Data Source: The primary data source used for this analysis are point-level business establishment data from InfoUSA. This commercial database produced by InfoGroup provides a comprehensive list of businesses in the SCAG region, including their industrial classification, number of employees, and several additional fields. Data have been post-processed for accuracy by SCAG staff and have an effective date of 2016. Locally-weighted regression: First, the SCAG region is overlaid with a grid, or fishnet, of 1km, 2km, and ½-km per cell. At the 1km cell size, there are 16,959 cells covering the SCAG region. Using the Spatial Join feature in ArcGIS, a sum total of business establishments and total employees (i.e., not separated by industrial classification) were joined to each grid cell. Note that since cells are of a standard size, the employment total in a cell is the equivalent of the employment density. A locally-weighted regression (LWR) procedure was developed using the R Statistical Software package in order to identify subcenters.The below procedure is described for 1km grid cells, but was repeated for 2km and 1/2km cells. Identify local maxima candidates.Using R’s lwr package, each cell’s 120 nearest neighbors, corresponding to roughly 5.5 km in each direction, was explored to identify high outliers or local maxima based on the total employment field. Cells with a z-score of above 2.58 were considered local maxima candidates.Identify local maxima. LWR can result in local maxima existing within close proximity. This step used a .dbf-format spatial weights matrix (knn=120 nearest neighbors) to identify only cells which are higher than all of their 120 nearest neighbors. At the 1km scale, 84 local maxima were found, which will form the “peak” of each individual subcenter. Search adjacent cells to include as part of each subcenter. In order to find which cells also are part of each local maximum’s subcenter, we use a queen (adjacency) contiguity matrix to search adjacent cells up to 120 nearest neighbors, adding cells if they are also greater than the average density in their neighborhood. A total of 695 cells comprise subcenters at the 1km scale. A video from Kane et al. (2018) demonstrates the above aspects of the methodology (please refer to 0:35 through 2:35 of https://youtu.be/ylTWnvCCO54), with several minor differences which result in a different final map of subcenters: different years and slightly different post-processing steps for InfoUSAdata, video study covers 5-county region (Imperial county not included), and limited to 1km scale subcenters.A challenge arises in that using 1km grid cells may fail to identify the correct local maximum for a particularly large employment center whose experience of high density occurs over a larger area. The process was repeated at a 2km scale, resulting in 54 “coarse scaled” subcenters. Similarly, some centers may exist with a particularly tightly-packed area of dense employment which is not detectable at the medium, 1km scale. The process was repeated again with ½-km grid cells, resulting in 95 “fine scaled” subcenters. In many instances, boundaries of fine, medium, and coarse scaled subcenters were similar, but differences existed. The next step was to qualitatively comparing results at each scale to create the final map of 72 job centers across the region. Most centers are medium scale, but some known areas of especially employment density were better captured at the 2km scale while . Giuliano and Small’s (1991) “ten jobs per acre” threshold was used as a rough guide to test for reasonableness when choosing a larger or smaller scale. For example, in some instances, a 1km scale included much additional land which reduced job density well below 10 jobs per acre. In this instance, an overlapping or nearby 1/2km scaled center provided a better reflection of the local employment peak. Ultimately, the goal was to identify areas where job density is distinct from nearby areas. Finally, in order to serve land use and travel demand modeling purposes for Connect SoCal, job centers were joined to their nearest TAZ boundaries. While the identification mechanism described above uses a combination of point and grid cell boundaries, the job centers boundaries expressed in this layer, and used for Connect SoCal purposes, are built from TAZ geographies. In Connect SoCal, job centers are associated with one of three strategies: focused growth, coworking space, or parking/AVR.Data Field/Value description:name: Name of job center based on name of local jurisdiction(s) or other discernable feature.Focused_Gr: Indicates whether job center was used for the 2020 RTP/SCS Focused Growth strategy, 1: center was used, 0: center was not used.Cowork: Indicates whether job center was used for the 2020 RTP/SCS Co-working space strategy, 1: center was used, 0: center was not used.Park_AVR: Indicates whether job center was used for the 2020 RTP/SCS parking and average vehicle ridership (AVR) strategies, 1: center was used, 0: center was not used. nTAZ: number of Transportation Analysis Zones (TAZs) which comprise this center.emp16: Estimated number of workers within job center boundaries based on 2016 InfoUSA point-based business establishment data. Values are rounded to the nearest 1000. acres: Land area within job center boundaries based on grid-based identification mechanism (i.e., not based on TAZ boundaries shown). Values are rounded to the nearest 100.

The professional map services market is experiencing robust growth, projected to reach $625.6 million in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 7.0% from 2025 to 2033. This expansion is fueled by several key factors. The increasing adoption of location-based services across diverse sectors like utilities, construction, transportation, and government is a primary driver. Advanced mapping technologies, including AI-powered mapping and real-time data integration, are enhancing the accuracy and functionality of map services, leading to increased demand. Furthermore, the growing need for precise mapping data for infrastructure planning, urban development, and disaster management is significantly contributing to market growth. The market segmentation reveals a strong reliance on consulting and advisory services, alongside significant demand for deployment and integration, and ongoing support and maintenance. Competition is fierce, with established players like Google, TomTom, and Esri vying for market share alongside emerging innovative companies specializing in niche applications. Geographic expansion is also a key aspect, with North America and Europe currently holding significant market share, but Asia-Pacific exhibiting rapid growth potential driven by infrastructure development and increasing technological adoption. The market's future trajectory appears bright, anticipating continued growth driven by technological advancements and expanding application areas. The integration of Internet of Things (IoT) data into mapping solutions presents a substantial opportunity for market expansion. The increasing reliance on autonomous vehicles and drone technology will further fuel demand for highly accurate and detailed mapping data. However, challenges remain, including data security concerns and the need for robust data management infrastructure. The competitive landscape necessitates continuous innovation and strategic partnerships to secure market share and capitalize on emerging opportunities. The ongoing development of standardized mapping data formats and protocols will play a crucial role in facilitating market growth and interoperability.

The General Order detailing RPD's performance assessment and career development system.

This dataset highlights localities that currently require the lower job creation threshold (25+ new jobs) to qualify for the Major Business Facility Job Tax Credit (MBFJTC).

MBFJTC-qualified companies locating or expanding anywhere in Virginia are eligible to receive a $1,000 income tax credit for each new full-time job created over a threshold number of jobs. Companies locating in an economically distressed locality or an Enterprise Zone are required to meet a 25-job threshold; all other locations have a 50-job threshold. For this tax credit, a locality qualifies as economically distressed if its unemployment rate for the preceding year is at least 0.5 percent higher than the average statewide unemployment rate.This data is updated in May/June of each year.Note: Unemployment rates for each county are determined by the Virginia Employment Commission. Additional Resources:Virginia's Guide to Business Incentives

This layer shows employment data in Tucson by neighborhood, aggregated from block level data for 2019. For questions, contact GIS_IT@tucsonaz.gov. The data shown is from Esri's 2019 Updated Demographic estimates.Esri's U.S. Updated Demographic (2019/2024) Data - Population, age, income, sex, race, home value, and marital status are among the variables included in the database. Each year, Esri's Data Development team employs its proven methodologies to update more than 2,000 demographic variables for a variety of U.S. geographies.Additional Esri Resources:Esri DemographicsU.S. 2019/2024 Esri Updated DemographicsEssential demographic vocabularyPermitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

This monthly publication includes statistics and data on employment, job growth, home sales, and commercial property in Anne Arundel County released in June of 2024.

[Metadata] Mental Health Professional Shortage Areas as of April 2024. Source - Hawaii State Department of Health. Description: Designation of Health Professional Shortage Areas for Mental Health. See also Health Professional Shortage Areas for Dental Health and Primary Care. A Health Professional Shortage Area (HPSA) means any of the following which has a shortage of health professionals: (a) an urban or rural area which is a rational service area for the delivery of health services, (b) a population group, or (c) a public or nonprofit private medical facility. HPSAs are divided into three major categories according to the type of health professional shortage: primary care, dental or mental health HPSAs. For more information about HPSA’s, visit the Hawaii State Department of Health HPSA website at https://health.hawaii.gov/opcrh/home/health-professional-shortage-area-hpsa/. Hawaii Statewide GIS Program staff downloaded data from https://data.hrsa.gov/data/download?hmpgtitle=hmpg-hrsa-data April 2024. Projected to UTM Zone 4 NAD 83 HARN, and clipped to coastline. For additional information, please refer to summary metadata at https://files.hawaii.gov/dbedt/op/gis/data/hpsa.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.

Annual employment and wage data from 2001 to present for boroughs and census areas from Alaska Department of Labor and Workforce Development. FieldsResidentsAge16AndOverResidentsEmployedWagesLessThan5k: Number of residents making between $0 and $4,999 per yearWages5k_10k: Number of residents making between $5,000 and $9,999 per yearWages10k_20k: Number of residents making between $10,000 and $19,999 per yearWages20k_50k: Number of residents making between $20,000 and $49,999 per yearWagesGreaterThan50k: Number of residents making more than $50,000 per yearEmployedInPrivateSector: Number of residents who are employed in the private sectorPercentInPrivateSector: Percent of residents employed in the private sector (of residents employed)EmployedInStateGovt: Number of residents who are employed in state governmentPercentInStateGovt: Percent of residents employed in state government (of residents employed)EmployedFemales: Number of female residents who are employedEmployedMales: Number of male residents who are employed

Employment Protection Districts are economically viable industrial and employment-rich areas, having policies that prevent the conversion of industrial land to non-industrial uses. These are for areas in UNINCORPORATED Los Angeles County only.Please see Figure 14.1 and the the Economic Development Element of the Los Angeles County General Plan 2035 for more information. https://planning.lacounty.gov/generalplan/Source: L.A. County Dept. of Regional Planning (DRP) GIS Section; created November 5, 2015.NEED MORE FUNCTIONALITY? If you are looking for more layers or advanced tools and functionality, then try our suite of GIS Web Mapping Applications.

Employment and wages data for economic regions in Alaska. Includes historic data from 2001 to present.This data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: Alaska Local and Regional Information

This map shows areas where population and jobs growth will be concentrated in the District through the year 2045.

Covered employment for the growth areas, urban centers and villages, for the City of Seattle Comprehensive Plan. This is a stand alone table that includes non-spatial records.Covered employment is reported annually from the State of Washington QCEW data.The Washington State Employment Security Department, Quarterly Census of Employment and Wages (QCEW) is a federal/state cooperative program that measures employment and wages in industries covered by unemployment insurance. Data are available by industry and county and used to evaluate labor trends, monitor major industry developments and develop training programs.

Growth data for housing units and employment for the growth areas, urban centers and villages, for the City of Seattle Comprehensive Plan.Housing unit growth is reported quarterly from the city's permitting system while employment change is reported annually from the State of Washington QCEW data.

A special tabulation of employment data by the Puget Sound Regional Council for monitoring employment goals in the City of Seattle 2035 Comprehensive Plan. Estimates are for the growth areas, urban centers and villages of the City of Seattle Comprehensive Plan.The comprehensive planning estimates are for "all jobs" minus the employment in the Construction/Resources sector. Employment reporting for the purposes of comparison to 2035 growth estimates are calculated as the covered employment reported from the Washington State Employment Security Department QCEW data plus an estimate of the remaining jobs not covered by unemployment insurance minus jobs in the construction / resources sector.This is a stand alone table that includes non-spatial records.

ABOUT THE CITY OF TEMPE EMPLOYEE SURVEY REPORTS DATASETThis data set includes the results from the Tempe Employee Survey, conducted every other year, to gather input from employees about issues in six major areas: professional development and career mobility; organizational support; supervisions and working environment; compensation and benefits; employee engagement; and peer relationships. Participation in the survey is voluntary and confidential. Employees are able to complete the survey during work hours or at home, with surveys directly returned to the vendor conducting the survey.PERFORMANCE MEASURESData collected in this survey applies directly to the following Performance Measures for the City of Tempe:1. Safe & Secure Communities1.11 Feeling Safe in City Facilities2. Strong Community Connections2.13 Employee Engagement2.25 Employee Work-Related NeedsThe City of Tempe Employee Survey was first conducted in 2016 and will occur every two years.Additional InformationSource: Employee SurveyContact (author): Aaron PetersonContact E-Mail (author): aaron_peterson@tempe.govContact (maintainer): Aaron PetersonContact E-Mail (maintainer): aaron_peterson@tempe.govData Source Type: PDFPreparation Method: NAPublish Frequency: BiennialPublish Method: Manual

The first step of the Bureau of Planning & Sustainability's Buildable Lands Inventory (BLI) model is used to identify parcels that are likely to redevelop in the City of Portland -- parcels that are either vacant or significantly underutilizing their allowed development capacity. FAR and building height limits are the primary limiting factor on development in employment, commercial, and high-density residential areas. In multi-family and single-family residential areas, capacities are determined by the allowed number of residential units.This portion of the BLI modeling process consists of 3 basic steps: 1) calculate existing building square footages and allowed development limits (in terms of building square footage or number of residential units); 2) identify parcels using significantly less than their allowed development capacity (using less than 20% of available capacity, not including any development bonuses or incentives); 3) apply development constraints to these parcels and calculate remaining capacity in terms of building square footage, allowed number of residential units, and allowed number of jobs.The attached graphics illustrate the process in more detail.

This featureclass is the result of that analysis. It is used to determine the total supply of residential and employment land, which is an input into subsequent steps of the BLI model, which allocates forecasted units and jobs to different areas of the City.--Additional Information: Category: Development Purpose: To quantify the existing development capacity within Portland under current and proposed land use regulations; to identify likely redevelopment scenarios and prospective clusters of future development activity by identifying sites that are significantly underutilizing their allowed development capacity; to generate development capacity statistics for different areas of the City to highlight the differences in terms of existing and allowed development capacity; and to serve as a basis for predicting residential and employment allocation based on regional growth forecasts. Update Frequency: As Needed-- Metadata Link: https://www.portlandmaps.com/metadata/index.cfm?&action=DisplayLayer&LayerID=52965

This dataset represents all future planned employment areas within the region.This dataset was compiled for the Edmonton Metropolitan Region Growth Plan which came into effect on October 26, 2017.

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Professional Growth Management - Provide analytics on hiring and retention.

Alaska Regional Development Organizations (ARDORs), their contact information, and their Comprehensive Economic Development Strategies (CEDS).The mission of the ARDORs Program is to encourage the formation of regional development organizations to prepare and implement regional development strategies (Alaska Statute 44.33.896). Through regional development strategies, local knowledge, and coordinated implementation, ARDORs champion economic development planning for Alaska’s regions and communities by leveraging baseline support provided by the State of Alaska.ARDORs develop customized work plans that contain goals, objectives, and strategies for addressing regional economic development needs including: Facilitating development of a healthy regional economy that results in sustainable business growth, new business investment, and economic diversification. Identifying and working to eliminate regional economic development barriers. Developing and implementing a comprehensive economic development strategy. Coordinating regional planning efforts that result in new employment and business opportunities. Working to enable multiple communities to collaborate and pool limited resources. Strengthening partnerships with public, private, and non-government organizations. Providing technical assistance to encourage business startup, retention, and expansion.Source: Alaska Department of Commerce, Community & Economic Development

Resilience—the keen ability of people to adapt to changing physical environments—is essential in today's world of unexpected changes.Resilient Communities across Geographies edited by Sheila Lakshmi Steinberg and Steven J. Steinberg focuses on how applying GIS to environmental and socio-economic challenges for analysis and planning helps make communities more resilient.A hybrid of theory and action, Resilient Communities across Geographies uses an interdisciplinary approach to explore resilience studied by experts in geography, social sciences, planning, landscape architecture, urban and rural sociology, economics, migration, community development, meteorology, oceanography, and other fields. Geographies covered include urban and rural, coastal and mountainous, indigenous areas in the United State and Australia, and more. Geographical Information Systems (GIS) is the unifying tool that helped researchers understand resilience.This book shows how GIS:integrates quantitative, qualitative, and spatial data to produce a holistic view of a need for resilience.serves as a valuable tool to capture and integrate knowledge of local people, places, and resources.allows us to visualize data clearly as portrayed in a real-time map or spatial dashboard, thus leading to opportunities to make decisions.lets us see patterns and communicate what the data means.helps us see what resources they have and where they are located.provides a big vision for action by layering valuable pieces of information together to see where gaps are located, where action is needed, or how policies can be instituted to manage and improve community resilience.Resilience is not only an ideal; it is something that people and communities can actively work to achieve through intelligent planning and assessment. The stories shared by the contributing authors in Resilient Communities across Geographies help readers to develop an expanded sense of the power of GIS to address the difficult problems we collectively face in an ever-changing world.AUDIENCEProfessional and scholarly. Higher education.AUTHOR BIOSSheila Lakshmi Steinberg is a professor of Social and Environmental Sciences at Brandman University and Chair of the GIS Committee, where she leads the university to incorporate GIS across the curriculum. Her research interests include interdisciplinary research methods, culture, community, environmental sociology, geospatial approaches, ethnicity, health policy, and teaching pedagogy.Steven J. Steinberg is the Geographic Information Officer for the County of Los Angeles, California. Throughout his career, he has taught GIS as a professor of geospatial sciences for the California State University and, since 2011, has worked as a geospatial scientist in the public sector, applying GIS across a wide range of both environmental and human contexts.Pub Date: Print: 11/24/2020 Digital: 10/27/2020ISBN: Print: 9781589484818 Digital: 9781589484825Price: Print: $49.99 USD Digital: $49.99 USDPages: 350 Trim: 7.5 x 9.25 in.Table of ContentsPrefaceChapter 1. Conceptualizing spatial resilience Dr. Sheila Steinberg and Dr Steven J. SteinbergChapter 2. Resilience in coastal regions: the case of Georgia, USAChapter 3. Building resilient regions: Spatial analysis as a tool for ecosystem-based climate adaptationChapter 4. The mouth of the Columbia River: USACE, GIS and resilience in a dynamic coastal systemChapter 5. Urban resilience: Neighborhood complexity and the importance of social connectivityChapter 6. Mapping Indigenous LAChapter 7. Indigenous Martu knowledge: Mapping place through song and storyChapter 8. Developing resiliency through place-based inquiry in CanadaChapter 9. Engaging Youth in Spatial Modes of Thought toward Social and Environmental ResilienceChapter 10. Health, Place, and Space: Public Participation GIS for Rural Community PowerChapter 11. Best Practices for Using Local KnowledgeContributorsIndex