Snapshot img

GIS Market Size 2025-2029

The GIS market size is forecast to increase by USD 24.07 billion at a CAGR of 20.3% between 2024 and 2029.

The Global Geographic Information System (GIS) market is experiencing significant growth due to the integration of Building Information Modeling (BIM) software and GIS, enabling more accurate and efficient construction projects. The increasing adoption of GIS solutions in precision farming for soil and water management is another key trend, with farmers utilizing sensors, GPS, and satellite data to optimize fertilizer usage and crop yields. However, challenges persist, such as the lack of proper planning leading to implementation failures of GIS solutions. In the realm of smart cities, GIS plays a crucial role in managing data from various sources, including LIDAR, computer-aided design, and digital twin technologies. Additionally, public safety and insurance industries are leveraging GIS for server-based data analysis, while smartphones and antennas facilitate real-time data collection. Amidst this digital transformation, ensuring data security and privacy becomes paramount, making it a critical consideration for market participants.

What will be the Size of the GIS Market During the Forecast Period?

Request Free Sample

The Global Geographic Information System (GIS) market encompasses a range of software solutions and hardware components used to capture, manage, analyze, and visualize geospatial data. Key industries driving market growth include transportation, smart city planning, green buildings, architecture and construction, utilities, oil and gas, agriculture, and urbanization. GIS technology plays a pivotal role in various applications such as 4D GIS software for infrastructure project management, augmented reality platforms for enhanced visualization, and LIDAR and GNSS/GPS antenna for accurate location data collection. Cloud technology is transforming the GIS landscape by enabling real-time data access and collaboration. The transportation sector is leveraging GIS for route optimization, asset management, and predictive maintenance.
Urbanization and population growth are fueling the demand for GIS in city planning and disaster management. Additionally, GIS is increasingly being adopted in sectors like agriculture for precision farming and soil mapping, and in the construction industry for Building Information Modeling (BIM). The market is also witnessing the emergence of innovative applications in areas such as video games and natural disasters risk assessment. Mobile devices are further expanding the reach of GIS, making it accessible to a wider audience. Overall, the market is poised for significant growth, driven by the increasing need for data-driven decision-making and the integration of geospatial technology into various industries.

How is this GIS Industry segmented and which is the largest segment?

The gis industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Software
  Data
  Services


Type

  Telematics and navigation
  Mapping
  Surveying
  Location-based services


Device

  Desktop
  Mobile


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France


  APAC

    China
    Japan
    South Korea


  South America

    Brazil


  Middle East and Africa

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.

The market encompasses desktop, mobile, cloud, and server software solutions, catering to various industries. Open-source software with limited features poses a challenge due to the prevalence of counterfeit products. Yet, the market witnesses an emerging trend toward cloud-based GIS software adoption. However, standardization and interoperability concerns hinder widespread adoption. Geospatial technology is utilized extensively in sectors such as Transportation, Utilities, Oil and Gas, Agriculture, and Urbanization, driven by population growth, urban planning, and sustainable development. Key applications include smart city planning, green buildings, BIM, 4D GIS software, augmented reality platforms, GIS collectors, LIDAR, and GNSS/GPS antennas. Cloud technology, mobile devices, and satellite imaging are critical enablers.

Get a glance at the GIS Industry report of share of various segments Request Free Sample

The software segment was valued at USD 5.06 billion in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 38% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during th

The dataset includes polygons representing the location and attributes of Central Employment Area (CEA). The CEA is the core area of the District of Columbia where the greatest concentration of employment in the city and region is encouraged, created as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies. Jurisdictions were identified from public records (map and written description created by the National Capital Planning Commission) and heads-up digitized from the 1995 orthophotographs.

Snapshot img

What is the GIS In Utility Industry Market Size?

The GIS market in the utility industry size is forecast to increase by USD 3.55 billion at a CAGR of 19.8% between 2023 and 2028. Market expansion hinges on various factors, such as the rising adoption of Geographic Information System (GIS) solutions in the utility sector, the convergence of GIS with Building Information Modeling, and the fusion of Augmented Reality with GIS technology. These elements collectively drive market growth, reflecting advancements in spatial data analytics and technological convergence. The increased adoption of GIS solutions in the utility industry underscores the importance of geospatial data in optimizing infrastructure management. Simultaneously, the integration of GIS with BIM signifies the synergy between spatial and building information for enhanced project planning and management. Additionally, the integration of AR with GIS technology highlights the potential for interactive and interactive visualization experiences in spatial data analysis. Thus, the interplay of these factors delineates the landscape for the anticipated expansion of the market catering to GIS and related technologies.

What will be the size of Market during the forecast period?

Request Free GIS In Utility Industry Market Sample

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019 - 2023 for the following segments.

Product

  Software
  Data
  Services


Deployment

  On-premises
  Cloud


Geography

  North America

    Canada
    US


  Europe

    Germany
    France


  APAC

    China
    India
    Japan


  Middle East and Africa



  South America

    Brazil

Which is the largest segment driving market growth?

The software segment is estimated to witness significant growth during the forecast period. In the utility industry, the spatial context of geographic information systems (GIS) plays a pivotal role in site selection, land acquisition, planning, designing, visualizing, building, and project management. Utilities, including electricity, gas, water, and telecommunications providers, leverage GIS software to efficiently manage their assets and infrastructure. This technology enables the collection, management, analysis, and visualization of geospatial data, derived from satellite imaging, aerial photography, remote sensors, and artificial intelligence. Geospatial AI, sensor technology, and digital reality solutions are integral components of GIS, enhancing capabilities for smart city planning, urban planning, water management, mapping systems, grid modernization, transportation, and green buildings.

Get a glance at the market share of various regions. Download the PDF Sample

The software segment was valued at USD 541.50 million in 2018. Moreover, the geospatial industry continues to evolve, with startups and software solutions driving innovation in hardware, smart city planning, land use management, smart infrastructure planning, and smart utilities. GIS solutions facilitate 4D visualization, enabling stakeholders to overcome geospatial data barriers and make informed decisions. The utility industry's reliance on GIS extends to building information modeling, augmented reality, and smart urban planning, ultimately contributing to the growth of the geospatial technology market.

Which region is leading the market?

For more insights on the market share of various regions, Request Free Sample

North America is estimated to contribute 37% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

How do company ranking index and market positioning come to your aid?

Companies are implementing various strategies, such as strategic alliances, partnerships, mergers and acquisitions, geographical expansion, and product/service launches, to enhance their presence in the market.

AABSyS IT Pvt. Ltd. - The company offers GIS solutions such as remote sensing and computer aided design and drafting solutions for electric and gas utility.

Technavio provides the ranking index for the top 20 companies along with insights on the market positioning of:

AABSyS IT Pvt. Ltd.
Autodesk Inc.
Avineon Inc.
Bentley Systems Inc.
Blue Marble Geographics
Cadcorp Ltd.
Caliper Corp.
Environmental Systems Research Institute Inc.
General Electric Co.
Hexagon AB
Mapbox Inc.
Maxar Technologies Inc.
Mobile GIS Services Ltd.
NV5 Global Inc.
Orbital Insight Inc.
Pitney Bowes Inc.
Schneider Electric SE
SuperMap Software Co. Ltd.
Trimble Inc.
VertiGIS Ltd.

Explore our company rankings and market positioning. Request Free Sample

How can Technavio assist you in ma

I’d love to begin by saying that I have not “arrived” as I believe I am still on a journey of self-discovery. I have heard people say that they find my journey quite interesting and I hope my story inspires someone out there.I had my first encounter with Geographic Information System (GIS) in the third year of my undergraduate study in Geography at the University of Ibadan, Oyo State Nigeria. I was opportune to be introduced to the essentials of GIS by one of the prominent Environmental and Urban Geographers in person of Dr O.J Taiwo. Even though the whole syllabus and teaching sounded abstract to me due to the little exposure to a practical hands-on approach to GIS software, I developed a keen interest in the theoretical learning and I ended up scoring 70% in my final course exam.

Data Overview WageScape's US job listings dataset offers real-time, forward-looking insights into the American labor market. Covering millions of job postings data from various industries and locations, it supports workforce analytics, economic forecasting, and strategic planning, enabling businesses to make data-driven decisions with salary data, skill taxonomy data, and comprehensive company data.

Data Enrichment The dataset includes enriched recruiting data with industry codes, title normalization, company normalization, geographic parsing, firmographic info, and compensation data, enhancing usability and accuracy with precise skill taxonomy data.

Main Attributes • Job Titles: Across various sectors and industries, supported by detailed job postings data. • Job Descriptions: Detailed roles and requirements based on recruiting data. • Salary Data: Expected pay information, sourced from comprehensive salary data. • Locations: From state-level to city-specific details, offering insights into company data. • Company Information: Name, size, revenue, and industry sectors, providing context to company data. • Posting Dates: Timeline of market activity reflecting trends in job postings data. • Job Requirements: Skills, education, and experience needed, informed by skill taxonomy data.

Coverage • Industries: Technology, healthcare, finance, manufacturing, retail, and more, analyzed through company data. • Geographical Reach: National coverage, including metropolitan areas, regional hubs, and smaller towns, enriched with job postings data.

Scale and Quality • Data Volume: Over 4 million job postings monthly, providing extensive recruiting data. • Hiring Organizations: Data from 6+ million organizations, supported by reliable company data. • High Precision: Rigorous validation for accuracy, ensuring dependable salary data.

Use Cases • Workforce Analytics: Analyze trends and dynamics using job postings data and recruiting data for HR decisions. • Economic Forecasting: Predict economic and labor market shifts based on company data and job postings data. • Talent Acquisition: Improve recruitment strategies with detailed recruiting data and insights from skill taxonomy data. • Market Research: Understand industry trends using enriched company data. • Strategic Planning: Inform long-term business strategies with comprehensive job postings data and salary data.

Data Accessibility • Delivery Channels: Available through Data-as-a-Service (DaaS), with access to recruiting data and job postings data. • Customizable Reports: Tailored to specific business needs, incorporating skill taxonomy data. • Integration: Seamless integration into existing systems, supporting detailed company data analysis.

Key Benefits • Real-Time Insights: Up-to-date job information for timely decisions based on job postings data. • Forward-Looking Data: Predict future labor market trends with enriched company data and salary data. • Comprehensive Coverage: Extensive industry and geographic data, including detailed recruiting data. • High Quality and Scale: Millions of postings monthly for robust analysis, supported by skill taxonomy data. • Actionable Insights: Enhance job modeling and workforce strategies with high-quality company data.

Key Points WageScape's dataset is essential for businesses to understand the labor market deeply. With extensive coverage and high-quality company data, it empowers organizations to optimize workforce strategies and maintain a competitive edge, leveraging the latest job postings data, salary data, and skill taxonomy data.

Overview WageScape's global job market data provide real-time, forward-looking insights into the international labor market. Covering diverse industries and regions, it supports workforce analytics, economic forecasting, and strategic planning, leveraging LinkedIn data, company data, and employee data.

Main Attributes • Job Titles: Across various sectors. • Locations: Country to city-specific details. • Job Descriptions: Roles and requirements. • Company Information: Names and sectors. • Posting Dates: Timeline of activity.

Coverage • Industries: Technology, healthcare, finance, manufacturing, retail, and more. • Geographical Reach: 62 countries, from national to local levels.

Scale and Quality Indicators • Data Volume: Over 5 million job postings data monthly. • Hiring Organizations: 15+ million worldwide. • High Precision: Rigorous validation.

Use Cases • Workforce Analytics: Analyze trends for HR decisions. • Economic Forecasting: Predict global trends. • Talent Acquisition: Improve recruitment strategies. • Market Research: Understand labor trends. • Strategic Planning: Inform business strategies.

Data Accessibility • Delivery Channels: Data-as-a-Service (DaaS). • Customizable Reports: Tailored to needs. • Integration: Seamless integration into existing systems.

Key Benefits • Real-Time Insights: Timely job market data for informed decisions. • Forward-Looking Data: Predict future global labor trends. • Comprehensive Coverage: Extensive industry and geographic data. • High Quality and Scale: Millions of job postings data monthly for robust analysis. • Actionable Insights: Enhance job modeling and workforce strategies globally.

Key Points WageScape's dataset is essential for navigating the global labor market. With extensive coverage, high-quality data, and actionable insights, it empowers businesses to make informed decisions, optimize workforce strategies, and stay competitive.

This geodatabase of point, line and polygon features is an effort to consolidate all of the range improvement locations on BLM-managed land in Idaho into one database. Currently, the line feature class has some data for all of the BLM field offices except the Coeur d'Alene and Cottonwood field offices. Range improvements are structures intended to enhance rangeland resources, including wildlife, watershed, and livestock management. Examples of range improvements include water troughs, spring headboxes, culverts, fences, water pipelines, gates, wildlife guzzlers, artificial nest structures, reservoirs, developed springs, corrals, exclosures, etc. These structures were first tracked by the Bureau of Land Management (BLM) in the Job Documentation Report (JDR) System in the early 1960s, which was predominately a paper-based tracking system. In 1988 the JDRs were migrated into and replaced by the automated Range Improvement Project System (RIPS), and version 2.0 is currently being used today. It tracks inventory, status, objectives, treatment, maintenance cycle, maintenance inspection, monetary contributions and reporting. Not all range improvements are documented in the RIPS database; there may be some older range improvements that were built before the JDR tracking system was established. There also may be unauthorized projects that are not in RIPS. Official project files of paper maps, reports, NEPA documents, checklists, etc., document the status of each project and are physically kept in the office with management authority for that project area. In addition, project data is entered into the RIPS system to enable managers to access the data to track progress, run reports, analyze the data, etc. Before Geographic Information System technology most offices kept paper atlases or overlay systems that mapped the locations of the range improvements. The objective of this geodatabase is to migrate the location of historic range improvement projects into a GIS for geospatial use with other data and to centralize the range improvement data for the state. This data set is a work in progress and does not have all range improvement projects that are on BLM lands. Some field offices have not migrated their data into this database, and others are partially completed. New projects may have been built but have not been entered into the system. Historic or unauthorized projects may not have case files and are being mapped and documented as they are found. Many field offices are trying to verify the locations and status of range improvements with GPS, and locations may change or projects that have been abandoned or removed on the ground may be deleted. Attributes may be incomplete or inaccurate. This data was created using the standard for range improvements set forth in Idaho IM 2009-044, dated 6/30/2009. However, it does not have all of the fields the standard requires. Fields that are missing from the line feature class that are in the standard are: ALLOT_NO, MGMT_AGCY, ADMIN_ST, ADMIN_OFF, SRCE_AGCY, MAX_PDOP, MAX_HDOP, CORR_TYPE, RCVR_TYPE, GPS_TIME, UPDATE_STA, UNFILT_POS, FILT_POS, DATA_DICTI, GPS_LENGTH, GPS_3DLGTH, AVE_VERT_P, AVE_HORZ_P, WORST_VERT, WORST_HORZ and CONF_LEVEL. Several additional fields have been added that are not part of the standard: top_fence, btm_fence, admin_fo_line and year_checked. There is no National BLM standard for GIS range improvement data at this time. For more information contact us at blm_id_stateoffice@blm.gov.

In the US, the growing demand for precision medicine, particularly in oncology, continues to put pressure on the availability of genetic counselors to meet that demand. This is especially true in certain geographic locations due to the uneven distribution of genetic counselors throughout the US. To assess these disparities, access to genetic counselors of all specialties is explored by geography, cancer type, and social determinants of health. Geospatial technology was used to combine and analyze genetic counselor locations and cancer incidence at the county level across the US, with a particular focus on tumors associated with BRCA mutations including ovarian, pancreatic, prostate and breast. Access distributions were quantified, and associations with region, cancer type, and socioeconomic variables were investigated using correlational tests. Nationally, in 2020, there were 4,813 genetic counselors, or 1.49 genetic counselors per 100,000 people, varying between 0.17 to 5.7 per 100,000 at the state level. Seventy-one percent of U.S. residents live within a 30-minute drive-time to a genetic counselor. Drive-times, however, are not equally distributed across the country – while 82% of people in metropolitan areas are 30 minutes from a genetic counselor, only 6% of people in nonmetro areas live within 30 minutes’ drive time. There are statistically significant differences in access across geographical regions, socioeconomics and cancer types. Access to genetic counselors for cancer patients differs across groups, including regional, socioeconomic, and cancer type. These findings highlight areas of the country that may benefit from increased genetic counseling provider supply, by increasing the number of genetic counselors in a region or by expanding the use of telegenetics a term used to describe virtual genetic counseling consults that occur via videoconference. Policy intervention to allow genetic counselors to bill for their services may be an effective route for increasing availability of genetic counselors’ services However, genetic counselors in direct patient care settings also face other challenges such as salary, job satisfaction, job recognition, overwork/burnout, and appropriate administrative/clinical support, and addressing these issues should also be considered along with policy support. These results could support targeted policy reform and alternative service models to increase access to identified pockets of unmet need, such as telemedicine. Data and analysis are available to the public through an interactive dashboard1.

Employment and wages data for census designated places (CDPs) & cities, census areas & boroughs, and 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

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

Recruiting Agency Software Market size was valued at USD 2.41 Billion in 2024 and is projected to reach USD 3.81 Billion by 2031, growing at a CAGR 5.94% during the forecasted period 2024 to 2031

Global Recruiting Agency Software Market Drivers

Demand for personnel Acquisition Solutions Has Increased: Recruiting agency software is in more demand as a result of the intensifying competition among industries for top personnel. Companies look for practical and efficient ways to expedite the recruiting, screening, and sourcing of candidates in their talent acquisition operations.

Trending Towards Digital Recruitment: Recruiting firms are progressively implementing software solutions to improve their digital recruitment capacities as a result of the widespread use of digital platforms and online job boards. With the help of capabilities like resume parsing, job advertising, and applicant database management, recruiting companies may reach a larger prospect pool and fill vacancies faster.

Administrative Task Automation: Repetitive administrative processes including candidate communication, resume screening, and interview scheduling are automated by recruiting agency software. Agencies can save time, cut down on human error, and concentrate on more strategic parts of hiring, like relationship-building and candidate engagement, by automating these procedures.

Requirement for Improved Candidate Experience: Attracting and keeping top talent in the current competitive employment market depends heavily on the candidate experience. Personalized contact channels, mobile-friendly application websites, and feedback systems are just a few of the elements that recruiting agency software provides to improve the candidate experience and recruitment results.

Integration with HR Technologies: Applicant tracking systems (ATS), customer relationship management (CRM) tools, and human resource information systems (HRIS) are just a few examples of the HR technologies that recruiting agency software easily interacts with. Recruiting agencies may use analytics to make better decisions, increase data visibility, and streamline their recruitment procedures with this interface.

Rise of the Gig Economy and Remote Work: These two trends have completely changed the nature of traditional employment, presenting new opportunities as well as obstacles for hiring firms. Recruiting firms may adjust to the changing needs of clients and candidates in a remote work environment by using recruiting agency software, which makes remote hiring, virtual interviews, and talent management easier.

Emphasis on Diversity and Inclusion: As businesses attempt to create more inclusive and diverse workforces, they are placing a greater emphasis on diversity and inclusion in their recruitment strategies. Recruiting agencies can encourage diversity and inclusion in their hiring practices by utilizing the capabilities that recruiting agency software provides, which include varied candidate sourcing, bias-free job postings, and analytics for tracking diversity metrics.

Demand for Data-driven Recruitment: Agencies can monitor important recruitment parameters like time-to-fill, cost-per-hire, and candidate quality thanks to recruiting agency software’s sophisticated analytics and reporting features. Agencies may enhance their recruitment efforts, make well-informed decisions, and showcase their worth to clients by utilizing data-driven insights.

Regulatory Compliance and Data Security: Recruiting agencies may guarantee compliance with rules like OFCCP, EEOC, and GDPR by using recruiting agency software. With features like audit trails, role-based access controls, and data encryption, these software solutions guarantee data security and regulatory compliance all the way through the hiring process.

Scalability and Flexibility: Recruiting agency software offers flexible price options and customized features, allowing it to grow with the demands of expanding agencies. Recruiting agency software can adjust to the specific needs of agencies of any size, from tiny boutique businesses to giant global corporations, and help them achieve their growth goals.

Spatial evaluation of the region is associated with the assessment of the Quality of Life (QoL). Despite numerous research endeavoring to define, measure, quantify, and map the quality of life, there exists a consistent fault in Sri Lanka. Hence, the objective of this study was to construct a QoL index and determine the spatial disparities of QoL from the Polpitigma town to its periphery. The assessment was conducted by employing 20 geographical factors that quantify QoL using the Geographic Information Systems (GIS). The evaluation assigned weights to each criterion based on the assessments of both local residents and experts, utilizing the Multi-Criteria Decision Analysis (MCDA) and the Analytical Hierarchy Process (AHP). The findings indicated that cultural factors made a greater contribution compared to the environment,service functions,security and socioeconomic factors. Within the study area, the region with a higher quality of life (HQoL) only covered 4.5% (17.3 km2), whilst the lower QoL zone encompassed 63.8% (252 km2). And also, the distance from the town is a crucial factor in determining the spatial variations in QoL. The derived model can serve as a road map for local-level planning, as it has been validated and shown to have an accuracy of 74% through the Receiver operating characteristic (ROC) curve. Considering the lack of previous research in this field, this study offers a crucial contribution in enhancing the QoL for underprivileged communities in the study area by improving employment, income, and accessibility to physical infrastructure, public utility services, and cultural and recreational facilities. Especially the findings of this study can efficiently guide decisions for the distribution of financial resources to enhance the QoL in impoverished rural communities on the rural periphery of DS.

Employment and wages data for all locations, 2001 to 2016. Note on use for analysis: This data set mixes scale. It includes rows for census areas and economic regions, which contain multiple CDP's and cities from this same data set in many cases. To view this data by year and by borough, economic region, or city, add 'Employment and Wages Group Layers' to a WebMap or to the Build Your Own Map application. Contact dcraresearchandanalysis@alaska.gov with questions.Source: Alaska Department of Labor and Workforce Development.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

Dataset, GDB, and Online Map created by Renee Haley, NMCDC, May 2023 DATA ACQUISITION PROCESS

Scope and purpose of project: New Mexico is struggling to maintain its healthcare workforce, particularly in Rural areas. This project was undertaken with the intent of looking at flows of healthcare workers into and out of New Mexico at the most granular geographic level possible. This dataset, in combination with others (such as housing cost and availability data) may help us understand where our healthcare workforce is relocating and why.

The most relevant and detailed data on workforce indicators in the United States is housed by the Census Bureau's Longitudinal Employer-Household Dynamics, LEHD, System. Information on this system is available here:

https://lehd.ces.census.gov/

The Job-to-Job flows explorer within this system was used to download the data. Information on the J2J explorer can ve found here:

https://j2jexplorer.ces.census.gov/explore.html#1432012

The dataset was built from data queried with the LED Extraction Tool, which allows for the query of more intersectional and detailed data than the explorer. This is a link to the LED extraction tool:

https://ledextract.ces.census.gov/

The geographies used are US Metro areas as determined by the Census, (N=389). The shapefile is named lehd_shp_gb.zip, and can be downloaded under this section of the following webpage: 5.5. Job-to-Job Flow Geographies, 5.5.1. Metropolitan (Complete). A link to the download site is available below:

https://lehd.ces.census.gov/data/schema/j2j_latest/lehd_shapefiles.html

DATA CLEANING PROCESS

This dataset was built from 8 non intersectional datasets downloaded from the LED Extraction Tool.

Separate datasets were downloaded in order to obtain detailed information on the race, ethnicity, and educational attainment levels of healthcare workers and where they are migrating.

Datasets included information for the four separate quarters of 2021. It was not possible to download annual data, only quarterly. Quarterly data was summed in a later step to derive annual totals for 2021.

4 datasets for healthcare workers moving OUT OF New Mexico, with details on race, ethnicity, and educational attainment, were downloaded. 1 contained information on educational attainment, 2 contained information on 7 racial categories identifying as non- Hispanic, 3 contained information on those same 7 categories also identifying as Hispanic, and 4 contained information for workers identifying as white and Hispanic.

4 datasets for healthcare worker moving INTO New Mexico, with details on race, ethnicity, and educational attainment, were downloaded with the same details outlined above.

Each dataset was cleaned according to Data Template which kept key attributes and discarded excess information. Within each dataset, the J2J Indicators reflecting 6 different types of job migration were totaled in order to simplify analysis, as this information was not needed in detail.

After cleaning, each set of 4 datasets for workers moving INTO New Mexico were joined. The process was repeated for workers moving OUT OF New Mexico. This resulted 2 main datasets.

These 2 main datasets still listed all of the variables by each quarter of 2021. Because of this the data was split in JMP, so that attributes of educational attainment, race and ethnicity, of workers migrating by quarter were moved from rows to columns. After this, summary columns for the year of 2021 were derived. This resulted in totals columns for workers identifying as: 6 separate races and all ethnicities, all races and Hispanic, white-Hispanic, and workers of 6 different education levels, reflecting how many workers of each indicator migrated to and from metro areas in New Mexico in 2021.

The data split transposed duplicate rows reflecting differing worker attributes within the same metro area, resulting in one row for each metro area and reflecting the attributes in columns, thus resulting in a mappable dataset.

The 2 datasets were joined (on Metro Area) resulting in one master file containing information on healthcare workers entering and leaving New Mexico.

Rows (N=389) reflect all of the metro areas across the US, and each state. Rows include the 5 metro areas within New Mexico, and New Mexico State.

Columns (N=99) contain information on worker race, ethnicity and educational attainment, specific to each metro area in New Mexico.

78 of these rows reflect workers of specific attributes moving OUT OF the 5 specific Metro Areas in New Mexico and totals for NM State. This level of detail is intended for analyzing who is leaving what area of New Mexico, where they are going to, and why.

13 Columns reflect each worker attribute for healthcare workers moving INTO New Mexico by race, ethnicity and education level. Because all 5 metro areas and New Mexico state are contained in the rows, this information for incoming workers is available by metro area and at the state level - there is less possability for mapping these attributes since it was not realistic or possible to create a dataset reflecting all of these variables for every healthcare worker from every metro area in the US also coming into New Mexico (that dataset would have over 1,000 columns and be unmappable). Therefore this dataset is easier to utilize in looking at why workers are leaving the state but also includes detailed information on who is coming in.

The remaining 8 columns contain geographic information.

GIS AND MAPPING PROCESS

The master file was opened in Arc GIS Pro and the Shapefile of US Metro Areas was also imported

The excel file was joined to the shapefile by Metro Area Name as they matched exactly

The resulting layer was exported as a GDB in order to retain null values which would turn to zeros if exported as a shapefile.

This GDB was uploaded to Arc GIS Online, Aliases were inserted as column header names, and the layer was visualized as desired.

SYSTEMS USED

MS Excel was used for data cleaning, summing NM state totals, and summing quarterly to annual data.

JMP was used to transpose, join, and split data.

ARC GIS Desktop was used to create the shapefile uploaded to NMCDC's online platform.

VARIABLE AND RECODING NOTES

Summary of variables selected for datasets downloaded focused on educational attainment:

J2J Flows by Educational Attainment

Summary of variables selected for datasets downloaded focused on race and ethnicity:

J2J Flows by Race and Ethnicity

Note: Variables in Datasets 1 through 4 downloaded twice, once for workers coming into New Mexico and once for those leaving NM. VARIABLE: LEHD VARIABLE DEFINITION LEHD VARIABLE NOTES DETAILS OR URL FOR RAW DATA DOWNLOAD

Geography Type - State Origin and Destination State

Data downloaded for worker migration into and out of all US States

Geography Type - Metropolitan Areas Origin and Dest Metro Area

Data downloaded for worker migration into and out of all US Metro Areas

NAICS sectors North American Industry Classification System Under Firm Characteristics Only downloaded for Healthcare and Social Assistance Sectors

Other Firm Characteristics No Firm Age / Size Detail Under Firm Characteristics Downloaded data on all firm ages, sizes, and other details.

Worker Characteristics Education, Race, Ethnicity

Non Intersectional data aside from Race / Ethnicity data.

Sex Gender

0 - All Sexes Selected

Age Age

A00 All Ages (14-99)

Education Education Level E0, E1, E2, E3, 34, E5 E0 - All Education Categories, E1 - Less than high school, E2 - High school or equivalent, no college, E3 - Some college or Associate’s degree, E4 - Bachelor's degree or advanced degree, E5 - Educational attainment not available (workers aged 24 or younger)

Dataset 1 All Education Levels, E1, E2, E3, E4, and E5

RACE

A0, A1, A2, A3, A4, A5 OPTIONS: A0 All Races, A1 White Alone, A2 Black or African American Alone, A3 American Indian or Alaska Native Alone, A4 Asian Alone, A5 Native Hawaiian or Other Pacific Islander Alone, SDA7 Two or More Race Groups

ETHNICITY

A0, A1, A2 OPTIONS: A0 All Ethnicities, A1 Not Hispanic or Latino, A2 Hispanic or Latino

Dataset 2 All Races (A0) and All Ethnicities (A0)

Dataset 3 6 Races (A1 through A5) and All Ethnicities (A0)

Dataset 4 White (A1) and Hispanic or Latino (A1)

Quarter Quarter and Year

Data from all quarters of 2021 to sum into annual numbers; yearly data was not available

Employer type Sector: Private or Governmental

Query included all healthcare sector workflows from all employer types and firm sizes from every quarter of 2021

J2J indicator categories Detailed types of job migration

All options were selected for all datasets and totaled: AQHire, AQHireS, EE, EES, J2J, J2JS. Counts were selected vs. earnings, and data was not seasonally adjusted (unavailable).

NOTES AND RESOURCES

The following resources and documentation were used to navigate the LEHD and J2J Worker Flows system and to answer questions about variables:

https://lehd.ces.census.gov/data/schema/j2j_latest/lehd_public_use_schema.html

https://www.census.gov/history/www/programs/geography/metropolitan_areas.html

https://lehd.ces.census.gov/data/schema/j2j_latest/lehd_csv_naming.html

Statewide (New

Locations of offices providing job training in Los Angeles CountyThis dataset is maintained through the County of Los Angeles Location Management System. The Location Management System is used by the County of Los Angeles GIS Program to maintain a single, comprehensive geographic database of locations countywide. For more information on the Location Management System, visithttp://egis3.lacounty.gov/lms/.

BRS and EBS Transmitters from the Federal Communications Commission -http://wireless.fcc.gov/uls/index.htm?job=transaction&page=weeklyThis dataset is maintained through the County of Los Angeles Location Management System. The Location Management System is used by the County of Los Angeles GIS Program to maintain a single, comprehensive geographic database of locations countywide. For more information on the Location Management System, visithttp://egis3.lacounty.gov/lms/.

2018-2019 Statewide School Districts for Texas. This information was collected from all 253 central appraisal districts and from the Texas Education Agency. GIS staff of the Texas Legislative Council created the school district boundaries using the 2010 TIGER/Line Shapefile as base geography and made further corrections to match the school district boundary updates and name changes for the 2018-2019 School Year. These changes include lines that are not census geography. Changes to school district boundaries may include one or all of the following types: school district annexations or de-annexations; school district consolidations, deletions or additions; boundary corrections to the Texas Legislative Council database; boundary adjustments due to more spatially accurate data involving land parcels and survey data received from a county central appraisal district. Note: The 2018-2019 School Year school districts in the council's geographic file are not the same as the districts in the Census Bureau's 2010 TIGER/Line Shapefile. The population data for the council's 2018-2019 school districts does not correspond with the population data reported for the school districts reported by the Census Bureau.