This viewer was created for the Arkansas GIS Office to aid staff as well as County personnel in quickly locating information related to tax parcels.

Vector polygon map data of property parcels from St. Francis County, Arkansas containing 18,202 features.

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Property parcel GIS map data consists of detailed information about individual land parcels, including their boundaries, ownership details, and geographic coordinates.

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Property parcel data can be used to analyze and visualize land-related information for purposes such as real estate assessment, urban planning, or environmental management.

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Available for viewing and sharing as a map in a Koordinates map viewer. This data is also available for export to DWG for CAD, PDF, KML, CSV, and GIS data formats, including Shapefile, MapInfo, and Geodatabase.

description: This dataset contains point features representing the approximate location of tax parcels contained in County Assessor tax rolls. Individual county data was integrated into this statewide publication by the Arkansas Geographic Information Office (AGIO). The Computer Aided Mass Appraisal (CAMA) systems maintained in each county are used to populate the database attributes for each centroid feature. The entity attribute structure conforms to the Arkansas Cadastral Mapping Standard. The digital cadastral data is provided as a publication version that only represents a snapshot of the production data at the time it was received from the county. Published updates may be made to counties throughout the year. These will occur after new data is digitized or updates to existing data are finished. Production versions of the data exist in the various counties where daily and weekly updates occur. Users should consult the BEGIN_DATE attribute column to determine the age of the data for a given county. This column reflects the date when AGIO received the data from the county. Only parcels with an associated Computer Assisted Mass Appraisal (CAMA) record are provided. This means a CAMA record may exist, but no point geometry or vice-versa. Cadastral data is dynamic by its nature; therefore it is impossible for any county to ever be considered complete. The data is NOT topologically enforced. As a statewide integrator, AGIO publishes the data but does not make judgment calls about where points or polygon lines are meant to be located. Therefore each county data set is published without topology rules being enforced. GIS Technicians use best practices such as polygon closure and vertex snapping, however, topology is not built for each county. Users should be aware, by Arkansas Law (15-21-504 2 B) digital cadastral data does not represent legal property boundary descriptions, nor is it suitable for boundary determination of the individual parcels included in the cadastre. Users requiring a boundary determination should consult an Arkansas Registered Land Surveyor (http://www.arkansas.gov/pels/search/search.php) on boundary questions. The digital cadastral data is intended to be a graphical representation of the tax parcel only. Just because a county is listed does NOT imply the data represents county wide coverage. AGIO worked with each county to determine a level of production that warranted the data was ready to be published. For example, in some counties only the north part of the county was covered or in other cases only rural parcels are covered and yet in others only urban parcels. The approach is to begin incremental publishing as production blocks are ready, even though a county may not have county wide coverage. Each case represents a significant amount of data that will be useful immediately. Users should consult the BEGIN_DATE attribute column to determine the age of the data for a given county. This date reflects when the data was received from the county. Digital cadastral data users should be aware the County Assessor Mapping Program adopted a phased approach for developing cadastral data. Phase One includes the production of a parcel centroid for each parcel that bears the attributes prescribed by the state cadastral mapping standard. Phase Two includes the production of parcel polygon geometry and bears the standard attributes. The Arkansas standard closely mirrors the federal Cadastral Core Data Standard established by the Federal Geographic Data Committee, Subcommittee for Cadastral Data. Counties within this file include: Arkansas, Ashley, Baxter, Boone, Carroll, Chicot, Clark, Clay, Columbia, Conway, Craighead, Crawford, Cross, Desha, Faulkner, Franklin, Hot Spring, Howard, Izard, Jackson, Jefferson, Lafayette, Lincoln, Little River, Logan, Lonoke, Madison, Mississippi, Montgomery, Nevada, Newton, Perry, Pike, Poinsett, Polk, Pope, Pulaski, Randolph, Saline, Sebastian, Stone, Van Buren, Washington and White.; abstract: This dataset contains point features representing the approximate location of tax parcels contained in County Assessor tax rolls. Individual county data was integrated into this statewide publication by the Arkansas Geographic Information Office (AGIO). The Computer Aided Mass Appraisal (CAMA) systems maintained in each county are used to populate the database attributes for each centroid feature. The entity attribute structure conforms to the Arkansas Cadastral Mapping Standard. The digital cadastral data is provided as a publication version that only represents a snapshot of the production data at the time it was received from the county. Published updates may be made to counties throughout the year. These will occur after new data is digitized or updates to existing data are finished. Production versions of the data exist in the various counties where daily and weekly updates occur. Users should consult the BEGIN_DATE attribute column to determine the age of the data for a given county. This column reflects the date when AGIO received the data from the county. Only parcels with an associated Computer Assisted Mass Appraisal (CAMA) record are provided. This means a CAMA record may exist, but no point geometry or vice-versa. Cadastral data is dynamic by its nature; therefore it is impossible for any county to ever be considered complete. The data is NOT topologically enforced. As a statewide integrator, AGIO publishes the data but does not make judgment calls about where points or polygon lines are meant to be located. Therefore each county data set is published without topology rules being enforced. GIS Technicians use best practices such as polygon closure and vertex snapping, however, topology is not built for each county. Users should be aware, by Arkansas Law (15-21-504 2 B) digital cadastral data does not represent legal property boundary descriptions, nor is it suitable for boundary determination of the individual parcels included in the cadastre. Users requiring a boundary determination should consult an Arkansas Registered Land Surveyor (http://www.arkansas.gov/pels/search/search.php) on boundary questions. The digital cadastral data is intended to be a graphical representation of the tax parcel only. Just because a county is listed does NOT imply the data represents county wide coverage. AGIO worked with each county to determine a level of production that warranted the data was ready to be published. For example, in some counties only the north part of the county was covered or in other cases only rural parcels are covered and yet in others only urban parcels. The approach is to begin incremental publishing as production blocks are ready, even though a county may not have county wide coverage. Each case represents a significant amount of data that will be useful immediately. Users should consult the BEGIN_DATE attribute column to determine the age of the data for a given county. This date reflects when the data was received from the county. Digital cadastral data users should be aware the County Assessor Mapping Program adopted a phased approach for developing cadastral data. Phase One includes the production of a parcel centroid for each parcel that bears the attributes prescribed by the state cadastral mapping standard. Phase Two includes the production of parcel polygon geometry and bears the standard attributes. The Arkansas standard closely mirrors the federal Cadastral Core Data Standard established by the Federal Geographic Data Committee, Subcommittee for Cadastral Data. Counties within this file include: Arkansas, Ashley, Baxter, Boone, Carroll, Chicot, Clark, Clay, Columbia, Conway, Craighead, Crawford, Cross, Desha, Faulkner, Franklin, Hot Spring, Howard, Izard, Jackson, Jefferson, Lafayette, Lincoln, Little River, Logan, Lonoke, Madison, Mississippi, Montgomery, Nevada, Newton, Perry, Pike, Poinsett, Polk, Pope, Pulaski, Randolph, Saline, Sebastian, Stone, Van Buren, Washington and White.

FOR non-AGOL ACCOUNT HOLDERS, DOWNLOAD THIS GEOSPATIAL DATA HERE: https://gis-fws.opendata.arcgis.com/search?tags=lmvjvThese boundaries are simplified from the U.S. Fish and Wildlife Service Real Estate Interest data layer containing polygons representing tracts of land (parcels) in which the Service has a real estate interest. Interior boundaries between parcels were dissolved to produce a single set of simplified external boundaries for each feature. These are resource grade mapping representations of the U.S. Fish and Wildlife Service boundaries. For legal descriptions of the land represented here, contact the USFWS Realty Office. This map layer was compiled by the U.S. Fish and Wildlife Service. Although these boundaries represent lands administered by the U.S. Fish and Wildlife Service, not all areas are open to the public. Some fragile habitats need to be protected from human traffic and some management areas are closed. The public is urged to contact specific Refuges or other conservation areas before visiting.

Property Viewing Software Market Outlook

The global property viewing software market size was valued at approximately USD 2.1 billion in 2023 and is expected to reach around USD 5.4 billion by 2032, growing at a compound annual growth rate (CAGR) of 11.2% during the forecast period. This significant growth is driven by the increasing demand for innovative real estate solutions that enhance customer experience and streamline property management. The rise of digital transformation in the real estate industry, coupled with advancements in virtual reality (VR) and augmented reality (AR), is significantly contributing to the market's expansion.

One of the primary growth factors of the property viewing software market is the increasing adoption of digital tools among real estate agents and property managers. As the real estate sector becomes more competitive, professionals are leveraging advanced software solutions to offer virtual tours, schedule viewings, and provide detailed property information to potential buyers and tenants. This shift towards digitalization not only improves operational efficiency but also enhances customer engagement and satisfaction, thereby driving market growth.

Additionally, the COVID-19 pandemic has acted as a catalyst for the adoption of property viewing software. Social distancing measures and travel restrictions have led to a surge in demand for virtual property viewings. Homebuyers and tenants are increasingly relying on virtual tours to explore properties from the comfort and safety of their homes. This trend is expected to continue post-pandemic, as the convenience and efficiency of virtual viewings become a standard practice in the real estate industry.

Technological advancements in VR and AR are also playing a crucial role in the growth of the property viewing software market. These technologies enable realistic and immersive virtual tours, providing potential buyers with a comprehensive understanding of the property layout and features. As these technologies become more sophisticated and accessible, the adoption of property viewing software is expected to increase further, driving market growth during the forecast period.

From a regional perspective, the North American market is anticipated to hold a significant share due to the early adoption of advanced technologies and the presence of major market players. Europe is also expected to witness substantial growth, driven by increasing digitalization initiatives in the real estate sector. The Asia Pacific region is projected to experience the highest growth rate, supported by rapid urbanization and the growing real estate market in countries such as China and India.

Component Analysis

The property viewing software market is segmented by component into software and services. The software segment is expected to dominate the market, driven by the increasing demand for advanced and user-friendly property viewing solutions. These software solutions enable real estate agents and property managers to create virtual tours, schedule viewings, and manage property information efficiently. The integration of AI and machine learning in property viewing software is further enhancing its capabilities, making it an indispensable tool for real estate professionals.

Within the software segment, 3D virtual tour software is gaining significant traction. This type of software allows for the creation of immersive and interactive property tours, providing potential buyers with a detailed view of the property without the need for physical visits. The rising popularity of VR and AR technologies is further boosting the demand for 3D virtual tour software, contributing to the growth of the software segment.

On the other hand, the services segment includes implementation, consulting, and support services. These services are essential for the successful deployment and maintenance of property viewing software. As the adoption of these software solutions grows, the demand for professional services to ensure seamless integration and optimization is also expected to increase. This segment is likely to witness steady growth during the forecast period, driven by the need for technical support and consultancy services.

Moreover, the trend towards Software as a Service (SaaS) models is gaining momentum in the property viewing software market. SaaS-based solutions offer several benefits, including lower upfront costs, scalability, and ease of access. These advantages are encouraging real estate professionals to opt for cloud-based pr

Geospatial data about Benton County, Arkansas Parcels. Export to CAD, GIS, PDF, CSV and access via API.

The FinTech in Real Estate market is experiencing robust growth, driven by increasing demand for digital solutions and automation across the real estate value chain. This market is projected to reach a substantial size, with a Compound Annual Growth Rate (CAGR) fueling significant expansion over the forecast period. While precise figures for market size and CAGR are absent from the provided data, considering the involvement of major players like Zillow Group, Opendoor, and Fiserv, and the rapid adoption of PropTech solutions, a reasonable estimate would place the 2025 market size at approximately $50 billion, with a CAGR of 15% from 2025 to 2033. This growth is fueled by several key drivers: the increasing preference for online property searches and transactions, the rising popularity of crowdfunding and alternative financing models in real estate, the adoption of blockchain technology for secure and transparent property transactions, and the proliferation of sophisticated data analytics for improved investment decisions. Several trends are shaping the market, including the integration of artificial intelligence (AI) and machine learning (ML) for property valuation and risk assessment, the increasing use of virtual and augmented reality (VR/AR) for property viewing and design, and the expansion of PropTech solutions into underserved markets. However, challenges such as regulatory hurdles, data security concerns, and the need for widespread digital literacy among consumers act as restraints on market growth. The market is segmented across various functionalities, including property search, mortgage applications, property management, and investment platforms. Companies such as those listed are actively competing in this dynamic landscape, continuously innovating to capture market share and enhance their service offerings. This suggests that strong future expansion is highly likely.

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .

This dataset provides information about the number of properties, residents, and average property values for Stonehenge Cove cross streets in North Little Rock, AR.

The virtual staging market is experiencing robust growth, driven by the increasing adoption of technology in the real estate sector and the rising demand for cost-effective and time-efficient property marketing solutions. The market size in 2025 is estimated at $1120.4 million. While the exact CAGR is not provided, considering the rapid technological advancements and expanding applications within real estate, a conservative estimate of the Compound Annual Growth Rate (CAGR) for the forecast period (2025-2033) would be around 15-20%. This growth is fueled by several key drivers: the increasing preference for online property viewings, the convenience and affordability of virtual staging compared to traditional methods, and the expanding use of virtual staging across various segments, including realtors, developers, and other businesses. Key trends include the integration of virtual reality (VR) and augmented reality (AR) technologies for immersive experiences, the rise of AI-powered staging solutions that automate and personalize the process, and an increased focus on creating high-quality, realistic virtual tours. While some restraints exist, such as the initial investment required for software and hardware, and the potential for technical glitches, the overall market outlook remains highly positive. The market is segmented by delivery method (Cloud-Based and Web-Based) and application (Realtors, Developers, and Others), with the cloud-based segment experiencing faster growth due to its scalability and accessibility. Geographical expansion is also contributing significantly to the market's expansion, with North America and Europe currently dominating, but with strong growth potential in the Asia-Pacific region driven by rising internet penetration and urbanization. The projected growth trajectory indicates significant opportunities for companies operating in the virtual staging space. Strategic alliances and partnerships are likely to increase, as companies collaborate to leverage complementary technologies and expand their market reach. The focus on improving user experience, integrating advanced visualization techniques, and expanding into new geographical markets will be critical success factors for market participants in the coming years. The continued evolution of virtual and augmented reality technologies promises to further enhance the realism and immersive quality of virtual staging, leading to broader adoption and increased market penetration across various segments and regions. This growth will translate into a substantial market value by 2033, significantly exceeding the 2025 figure, driven by both the expanding user base and the ongoing technological innovations within the industry.

This dataset provides information about the number of properties, residents, and average property values for Clancy Court cross streets in Little Rock, AR.

Springs and sinkholes in the Ozark Plateaus Physiographic Province (Ozarks) in Arkansas were digitized from 1:24,000 scale topographic maps to produce a digital dataset of karst features. Karst landscapes generally are created from bedrock dissolution that results in distinctive landforms, including sinkholes, springs, caves, and sinking streams, and a high degree of interaction between surface water and groundwater. The dataset can be used to better understand groundwater flow in the karst landscape of the Arkansas Ozarks and potential effects of karst-feature density on water quality, geomorphology, water resources, and karst hazards. In the Ozarks, karst features are present in several limestone and dolomite formations (for example, the Boone Formation, Pitkin Limestone, and Powell Dolomite). Springs (points) and sinkholes (polygons and centroid points) were digitized from over 200 topographic quadrangle maps from 22 different counties with published dates ranging from 1942 to 2014. The digitization efforts using the topographic maps resulted in 805 springs and 1,242 sinkholes across the Arkansas Ozarks. Topographic maps were the only source of data used to provide an unbiased distribution over the Ozarks in Arkansas. This karst-feature dataset will be a resource for years to come in karst science, water science, geomorphology, and other fields.

This dataset provides information about the number of properties, residents, and average property values for Tanglewood Drive cross streets in Springdale, AR.

This dataset provides information about the number of properties, residents, and average property values for 49th Street cross streets in Fort Smith, AR.

Map layer giving the boundaries of the post OD Land Management teams. In MyMap this is layer at Operational Layers/Resource Management Terrestrial/Land Management Boundaries. It is the post OD (Organisational Design) boundaries for the Land and Assets teams. Will be used to ensure enquiries, incidents etc are passed to the correct team and to show geographical areas of teams’ responsibilities.

These data are part of a larger USGS project to develop an updated geospatial database of mines, mineral deposits and mineral regions in the United States. Mine and prospect-related symbols, such as those used to represent prospect pits, mines, adits, dumps, tailings, etc., hereafter referred to as “mine” symbols or features, are currently being digitized on a state-by-state basis from the 7.5-minute (1:24,000-scale) and the 15-minute (1:48,000 and 1:62,500-scale) archive of the USGS Historical Topographic Maps Collection, or acquired from available databases (California and Nevada, 1:24,000-scale only). Compilation of these features is the first phase in capturing accurate locations and general information about features related to mineral resource exploration and extraction across the U.S. To date, the compilation of 500,000-plus point and polygon mine symbols from approximately 67,000 maps of 22 western states has been completed: Arizona (AZ), Arkansas (AR), California (CA), Colorado (CO), Idaho (ID), Iowa (IA), Kansas (KS), Louisiana (LA), Minnesota (MN), Missouri (MO), Montana (MT), North Dakota (ND), Nebraska (NE), New Mexico (NM), Nevada (NV), Oklahoma (OK), Oregon (OR), South Dakota (SD), Texas (TX), Utah (UT), Washington (WA), and Wyoming (WY). The process renders not only a more complete picture of exploration and mining in the western U.S., but an approximate time line of when these activities occurred. The data may be used for land use planning, assessing abandoned mine lands and mine-related environmental impacts, assessing the value of mineral resources from Federal, State and private lands, and mapping mineralized areas and systems for input into the land management process. The data are presented as three groups of layers based on the scale of the source maps. No reconciliation between the data groups was done.

Mineral resource occurrence data covering the world, most thoroughly within the U.S. This database contains the records previously provided in the Mineral Resource Data System (MRDS) of USGS and the Mineral Availability System/Mineral Industry Locator System (MAS/MILS) originated in the U.S. Bureau of Mines, which is now part of USGS. The MRDS is a large and complex relational database developed over several decades by hundreds of researchers and reporters. While database records describe mineral resources worldwide, the compilation of information was intended to cover the United States completely, and its coverage of resources in other countries is incomplete. The content of MRDS records was drawn from reports previously published or made available to USGS researchers. Some of those original source materials are no longer available. The information contained in MRDS was intended to reflect the reports used as sources and is current only as of the date of those source reports. Consequently MRDS does not reflect up-to-date changes to the operating status of mines, ownership, land status, production figures and estimates of reserves and resources, or the nature, size, and extent of workings. Information on the geological characteristics of the mineral resource are likely to remain correct, but aspects involving human activity are likely to be out of date.