The Geographic Information Systems (GIS) Platform market is experiencing robust growth, projected to reach a market size of $4078.2 million in 2025. While the provided CAGR is missing, considering the widespread adoption of GIS across various sectors like government, utilities, and commercial businesses, coupled with advancements in cloud-based GIS and increasing demand for spatial analytics, a conservative estimate of the Compound Annual Growth Rate (CAGR) between 2025 and 2033 would be around 7-9%. This suggests a significant expansion of the market over the forecast period. Key drivers include the rising need for efficient resource management, improved infrastructure planning, precise location-based services, and the growing adoption of big data analytics combined with location intelligence. The market is segmented by type (Desktop GIS, Web Map Service GIS, Others) and application (Government & Utilities, Commercial Use), reflecting the diverse applications of GIS technology. Leading players like Environmental Systems Research Institute (Esri), Hexagon, Pitney Bowes, and SuperMap are shaping the market landscape through continuous innovation and strategic partnerships. The North American market currently holds a significant share due to high technology adoption and substantial investments in GIS infrastructure, but rapid growth is anticipated in Asia Pacific regions like China and India driven by urbanization and infrastructure development. The increasing availability of affordable high-resolution imagery and data fuels further expansion. The continued integration of GIS with other technologies like AI and IoT is expected to unlock new applications and further drive market growth. Challenges include the high initial investment costs for sophisticated GIS solutions, the need for skilled professionals to manage and interpret data, and ensuring data security and privacy. However, the benefits of improved decision-making, optimized resource allocation, and enhanced operational efficiency are expected to outweigh these challenges, contributing to the sustained expansion of the GIS Platform market throughout the forecast period. The market's future trajectory remains positive, fueled by technological advancements and the increasing reliance on location intelligence across various industries.

This dataset is a subset of the statewide parcel dataset. The parcels in this dataset have been assigned a Government Ownership classification using the values "Federal", "State", "County Fee", "Tax Forfeit", or "Tribal" where it can be inferred from other fields in the parcel record. Only parcels from counties that have opted-in to sharing parcel data are included in this dataset.

For more information about the opt-in open parcel dataset, please refer to the opt-in open parcel compilation. https://gisdata.mn.gov/dataset/plan-parcels-open.

The State of Minnesota makes no representation or warranties, express or implied, with respect to the use or reuse of data provided herewith, regardless of its format or the means of its transmission. THE DATA IS PROVIDED “AS IS” WITH NO GUARANTEE OR REPRESENTATION ABOUT THE ACCURACY, CURRENCY, SUITABILITY, PERFORMANCE, MECHANTABILITY, RELIABILITY OR FITINESS OF THIS DATA FOR ANY PARTICULAR PURPOSE. This dataset is NOT suitable for accurate boundary determination. Contact a licensed land surveyor if you have questions about boundary determinations.

The North Carolina state and local government metadata profile as adopted by the NC Geographic Information Coordinating Council. The document and other information can be found at: https://it.nc.gov/documents/files/gicc-smac-state-local-gov-metadata-profile.

[Metadata] Description: Detailed Government Landownership in the State of Hawaii as of 2022: County, Federal, State, and State DHHL Lands (by TMK parcel)

[Metadata] Description: Government Landownership in the State of Hawaii as of 2022: County, Federal, State, and State DHHL LandsSources: County of Kauai, April, 2022; City & County of Honolulu, April 27, 2022; County of Maui, April, 2022; County of Hawaii, April, 2022; State Department of Hawaiian Home Lands, October, 2022. This dataset was created using ownership information provided by the counties via tax map key parcel layers and ownership tables. Parcels were queried using the "Owner" field for state, county, and federal agency names. State GIS staff verified land ownership using the online service QPublic, the 2022 Department of Hawaiian Home Lands layer and other GIS layers and resources. Where ownership was still unclear, State GIS personnel reached out to appropriate agencies for clarification. Summary fields “majorowner” and “type” were created using additional filters, queries and analysis tools to summarize the data based upon government ownership sector and type. Also see detailed government ownership layer (gov_own_detailed) which is comprised of government land ownership by TMK parcel. The parcel boundaries are intended to provide a visual reference only and do not represent legal or survey level accuracy. Attributes are for assessment purposes only and are subject to change at any time. For additional information, please refer to metadata at https://files.hawaii.gov/dbedt/op/gis/data/gov_own.pdf or contact the 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.

The California State Lands Commission (CSLC) was created by the California Legislature in 1938 and given the authority and responsibility to manage certain public lands within the state. The public lands under the Commission’s jurisdiction are of two distinct types—sovereign lands acquired upon California’s admission into the Union in 1850; and certain federally granted lands including school lands, and swamp and overflowed lands. For purposes of this GIS data, sovereign lands are considered to be further divided into two general categories—fixed-boundary sovereign lands and ambulatory-boundary sovereign lands. The following lands are included in this data: Portions of the ambulatory-boundary for state sovereign lands at a specific point in time, for portions of the San Joaquin River. NOT INCLUDED IN THIS DATA: School lands: These are what remains of nearly 5.5 million acres throughout the state originally granted to California by Congress in 1853 to benefit public education. Fixed-boundary sovereign lands: These are sovereign, public trust lands having fixed boundaries as the result of land exchanges, boundary line agreements or court orders. Swamps and overflowed lands: These are what remain of federal lands granted to California by Congress in 1850 to encourage reclamation and development of agricultural lands. ALSO NOT INCLUDED IN THIS DATA: Ownership details within the U.S. Government meanders of Owens Lake. THIS DATA SUPERSEDES all previously published GIS information with respect to the above described state-owned lands under the jurisdiction of the CSLC.

The dataset contains locations and attributes of GSA owned or leased buildings, 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. A database provided by GSA identified Federal locations and DC GIS staff geo-processed the data.

This GIS layer contains the boundaries of the unincorporated county and incorporated cities within the state of California. The initial dataset was first published on October 20, 2021, and was based on the State Board of Equalization's tax rate area boundaries. As of April 1, 2024, the maintenance of this dataset is provided by the California Department of Tax and Fee Administration for the purpose of determining sales and use tax jurisdictions. The boundaries are continuously being revised when areas of conflict are discovered between the original boundary provided by the California State Board of Equalization and the boundary made publicly available by local, state, and federal government. Some differences may occur between actual recorded boundaries and the boundaries used for sales and use tax purposes. The boundaries in this map are representations of taxing jurisdictions for the purpose of determining sales and use tax areas and should not be used to determine precise city or county boundary line locations. COUNTY = county name; CITY = city name or unincorporated territory; COPRI = county number followed by the 3-digit city primary number used in the California State Board of Equalization's 6-digit tax rate area numbering system (for the purpose of this map, unincorporated areas are assigned "000" to indicate that the area is not within a city).

1. US Census Bureau Cartographic Boundary File of county boundaries for each state in the Unites States.

From the US Census Bureau: "The cartographic boundary files are simplified representations of selected geographic areas from the Census Bureau’s MAF/TIGER geographic database. These boundary files are specifically designed for small scale thematic mapping."

The global Government Information Construction Service market is experiencing robust growth, driven by increasing government investments in digital infrastructure, the rising adoption of cloud-based solutions for enhanced data security and accessibility, and the growing need for efficient and streamlined information management systems. The market's expansion is further fueled by the imperative to improve citizen services through digital channels and the ongoing development of smart city initiatives. While on-premises solutions still hold a significant share, the cloud-based segment is witnessing accelerated growth, projected to dominate the market in the coming years due to its scalability, cost-effectiveness, and enhanced accessibility. Regional variations exist, with North America and Europe currently leading the market due to advanced technological infrastructure and high government spending on digital transformation projects. However, Asia-Pacific is poised for substantial growth, driven by rapid urbanization and increasing government initiatives focused on digitalization and e-governance. Challenges include data security concerns, the need for robust cybersecurity measures, and the complexities associated with integrating legacy systems with new cloud-based solutions. This requires significant investment in training and skilled personnel to manage and maintain these systems effectively. The market is highly competitive, with established players like IBM, Microsoft, and SAP competing with specialized service providers and consulting firms such as Accenture and Deloitte. The forecast period (2025-2033) anticipates continued expansion, propelled by ongoing technological advancements and increasing government focus on data-driven decision-making. The shift towards advanced analytics and artificial intelligence (AI) in government operations is another key growth driver, enabling more efficient resource allocation, improved public services, and better citizen engagement. Furthermore, the increasing adoption of big data technologies and the Internet of Things (IoT) within government infrastructure will further drive the demand for robust information construction services. However, potential restraints include budgetary constraints in some regions, concerns regarding data privacy and compliance, and the need for seamless interoperability across different government agencies and systems. The market will witness a dynamic landscape with ongoing mergers and acquisitions, strategic partnerships, and the emergence of innovative solutions catering to specific government needs. Competitive differentiation will increasingly rely on the ability to provide secure, scalable, and user-friendly solutions that address the evolving challenges of public sector information management.

The Government Information Construction Service market is experiencing robust growth, driven by increasing government initiatives to modernize infrastructure, enhance citizen services, and improve data management capabilities. The market's expansion is fueled by a rising need for efficient and secure data handling, particularly in the context of smart city development and the increasing adoption of cloud-based solutions. This shift towards cloud-based services offers scalability, cost-effectiveness, and improved accessibility, surpassing traditional on-premises systems. While the initial investment for cloud migration can be substantial, the long-term benefits in terms of reduced maintenance costs and enhanced agility are compelling government agencies to embrace this technology. Furthermore, the growing adoption of data analytics and artificial intelligence (AI) within government operations is further fueling market growth, enabling better decision-making and enhanced service delivery. However, challenges remain, including concerns about data security, interoperability issues across different systems, and the need for skilled professionals to manage and maintain these complex systems. Regional variations exist within the market, with North America and Europe currently holding the largest market share, due to advanced digital infrastructure and high government spending on IT initiatives. However, Asia-Pacific is emerging as a region with significant growth potential, driven by substantial investments in digital transformation across various governments within the region. The market is segmented by application (city and rural) and deployment type (cloud-based and on-premises). Cloud-based solutions are witnessing rapid adoption, while on-premises deployments remain relevant, particularly in sectors with stringent security requirements. Key players like IBM, Microsoft, SAP, Oracle, and Accenture are actively involved in providing solutions, fostering competition and innovation within the sector. The forecast period (2025-2033) anticipates sustained growth, propelled by continued digital transformation efforts and the increasing importance of data-driven governance. Let's assume a 2025 market size of $15 billion, with a CAGR of 12% for the forecast period. This implies a substantial market expansion by 2033.

This dataset includes all 7 metro counties that have made their parcel data freely available without a license or fees.

This dataset is a compilation of tax parcel polygon and point layers assembled into a common coordinate system from Twin Cities, Minnesota metropolitan area counties. No attempt has been made to edgematch or rubbersheet between counties. A standard set of attribute fields is included for each county. The attributes are the same for the polygon and points layers. Not all attributes are populated for all counties.

NOTICE: The standard set of attributes changed to the MN Parcel Data Transfer Standard on 1/1/2019.
https://www.mngeo.state.mn.us/committee/standards/parcel_attrib/parcel_attrib.html

See section 5 of the metadata for an attribute summary.

Detailed information about the attributes can be found in the Metro Regional Parcel Attributes document.

The polygon layer contains one record for each real estate/tax parcel polygon within each county's parcel dataset. Some counties have polygons for each individual condominium, and others do not. (See Completeness in Section 2 of the metadata for more information.) The points layer includes the same attribute fields as the polygon dataset. The points are intended to provide information in situations where multiple tax parcels are represented by a single polygon. One primary example of this is the condominium, though some counties stacked polygons for condos. Condominiums, by definition, are legally owned as individual, taxed real estate units. Records for condominiums may not show up in the polygon dataset. The points for the point dataset often will be randomly placed or stacked within the parcel polygon with which they are associated.

The polygon layer is broken into individual county shape files. The points layer is provided as both individual county files and as one file for the entire metro area.

In many places a one-to-one relationship does not exist between these parcel polygons or points and the actual buildings or occupancy units that lie within them. There may be many buildings on one parcel and there may be many occupancy units (e.g. apartments, stores or offices) within each building. Additionally, no information exists within this dataset about residents of parcels. Parcel owner and taxpayer information exists for many, but not all counties.

This is a MetroGIS Regionally Endorsed dataset.

Additional information may be available from each county at the links listed below. Also, any questions or comments about suspected errors or omissions in this dataset can be addressed to the contact person at each individual county.

Anoka = http://www.anokacounty.us/315/GIS
Caver = http://www.co.carver.mn.us/GIS
Dakota = http://www.co.dakota.mn.us/homeproperty/propertymaps/pages/default.aspx
Hennepin = https://gis-hennepin.hub.arcgis.com/pages/open-data
Ramsey = https://www.ramseycounty.us/your-government/open-government/research-data
Scott = http://opendata.gis.co.scott.mn.us/
Washington: http://www.co.washington.mn.us/index.aspx?NID=1606

City council members, their positions, and term end date for municipalities in Alaska.Source: Alaska Municipal LeagueThis 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 Municipal League.

A feature layer view used to share GIS activity information with the public.

The COVID-19 Guidance Questionnaire

The COVID-19 Guidance Questionnaire is published on Government website. The opinion he provides has no medical value: its goal is to direct the respondent according to his or her health and symptoms towards the right behavior to adopt.

The algorithm used to propose guidance is in line with documentation published by the Ministry of Solidarity and Health, which reference also other solutions that comply with it.

Data from the COVID-19 Guidance Questionnaire

This data is published in open data to inform about the number of times the questionnaire has been completed to the end as well as the guidance messages sent.

Data are grouped by week, department and age group. For each week/department/age class are indicated the number of responses obtained, the number of referrals to the UAS, the number of referrals to a consultation, and the number of messages indicating that the respondent should stay at home and monitor their health.

The published ‘csv’ file will be updated weekly by adding the new data.

The questionnaire was offline on 27 January 2021, so data runs until 26 January 2021.

Administered Lands is a BLM Alaska GIS dataset that combines publicly available borough, municipality, state, federal, and other entity management and ownership GIS data. This is the basis for BLM’s national Surface Management Agency GIS dataset that was developed to fulfill the public and Government’s need to know what agency is managing Federal land in a given area. This data set is comprised of various sources of geospatial information that have been acquired from local, state and federal agencies in order to assemble a comprehensive representation of current land surface manager. There are many land managing agencies and branches of government and this dataset attempts to classify these entities into general categories. This data does not demonstrate or infer land ownership. The business need for this data includes, but is not limited to, land use planning, permitting, recreation, and emergency response. Due to the nature of assembling geospatial information from multiple sources, integration of features into a single layer may introduce inaccurate artifacts. Acquired datasets have been cross-walked to a standardized schema to aid in the depiction of land surface manager across the state of Alaska. This dataset will contain errors. For the most up to date and accurate information, please contact the surface manager agency for the area in which you are interested.

1. US Census Bureau Cartographic Boundary File of county boundaries for the Commonwealth of Virginia.

From the US Census Bureau: "The cartographic boundary files are simplified representations of selected geographic areas from the Census Bureau’s MAF/TIGER geographic database. These boundary files are specifically designed for small scale thematic mapping."

The dataset contains locations and attributes of Post Offices, 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. Information provided by the United States Postal Service (USPS) and DC GIS staff geo-processed the data.

The GIS Open Data Portal is designed to provide the public with simple and open access to high quality, location-based data free of charge. This portal provides capabilities to view, download, visualize, and analyze available data. This is the new GIS open data portal launched 08/26/2020.

Data.AustinTexas.gov is the official portal for Open Data from the City of Austin (COA). The City of Austin’s GIS/Map Downloads page is the official portal for COA GIS data and map products that do not reside on Data.AustinTexas.gov. Both are public domain websites, which means you may link to Data.AustinTexas.gov and ftp://ftp.ci.austin.tx.us/GIS-Data/Regional/coa_gis.html at no cost. When you link to Data.AustinTexas.gov or ftp://ftp.ci.austin.tx.us/GIS-Data/Regional/coa_gis.html, please do it in an appropriate context as a service to people when they need to find official City of Austin data. We encourage you to use our logo, which we’ve provided below. Placement of the Data.AustinTexas.gov logo is to be used only as a marker and link to the home page. It is not meant as a form of endorsement or approval from the City of Austin. City of Austin Open Data Terms of Use - https://data.austintexas.gov/stories/s/ranj-cccq