Field descriptions for the James City County Parcel layer and the Data table.

City Property data is maintained by the Cadastral Team in GeoMedia. On a monthly basis, a shapefile is provided is copied to the GIS.Base layer for use by ArcGIS users in CGIS.

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Geographic Information System Analytics Market Size 2024-2028

The geographic information system analytics market size is forecast to increase by USD 12 billion at a CAGR of 12.41% between 2023 and 2028.

The GIS Analytics Market analysis is experiencing significant growth, driven by the increasing need for efficient land management and emerging methods in data collection and generation. The defense industry's reliance on geospatial technology for situational awareness and real-time location monitoring is a major factor fueling market expansion. Additionally, the oil and gas industry's adoption of GIS for resource exploration and management is a key trend. Building Information Modeling (BIM) and smart city initiatives are also contributing to market growth, as they require multiple layered maps for effective planning and implementation. The Internet of Things (IoT) and Software as a Service (SaaS) are transforming GIS analytics by enabling real-time data processing and analysis.
Augmented reality is another emerging trend, as it enhances the user experience and provides valuable insights through visual overlays. Overall, heavy investments are required for setting up GIS stations and accessing data sources, making this a promising market for technology innovators and investors alike.

What will be the Size of the GIS Analytics Market during the forecast period?

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The geographic information system analytics market encompasses various industries, including government sectors, agriculture, and infrastructure development. Smart city projects, building information modeling, and infrastructure development are key areas driving market growth. Spatial data plays a crucial role in sectors such as transportation, mining, and oil and gas. Cloud technology is transforming GIS analytics by enabling real-time data access and analysis. Startups are disrupting traditional GIS markets with innovative location-based services and smart city planning solutions. Infrastructure development in sectors like construction and green buildings relies on modern GIS solutions for efficient planning and management. Smart utilities and telematics navigation are also leveraging GIS analytics for improved operational efficiency.
GIS technology is essential for zoning and land use management, enabling data-driven decision-making. Smart public works and urban planning projects utilize mapping and geospatial technology for effective implementation. Surveying is another sector that benefits from advanced GIS solutions. Overall, the GIS analytics market is evolving, with a focus on providing actionable insights to businesses and organizations.

How is this Geographic Information System Analytics Industry segmented?

The geographic information system analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Retail and Real Estate
  Government
  Utilities
  Telecom
  Manufacturing and Automotive
  Agriculture
  Construction
  Mining
  Transportation
  Healthcare
  Defense and Intelligence
  Energy
  Education and Research
  BFSI


Components

  Software
  Services


Deployment Modes

  On-Premises
  Cloud-Based


Applications

  Urban and Regional Planning
  Disaster Management
  Environmental Monitoring Asset Management
  Surveying and Mapping
  Location-Based Services
  Geospatial Business Intelligence
  Natural Resource Management


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    South Korea


  Middle East and Africa

    UAE


  South America

    Brazil


  Rest of World

By End-user Insights

The retail and real estate segment is estimated to witness significant growth during the forecast period.

The GIS analytics market analysis is witnessing significant growth due to the increasing demand for advanced technologies in various industries. In the retail sector, for instance, retailers are utilizing GIS analytics to gain a competitive edge by analyzing customer demographics and buying patterns through real-time location monitoring and multiple layered maps. The retail industry's success relies heavily on these insights for effective marketing strategies. Moreover, the defense industries are integrating GIS analytics into their operations for infrastructure development, permitting, and public safety. Building Information Modeling (BIM) and 4D GIS software are increasingly being adopted for construction project workflows, while urban planning and designing require geospatial data for smart city planning and site selection.

The oil and gas industry is leveraging satellite imaging and IoT devices for land acquisition and mining operations. In the public sector, gover

Description: This dataset, created by the Town of Cary GIS Group, contains real estate data from the western portion of Wake County, the eastern portion of Chatham County, and the southern portion of Durham County. The data has been modified from its original sources to combine these areas into one cohesive layer for use by the Town of Cary. It is updated monthly based on the latest data from the respective counties.

For the most current information, please refer to the individual county data sources:

Wake County Data
Durham County Data
Chatham County Data

This dataset serves as a valuable resource for property analysis and urban planning in the specified regions, providing consistent, updated data for public use.

This is a comprehensive collection of tax and assessment data extracted at a specific time. The data is in CSV format. A data dictionary (pdf) and the current tax rate book (pdf) are also included.

This repository contains data and codes that support the findings of the study.- PPD-EPC open dataset with the enriched spatial analyses scores and UPRN.- Batch Geocoding Notebook of PPD-EPC dataset with GeoPy - Here API- PyQGIS codes for proximity, terrain, and visibility spatial analyses.- Jupyter Notebook of Machine Learning algorithms for mass property valuation.

This dataset has been published by the Office of the Real Estate Assessor of the City of Virginia Beach and data.virginiabeach.gov. The mission of data.virginiabeach.gov is to provide timely and accurate City information to increase government transparency and access to useful and well organized data by the general public, non-governmental organizations, and City of Virginia Beach employees.

James City County DataCombination of parcel information from the GIS/Mapping and the Real Estate departments.This table does not included multiple improvements per parcel.There is only 1 record per parcel IDAlso download the GIS and Real Estate Data Field Description.pdf file for a list of field descriptions.This data is updated every night

Context

I'm trying to make a Choropleth map over time of home sale prices by block in Brooklyn for the last 15 years to visualize gentrification. I have the entire dataset for all 5 boroughs of New York, but am starting with Brooklyn.

Content and Acknowledgements

Primary dataset is the NYC Housing Sales Data Found in this Link: http://www1.nyc.gov/site/finance/taxes/property-rolling-sales-data.page

The data in all the separate excel spreadsheets for 2003-2017 was merged via VBA scripting in Excel and further cleaned & de-duped in R

Additionally, in my hunt for shapefiles I discovered these wonderful shapefiles from NYCPluto: https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page

I left joined it by "Block" & "Lot" onto the primary data frame, but 25% of the block/lot combo's ended up not having a corresponding entry in the Pluto shapefile and are NAs.

Note that as in other uploaded datasets of NYC housing on Kaggle, many of these transactions have a sale_price of $0 or only a nominal amount far less than market value. These are likely property transfers to relatives and should be excluded from any analysis of market prices.

Inspiration

Can you model Brooklyn home prices accurately?

Access Arkansas's 61 data folders with 315 services and 1,625 layers of parcel boundaries, property tax records, and GIS mapping data.

This dataset is created by the Town of Cary GIS Group. It contains data from the western portion of Wake County, the eastern portion of Chatham County, and the southern portion of Durham County. It has been modified from the original sources to act as one layer for use by the Town of Cary.

This file is updated once a month from the respective sources. Please refer to each Counties' data for the latest information:

• Wake County
• Durham County
• Chatham County

Air right lots are reflect a party’s right to construct an improvement above an existing area of land that is not owned by the constructor. They are a type of development right in real estate referring to the empty space about a property. These tax lot numbers start at 7000. There are approximately 704 air rights lots. Non-contiguous Air Rights Lots numbered in 8000 series can either be District owned Multifamily rental units or Existing Development Mixed (residential and commercial).Multifamily 8000 series lots can be proposed development projects that are inclusive of the Mayor’s Office Affordable/Public Housing Initiatives. Additionally, they can either be development sites that are owned by the District and the site is leased to developer. Due to financing and legal requirements, each set of government funded units are required to have separate parcel ID’s (SSL’s). All the units are rentals, none of the units will be for sale.Existing Development Mixed Use 8000 series lots are residential owner(s) that own both residential and commercial portions. The Lot split is done to ensure each party pays the appropriate real estate taxes assessed to each specific use. There is a master covenant lease outlining property access-rights-use between residential and commercial owner and lease holders. There is also a master lease related to the commercial space where the residential owner is the lease holder.

Access Washington's 206 data folders with 2,104 services and 5,824 layers of parcel boundaries, property tax records, and GIS mapping data.

Access New Jersey's 45 data folders with 290 services and 832 layers of parcel boundaries, property tax records, and GIS mapping data.

Like other Assessor and Recorder data sets from First American, BlackKnight, ATTOM or HouseCanary, we provide both residential real estate and commercial restate data on homes, properties and pracels nationally.

Over 250M parcels, updated daily.

Access detailed property and tax assessment records with our extensive nationwide database. This robust dataset provides comprehensive information about residential and commercial properties, including detailed ownership, valuation, and transaction history. Core Data Elements:

Complete property identification (APNs, Tax IDs) Full property addresses with geocoding Precise latitude/longitude coordinates FIPS codes and Census tract information School district assignments

Property Characteristics:

Detailed lot dimensions and size Building square footage breakdowns Living area measurements Basement and attic specifications Garage and parking information Year built and effective year Number of bedrooms and bathrooms Room counts and configurations Building class and condition codes Construction details and materials Property amenities and features

Valuation Information:

Current AVM (Automated Valuation Model) values Confidence scores and value ranges Market valuations with dates Assessed values (land and improvements) Tax amounts and years Tax rate codes and districts Various tax exemption statuses

Transaction History:

Current and previous sale details Recording dates and document numbers Sale prices and price codes Buyer and seller information Multiple mortgage records including:

Loan amounts and terms Lender information Recording dates Interest rates Due dates Loan types and positions

Ownership Details:

Current owner information Corporate ownership indicators Owner-occupied status Mailing addresses Care of names Foreign address indicators

Legal Information:

Complete legal descriptions Subdivision details Lot and block numbers Zoning information Land use codes HOA information and fees

Property Status Indicators:

Vacancy flags Pre-foreclosure status Current listing status Price ranges Market position

Perfect For:

Real Estate Professionals

Property researchers Title companies Real estate attorneys Appraisers Market analysts

Financial Services

Mortgage lenders Insurance companies Investment firms Risk assessment teams Portfolio managers

Government & Planning

Urban planners Tax assessors Economic developers Policy researchers Municipal agencies

Data Analytics

Market researchers Data scientists Economic analysts GIS specialists Demographics experts

Data Delivery Features:

Multiple format options Regular updates Bulk download capability Custom field selection Geographic filtering API access available Standardized formatting Quality assured data

Quality Assurance:

Verified against public records Regular updates Standardized formatting Address verification Geocoding validation Duplicate removal Data normalization Quality control processes

This comprehensive property database provides unprecedented access to detailed property information, perfect for industry professionals requiring in-depth property data for analysis, research, or business development. Our data undergoes rigorous quality control processes to ensure accuracy and completeness, making it an invaluable resource for real estate professionals, financial institutions, and government agencies. Updated continuously from authoritative sources, this dataset offers the most current and accurate property information available in the market. Custom data extracts and specific geographic coverage options are available to meet your exact needs.

Weekly/Quarterly/Annual and One-time options are available for sale.

See our sample

This dataset visualises the spatial distribution of the rental value in Amsterdam between 1647 and 1652. The source of rental value comes from the Verponding registration in Amsterdam. The verponding or the ‘Verpondings-quohieren van den 8sten penning’ was a tax in the Netherlands on the 8^th penny of the rental value of immovable property that had to be paid annually. In Amsterdam, the citywide verponding registration started in 1647 and continued into the early 19^th century. With the introduction of the cadastre system in 1810, the verponding came to an end.

The original tax registration is kept in the Amsterdam City Archives (Archief nr. 5044) and the four registration books transcribed in this dataset are Archief 5044, inventory 255, 273, 281, 284. The verponding was collected by districts (wijken). The tax collectors documented their collecting route by writing down the street or street-section names as they proceed. For each property, the collector wrote down the names of the owner and, if applicable, the renter (after ‘per’), and the estimated rental value of the property (in guilders). Next to the rental value was the tax charged (in guilders and stuivers). Below the owner/renter names and rental value were the records of tax payments by year.

This dataset digitises four registration books of the verponding between 1647 and 1652 in two ways. First, it transcribes the rental value of all real estate properties listed in the registrations. The names of the owners/renters are transcribed only selectively, focusing on the properties that exceeded an annual rental value of 300 guilders. These transcriptions can be found in Verponding1647-1652.csv. For a detailed introduction to the data, see Verponding1647-1652_data_introduction.txt.

Second, it geo-references the registrations based on the street names and the reconstruction of tax collectors’ travel routes in the verponding. The tax records are then plotted on the historical map of Amsterdam using the first cadaster of 1832 as a reference. Since the geo-reference is based on the street or street sections, the location of each record/house may not be the exact location but rather a close proximation of the possible locations based on the street names and the sequence of the records on the same street or street section. Therefore, this geo-referenced verponding can be used to visualise the rental value distribution in Amsterdam between 1647 and 1652. The preview below shows an extrapolation of rental values in Amsterdam. And for the geo-referenced GIS files, see Verponding_wijken.shp.

GIS specifications:

Coordination Reference System (CRS): Amersfoort/RD New (ESPG:28992)

Historical map tiles URL (From Amsterdam Time Machine)

NB: This verponding dataset is a provisional version. The georeferenced points and the name transcriptions might contain errors and need to be treated with caution.

Contributors

• Historical and archival research: Weixuan Li, Bart Reuvekamp
• Plotting of geo-referenced points: Bart Reuvekamp
• Spatial analysis: Weixuan Li
• Mapping software: QGIS
• Acknowledgements: Virtual Interiors project, Daan de Groot

Access Texas's 383 data folders with 4,999 services and 11,137 layers of parcel boundaries, property tax records, and GIS mapping data.

Real Estate Information - Parcel Level Data. Only active parcels are included in this data set. The "ParcelNumber" field can be joined to the "ParcelNumber" field in the "Parcel Area Details" data set for mapping purposes.Please refer to this Data Guide for details on how to access and join Real Estate data

This is a comprehensive collection of tax and assessment data extracted at a specific time. The data is in CSV format. A data dictionary (pdf) and the current tax rate book (pdf) are also included.

This is a comprehensive collection of tax and assessment data extracted at a specific time. The data is in CSV format. A data dictionary (pdf) and the current tax rate book (pdf) are also included.