Where does healthcare cost the most? (Learn ArcGIS online lesson).

Ownership Cost 30% of income from PUMA (Public Use MicroData Area) Data

The global Geographic Information System (GIS) software market is experiencing robust growth, driven by increasing adoption across various sectors like government, utilities, and transportation. The market, currently valued at approximately $15 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This significant expansion is fueled by several key trends, including the rising demand for location-based services, the proliferation of geospatial data, and the increasing use of cloud-based GIS solutions. The cloud-based segment is rapidly gaining traction due to its scalability, cost-effectiveness, and accessibility. Furthermore, the enterprise application segment dominates the market share, reflecting the widespread adoption of GIS for complex spatial analysis and decision-making in large organizations. While the market faces some restraints, such as the high initial investment costs for some advanced systems and the need for specialized expertise, the overall growth trajectory remains positive. The increasing integration of GIS with other technologies like AI and IoT further enhances its capabilities, opening new avenues for market expansion. Major players like Esri, Google, and Pitney Bowes are leading the market, constantly innovating and expanding their product offerings to meet evolving customer needs. The regional distribution of the market shows strong performance in North America and Europe, driven by advanced technological infrastructure and high adoption rates. However, the Asia-Pacific region is emerging as a significant growth area, propelled by rapid urbanization and infrastructure development. The competitive landscape is marked by both established players and emerging startups, fostering innovation and competition. The ongoing advancements in GIS technology, including improvements in data visualization, analytics, and mobile accessibility, are expected to further propel market growth in the coming years. The integration of GIS with other technologies will lead to new applications and expanded opportunities, ultimately driving the market towards sustained expansion throughout the forecast period.

Displacement risk indicator showing how many households within the specified groups are facing either housing cost burden (contributing more than 30% of monthly income toward housing costs) or severe housing cost burden (contributing more than 50% of monthly income toward housing costs).

The Cost of Risk metric shows how much the city spends on handling risks (like insurance, legal expenses, or accident payouts) compared to how much money it collects overall.The performance measure dashboard is available at 5.17 Total Cost of Risk.Additional InformationSource: Peoplesoft and ACFRContact: Laura CalderContact E-Mail: laura.calder@tempe.govData Source Type: ExcelPreparation Method: The total expenses in Fund 2661 (The Risk Management cost center) is divided by the total revenue from Annual Comprehensive Financial Report to calculate the total cost of Risk.Publish Frequency: AnnualPublish Method: ManualData Dictionary (pending update)

THE GEOINQUIRIES™ COLLECTION FOR GOVERNMENT AND CIVICShttp://www.esri.com/geoinquiriesThe Esri GeoInquiry™ collection for Government and Civics contains 20 free, web-mapping activities that correspond and extend map-based concepts in leading middle school Government and Civics science textbooks. The activities use a standard inquiry-based instructional model, require about 15 minutes for a teacher to deliver, and are device agnostic. The activities harmonize with the C3 Framework. Fifteen activities are Level 1, requiring no login. Five activities are Level 2, requiring a login and use of the analysis tools in ArcGIS Online.All Government and Civics GeoInquiries™ can be found at: http://esriurl.com/govGeoInquiries All GeoInquiries™ can be found at: http://www.esri.com/geoinquiries

This packaged data collection contains two sets of two additional model runs that used the same inputs and parameters as our primary model, with the exception being we implemented a "maximum corridor length" constraint that allowed us to identify and visualize the corridors as being well-connected (≤15km) or moderately connected (≤45km). This is based on an assumption that corridors longer than 45km are too long to sufficiently accommodate dispersal. One of these sets is based on a maximum corridor length that uses Euclidean (straight-line) distance, while the other set is based on a maximum corridor length that uses cost-weighted distance. These two sets of corridors can be compared against the full set of corridors from our primary model to identify the remaining corridors, which could be considered poorly connected. This package includes the following data layers: Corridors classified as well connected (≤15km) based on Cost-weighted Distance Corridors classified as moderately connected (≤45km) based on Cost-weighted Distance Corridors classified as well connected (≤15km) based on Euclidean Distance Corridors classified as moderately connected (≤45km) based on Euclidean Distance Please refer to the embedded metadata and the information in our full report for details on the development of these data layers. Packaged data are available in two formats: Geodatabase (.gdb): A related set of file geodatabase rasters and feature classes, packaged in an ESRI file geodatabase. ArcGIS Pro Map Package (.mpkx): The same data included in the geodatabase, presented as fully-symbolized layers in a map. Note that you must have ArcGIS Pro version 2.0 or greater to view. See Cross-References for links to individual datasets, which can be downloaded in raster GeoTIFF (.tif) format.

This is a representation of all primary roads as well as parks and institution roads that the Iowa Department of Transportation has the responsibility to maintain. Each maintenance garage is displayed in a different color to show where one area of responsibility ends and another begins. Updated August 2016.

PUMA data Gross rent as % of House Hold Income (30% or more)

Alaska Energy Authority Power Cost Equalization (PCE) program by community. The power cost equalization program supports rural Alaskans who live in areas where energy costs are significantly higher than urban areas in meeting the cost of electricity."AEA determines eligibility of community facilities and residential customers and authorizes payment to the electric utility. Commercial customers are not eligible to receive PCE credit. Participating utilities are required to reduce each eligible customer’s bill by the amount that the State pays for PCE. RCA determines if a utility is eligible to participate in the program and calculates the amount of PCE per kWh payable to the utility. More information about the RCA may be found at www.state.ak.us/rca."(AEA, 2017)Source: Alaska Energy AuthorityThis 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 - it has been primarily compiled from AEA PCE Fiscal Year Utility Report PDFs. For more information and for questions about this data, see: AEA Power Cost Equalization

LOW TRANSPORTATION COST INDEXSummaryThe Low Transportation Cost Index is based on estimates of transportation expenses for a family that meets the following description: a 3-person single-parent family with income at 50% of the median income for renters for the region (i.e. CBSA). The estimates come from the Location Affordability Index (LAI). The data correspond to those for household type 6 (hh_type6_) as noted in the LAI data dictionary. More specifically, among this household type, we model transportation costs as a percent of income for renters (t_rent). Neighborhoods are defined as census tracts. The LAI data do not contain transportation cost information for Puerto Rico.InterpretationValues are inverted and percentile ranked nationally, with values ranging from 0 to 100. The higher the transportation cost index, the lower the cost of transportation in that neighborhood. Transportation costs may be low for a range of reasons, including greater access to public transportation and the density of homes, services, and jobs in the neighborhood and surrounding community.

Data Source: Location Affordability Index (LAI) data, 2012-2016.Related AFFH-T Local Government, PHA and State Tables/Maps: Table 12; Map 11.

References: www.locationaffordability.infohttps://lai.locationaffordability.info//lai_data_dictionary.pdf

To learn more about the Low Transportation Cost 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

Sites in the State Water Resources Control Board GeoTracker system under the Site Cleanup Program that are open with categories of in and out of cost recovery. Layer contains sites managed under the Site Cleanup Program and is intended for use and viewing in the Site Cleanup Program GIS Story. The DWQ at the State Water Board developed this GIS Story of the Site Cleanup Program to inform the public of its mission and duties. The story intends to depict the importance of the program, describe the program's main roles and responsibilities, and provide input on the current and potential future challenges of the Site Cleanup Program. For more information on the Water Board's Site Cleanup Program visit Site Cleanup Program (SCP) | California State Water Resources Control Board.

The global GIS mapping tools market is experiencing robust growth, driven by increasing demand across diverse sectors. The market, estimated at $15 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033, reaching approximately $28 billion by 2033. This growth is fueled by several key factors. Firstly, the burgeoning adoption of cloud-based solutions offers scalability, cost-effectiveness, and enhanced accessibility to a wider user base, including small and medium-sized enterprises (SMEs). Secondly, the escalating need for precise spatial data analysis in various applications, such as urban planning, geological exploration, and water resource management, is significantly boosting market demand. The increasing integration of GIS with other technologies like AI and IoT further amplifies its capabilities, leading to more sophisticated applications and increased market penetration. Finally, government initiatives promoting digitalization and smart city development across the globe are indirectly fueling this market expansion. However, certain restraints limit market growth. The high initial investment cost for advanced GIS software and the requirement for skilled professionals to operate these systems can be a barrier, especially for smaller organizations. Additionally, data security and privacy concerns related to the handling of sensitive geographical information pose challenges to wider adoption. Market segmentation reveals strong growth in the cloud-based GIS segment, driven by its inherent advantages, while applications in urban planning and geological exploration lead the application-based segmentation. North America and Europe currently hold significant market shares, with strong growth potential in the Asia-Pacific region due to increasing infrastructure development and government investments. Leading companies like Esri, Hexagon, and Autodesk are shaping the market landscape through continuous innovation and competitive pricing strategies, while the emergence of open-source options like QGIS and GRASS GIS provides alternative, cost-effective solutions.

Deprecation notice: This tool is deprecated because this functionality is now available with out-of-the-box tools in ArcGIS Pro. The tool author will no longer be making further enhancements or fixing major bugs.Use Add GTFS to a Network Dataset to incorporate transit data into a network dataset so you can perform schedule-aware analyses using the Network Analyst tools in ArcMap.After creating your network dataset, you can use the ArcGIS Network Analyst tools, like Service Area and OD Cost Matrix, to perform transit/pedestrian accessibility analyses, make decisions about where to locate new facilities, find populations underserved by transit or particular types of facilities, or visualize the areas reachable from your business at different times of day. You can also publish services in ArcGIS Server that use your network dataset.The Add GTFS to a Network Dataset tool suite consists of a toolbox to pre-process the GTFS data to prepare it for use in the network dataset and a custom GTFS transit evaluator you must install that helps the network dataset read the GTFS schedules. A user's guide is included to help you set up your network dataset and run analyses.Instructions:Download the tool. It will be a zip file.Unzip the file and put it in a permanent location on your machine where you won't lose it. Do not save the unzipped tool folder on a network drive, the Desktop, or any other special reserved Windows folders (like C:\Program Files) because this could cause problems later.The unzipped file contains an installer, AddGTFStoaNetworkDataset_Installer.exe. Double-click this to run it. The installation should proceed quickly, and it should say "Completed" when finished.Read the User's Guide for instructions on creating and using your network dataset.System requirements:ArcMap 10.1 or higher with a Desktop Standard (ArcEditor) license. (You can still use it if you have a Desktop Basic license, but you will have to find an alternate method for one of the pre-processing tools.) ArcMap 10.6 or higher is recommended because you will be able to construct your network dataset much more easily using a template rather than having to do it manually step by step. This tool does not work in ArcGIS Pro. See the User's Guide for more information.Network Analyst extensionThe necessary permissions to install something on your computer.Data requirements:Street data for the area covered by your transit system, preferably data including pedestrian attributes. If you need help preparing high-quality street data for your network, please review this tutorial.A valid GTFS dataset. If your GTFS dataset has blank values for arrival_time and departure_time in stop_times.txt, you will not be able to run this tool. You can download and use the Interpolate Blank Stop Times tool to estimate blank arrival_time and departure_time values for your dataset if you still want to use it.Help forum

Beginning in 2005, the Division of Community and Regional Affairs began collecting prices of heating fuel and unleaded gasoline in 100 select communities. The communities have remained constant since the project’s inception. The prices for heating fuel in these 100 communities are collected via a telephone survey of each fuel retailer. Survey methodology has evolved over time; however, the reported prices should be considered representative of what a community resident would have paid for a gallon of heating fuel (including tax) on the day of contact.

This publication contains tabular data used to evaluate the effects of fuel treatments and previously burned areas on daily wildland fire management costs. The data represent daily Forest Service fire management costs for a sample of 56 fires that burned between 2008 and 2012 throughout the conterminous United States. Included in the data is a suite of spatially derived variables used to control for variation in daily fire management costs, including topography, fire weather, fuel loading, remoteness, and human populations-at-risk. These data were extracted using daily fire progression maps produced using the methods outlined in Parks (2014).

This packaged data collection contains all of the outputs from our primary model, including the following data layers: Habitat Cores (vector polygons) Least-cost Paths (vector lines) Least-cost Corridors (raster) Least-cost Corridors (vector polygon interpretation) Modeling Extent (vector polygon) Please refer to the embedded spatial metadata and the information in our full report for details on the development of these data layers. Packaged data are available in two formats: Geodatabase (.gdb): A related set of file geodatabase rasters and feature classes, packaged in an ESRI file geodatabase. ArcGIS Pro Map Package (.mpkx): The same data included in the geodatabase, presented as fully-symbolized layers in a map. Note that you must have ArcGIS Pro version 2.0 or greater to view. See Cross-References for links to individual datasets, which can be downloaded in shapefile (.shp) or raster GeoTIFF (.tif) formats.

B25088_001E-- Median Selected Monthly Owner Costs (Dollars) by Mortgage Status - TotalB25088_001M-- Median Selected Monthly Owner Costs (Dollars) by Mortgage Status - Margin of Error This data was accessed from the 2013 ACS 5 Year Data API.

Data contains estimated costs for maintenance and replacement of stormwater assets located in the City of Johns Creek, GA.Cost modeling was performed in 2020 by Lowe Engineering as part of the 2019-2020 Complete Stormwater Assessment Project.

The geographical mapping software market is experiencing robust growth, driven by increasing demand across diverse sectors. The market's expansion is fueled by several key factors: the rising adoption of cloud-based solutions offering enhanced accessibility and scalability, the burgeoning need for precise spatial data analysis in urban planning and water resource management, and the escalating use of GIS technology in geological exploration for resource discovery and environmental monitoring. The market's compound annual growth rate (CAGR) is estimated at 8% between 2025 and 2033, projecting significant market expansion. This growth is further supported by the increasing availability of high-resolution satellite imagery and improved data processing capabilities, leading to more accurate and detailed maps for various applications. While the market shows strong potential, certain restraints, including high software licensing costs and the complexity of some GIS software, may impede growth to some extent. However, the overall trend leans towards increased adoption driven by the significant benefits of enhanced spatial analysis across industries. Market segmentation reveals a strong demand for cloud-based solutions due to their flexibility and cost-effectiveness compared to web-based or on-premise software. Geographically, North America and Europe currently hold significant market shares, reflecting established GIS infrastructure and technological advancement. However, Asia-Pacific is expected to witness substantial growth in the coming years driven by rapid urbanization, infrastructure development, and increased government investment in mapping initiatives. This region's expanding market will be fueled by countries like China and India, with significant potential for market penetration. The key players in this competitive landscape continually innovate, releasing new features and functionalities to maintain their market positions. The focus is increasingly on user-friendliness, integration with other software platforms, and advanced analytical capabilities.