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

The U.S. Army Corps of Engineers Geospatial Open Data provides shared and trusted USACE geospatial data, services and applications for use by our partner agencies and the public.

"Hi, I'm Adam Burke. I am the Lead Specialist Advisor for Geospatial at Natural Resources Wales. Read on to find out more about the work I do and how I got here."I graduated from Aberystwyth University with a BSc in Physical Geography and a MSc in Geographic Information Systems.

This dataset is a compilation of address point data for the City of Tempe. The dataset contains a point location, the official address (as defined by The Building Safety Division of Community Development) for all occupiable units and any other official addresses in the City. There are several additional attributes that may be populated for an address, but they may not be populated for every address. Contact: Lynn Flaaen-Hanna, Development Services Specialist Contact E-mail Link: Map that Lets You Explore and Export Address Data Data Source: The initial dataset was created by combining several datasets and then reviewing the information to remove duplicates and identify errors. This published dataset is the system of record for Tempe addresses going forward, with the address information being created and maintained by The Building Safety Division of Community Development.Data Source Type: ESRI ArcGIS Enterprise GeodatabasePreparation Method: N/APublish Frequency: WeeklyPublish Method: AutomaticData Dictionary

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

This dataset contains a list of useful links to 3rd party GIS open resources, as well as to YorkView, which is City of York Council's public mapping platform.

I graduated from Durham University with a Mathematics degree (MMath) and began my career working for the engineering consultancy Mott MacDonald as a Graduate Transport Modeller.I spent three years in this role, developing simulation models for transport systems at the local and national level. During this time I began using GIS to aid in designing network structures and displaying model outputs.

Dataset contains training material on using open source Geographic Information Systems (GIS) to improve protected area planning and management from workshops that were conducted on February 19-21 and October 6-7, 2020. Specifically, the dataset contains lectures on GIS fundamentals, QGIS 3.x, and global positioning system (GPS), as well as country-specific datasets and a workbook containing exercises for viewing data, editing/creating datasets, and creating map products in QGIS. Supplemental videos that narrate a step-by-step recap and overview of these processes are found in the Related Content section of this dataset.

Funding for this workshop and material was funded by the Biodiversity and Protected Areas Management (BIOPAMA) programme. The BIOPAMA programme is an initiative of the Organisation of African, Caribbean and Pacific (ACP) Group of States financed by the European Union's 11th European Development Fund. BIOPAMA is jointly implemented by the International Union for Conservation of Nature {IUCN) and the Joint Research Centre of the European Commission (EC-JRC). In the Pacific region, BIOPAMA is implemented by IUCN's Oceania Regional Office (IUCN ORO) in partnership with the Secretariat of the Pacific Regional Environment Programme (SPREP). The overall objective of the BIOPAMA programme is to contribute to improving the long-term conservation and sustainable use of biodiversity and natural resources in the Pacific ACP region in protected areas and surrounding communities through better use and monitoring of information and capacity development on management and governance.

Addy PopeHigher Education Manager - Esri UKStill think I am a glaciologistGIS consultant GIS EducationDidn't actually do any GIS as an undergrad.

The Digital Geomorphic-GIS Map of Gulf Islands National Seashore (5-meter accuracy and 1-foot resolution 2006-2007 mapping), Mississippi and Florida is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (guis_geomorphology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (guis_geomorphology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (guis_geomorphology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (guis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (guis_geomorphology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (guis_geomorphology_metadata_faq.pdf). Please read the guis_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (guis_geomorphology_metadata.txt or guis_geomorphology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:26,000 and United States National Map Accuracy Standards features are within (horizontally) 13.2 meters or 43.3 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

Hi, I'm Patrick,I initially pursued an undergraduate degree in Computer Science because I wanted to make video games; however, after taking an Environmental Science course, I wanted to see if there was a way I could study both. This led me to GIS and I made that my specialism, doing a Masters and later PhD on the subject.

Dataset description: This dataset contains the information needed to replicate the results presented in the article “Optimizing recruitment in PPGIS – is it worth the time and the costs?”. The data were collected as part of a study investigating recruitment strategies for a large-scale online public participation GIS (PPGIS) platform in coastal areas of Northern Norway. To investigate different recruitment strategies, we reviewed previous environmental PPGIS studies using random sampling and methods to increase response rates. We compared the attained results with our large-scale PPGIS in Northern Norway, where we used both random and volunteer (traditional and social media) sampling. The dataset includes response rates for the 5% of the population (13 regions in Northern Norway) recruited by mail to participate in an online PPGIS survey, response rates from volunteers recruited through traditional and social media, synthetic demographic data, and the code necessary for processing demographic data to obtain the results presented in the article. Original demographic data is not shared due to privacy legislation. We furthermore calculated time spent and costs used for recruiting both randomly sampled persons and volunteers. Article abstract: Public participation GIS surveys use both random and volunteer sampling to recruit people to participate in a self-administered mapping exercise online. From random sampling designs, the participation rate is known to be relatively low, and biased to specific segments (e.g., mid-aged, educated men). Volunteer sampling provides the opportunity to reach a large crowd at reasonable costs, but generally suffers from unknown sampling biases and lower data quality. The low participation rates and the quality of mapping question the validity and generalizability of the results, limiting its use as a democracy tool for enhancing participation in development and planning. We therefore asked: How can we increase participation in online PPGIS surveys? Is it worth the time and the costs? We reviewed environmentally related, online PPGIS surveys (N=51) and analyzed the sampling biases and recruitment strategies utilized in a large scale online PPGIS platform in coastal areas of Northern Norway using both random sampling (16978 invited participants) and volunteer sampling. We found the time, effort, and costs spent to increase participation rates to yield meager results. We discuss the time and cost efficiency of different recruitment methods, as well as the implications of the low participation levels notwithstanding the recruitment methods used.

Building information modeling (BIM) allows representation of detailed information regarding building elements while geographic information system (GIS) allows representation of spatial information about buildings and their surroundings. Overlapping these domains will combine their individual features and provide support to important activities such as building emergency response, construction site safety, construction supply chain management, and sustainable urban design. Interoperability through open data standards is one method of connecting software tools from BIM and GIS domains. However, no single open data standard available today can support all information from the two domains. As a result, many researchers have been working to overlap or connect different open data standards to enhance interoperability. An overview of these studies will help identify the different approaches used and determine the approach with the most potential to enhance interoperability. This paper adopted a strong definition of interoperability using information technology (IT) based standard documents. Based on this definition, previous approaches towards improving interoperability between BIM and GIS applications through open data standards were studied. The result shows previous approaches have implemented data conversion, data integration, and linked data approaches. Between these methods, linked data emerged as having the most potential to connect open data standards and expand interoperability between BIM and GIS applications because it allows information exchange without editing the original data. The paper also identifies the main challenges in implementing linked data technologies for interoperability and provides directions for future research.

The data layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s Longitudinal Employer-Household Dynamics (LEHD) to show change in job characteristics over time, including total number of jobs, worker age, sectors and earnings, from 2010-2019, by various geographies for the state of Georgia.Data manifest: https://opendata.atlantaregional.com/datasets/employment-and-job-flows-2010-2019-manifest/explore

The Gravity model toolbox, a programmed ArcGIS tool to map and prioritize the potential corridors of urban green space.

This dataset includes mapping data to track the socio-economic metrics associated with a number of projects funded through the Hurricane Sandy Coastal Resiliency Program. Project locations are found in Delaware, Massachusetts, New Jersey, Maryland, and New York. Data was collected from 2017 to 2020. The map data shows agricultural and cropland data, the area of influence at each site, the flooded areas around project sites, area with reduced flood depth because of the project, buildings and their position above and below water, concentrated animal feeding operations, emergency facilities, schools, correction facilities, natural gas processing plants, waste treatment plants, transportation data including data came from a variety of sources, including railways and roads, and watersheds. Data sources include the Department of Homeland Society, U.S. Census Bureau, Pipeline and Hazardous Materials Safety Administration, and U.S. Government open data.

Half-mile access sheds to open access open space in the conservation areas dataset built for CA Nature. Each has been intersected to a city and county dataset to allow summarization of demographics. These were then enriched using ESRI's geoenrichment services to provide select demographics. Three layers are included:1. Half-mile access sheds from open access areas considered 30x30 Conservation Areas (GAP Code 1 and 2)2. Half-mile access sheds from open access areas in the Conservation Areas dataset (GAP Codes 1, 2, 3, 4)3. All city and county areas to provide baseline demographics for comparison. Demographic variables include:PopulationAge DistributionEducational AttainmentHousing Unit OccupancyHispanic or Latino OriginRaceHousehold income

Dataset contains training material on using open source Geographic Information Systems (GIS) to improve protected area planning and management from a workshop that was conducted on August 17-21, 2020. Specifically, the dataset contains lectures on GIS fundamentals, QGIS 3.x, and global positioning system (GPS), as well as country-specific datasets and a workbook containing exercises for viewing data, editing/creating datasets, and creating map products in QGIS. Supplemental videos that narrate a step-by-step recap and overview of these processes are found in the Related Content section of this dataset.

Funding for this workshop and material was funded by the Biodiversity and Protected Areas Management (BIOPAMA) programme. The BIOPAMA programme is an initiative of the Organisation of African, Caribbean and Pacific (ACP) Group of States financed by the European Union's 11th European Development Fund. BIOPAMA is jointly implemented by the International Union for Conservation of Nature {IUCN) and the Joint Research Centre of the European Commission (EC-JRC). In the Pacific region, BIOPAMA is implemented by IUCN's Oceania Regional Office (IUCN ORO) in partnership with the Secretariat of the Pacific Regional Environment Programme (SPREP). The overall objective of the BIOPAMA programme is to contribute to improving the long-term conservation and sustainable use of biodiversity and natural resources in the Pacific ACP region in protected areas and surrounding communities through better use and monitoring of information and capacity development on management and governance.

Data record audit of feature classes within the MD iMAP spatial database.

The Raster Based GIS Coverage of Mexican Population is a gridded coverage (1 x 1 km) of Mexican population. The data were converted from vector into raster. The population figures were derived based on available point data (the population of known localities - 30,000 in all). Cell values were derived using a weighted moving average function (Burrough, 1986), and then calculated based on known population by state. The result from this conversion is a coverage whose population data is based on square grid cells rather than a series of vectors. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Instituto Nacional de Estadistica Geografia e Informatica (INEGI).