinate Data Base (GCDB) survey grid to produce a mapped layer. In some cases an error file may come with this data set. The error file shows polygons that did not process through the automated procedure. In other cases the BLM GIS Specialist may have manually completed identified error polygons. Mapped parcels should be checked against the legal land description for the sale parcels in the sale notice to verify and validate data. These data are consolidated from Field Office inputs.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for gis in the U.S.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. The CURE vegetation mapping project area was divided into 11,133 polygons and 42 map classes. A total of 10,520 map polygons represent 27 natural and semi-natural vegetation map classes. Fifteen land use map classes describe 613 other polygons within the mapping area. Average polygon size across all map classes is 4.4 ha (10.8 acres). The mapping component of the CURE project used a combination of methods to interpret and delineate vegetation polygons. Initial line work was prepared by USBOR photointerpreters who delineated the most contrasting signatures, e.g., water bodies, exposed shoreline, unvegetated geology, land use types, and vegetation at the physiognomic level. The project photo interpreter used this baseline mapping and refined it by examining digital orthophotos in stereo. The stereo photography was used as needed to distinguish fine scale vegetation patterns. Ancillary datasets including plot and observation point data and classification and local descriptions of plant associations were used by the photointerpreter to assist with map class definitions and guide manual delineations. Polygons were drawn on Mylar overlays of printed orthophotos that were later scanned, or were drawn digitally on a computer screen. Heads-up digitizing consisted of delineating map class polygons on an electronic version of the digital orthophotos at a computer workstation. Digitizing was performed using vector editing in ArcGIS. The line work was refined and finalized by the SEUG GIS Specialist and the map class and other descriptive attributes for each polygon were assigned. The recreation area and the environs were interpreted and mapped to the same level of detail.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for forest engineering (gis) in the U.S.

This information is being published so that can be used for research projects / ransparency, and recognised that the data is both detailed, and requires specialist GIS software for analysis Between 23 November 2023 and 4 February 2024, we held a significant public consultation looking at 10 policies to deliver a more sustainable future for York’s transport. There are ten policy areas which we believe will help us deliver our ambitions for transport:

This data set consists of digital mapping data describing polygon geometry and linked attribute data for BLM Colorado's Quarterly Oil and Gas Lease Sale Parcels in the State of Colorado, BLM. The polygons in this data set were generated using the legal land descriptions of the identified Oil and Gas Lease Sale parcels and the Geographic Coordinate Data Base (GCDB) survey grid to produce a mapped layer. In some cases an error file may come with this data set. The error file shows polygons that did not process through the automated procedure. In other cases the BLM GIS Specialist may have manually completed identified error polygons. Mapped parcels should be checked against the legal land description for the sale parcels in the sale notice to verify and validate data. These data are consolidated from Field Office inputs.