In the realm of real estate data solutions, BatchData Property Data Search API emerges as a technical marvel, tailored for product and engineering leadership seeking robust and scalable solutions. This purpose-built API seamlessly integrates diverse datasets, offering over 600 data points, to provide a holistic view of property characteristics, valuation, homeowner information, listing data, county assessor details, photos, and foreclosure information. With state-of-the-art infrastructure and performance features, BatchData sets the standard for efficiency, reliability, and developer satisfaction.

Unraveling the Technical Prowess of BatchData Property Data Search API:

State-of-the-Art Infrastructure: At the heart of BatchData lies a state-of-the-art infrastructure that leverages the latest technologies available. Our systems are engineered to handle increased loads and growing datasets with ease, ensuring optimal performance without significant degradation. This commitment to technological advancement ensures that our data infrastructure and API systems operate at peak efficiency, even in the face of evolving demands and complexities.

Integration Capabilities: BatchData boasts integration capabilities that are second to none, thanks to our innovative data lake house architecture. This architecture empowers us to seamlessly integrate our data with any data platforms or pipelines in a matter of minutes. Whether it's connecting with existing data systems, third-party applications, or internal pipelines, our API offers limitless integration possibilities, enabling product and engineering teams to unlock the full potential of property data with minimal effort.

Developer Documentation: One of the hallmarks of BatchData is our clear and comprehensive developer documentation, which developers love. We understand the importance of providing developers with the resources they need to integrate our API seamlessly into their projects. Our documentation offers detailed guides, code samples, API reference materials, and best practices, empowering developers to hit the ground running and leverage the full capabilities of BatchData with confidence.

Performance Features: BatchData Property Search API is engineered for performance, delivering lightning-fast response times and seamless scalability. Our API is designed to efficiently handle increased loads and growing datasets, ensuring that users experience minimal latency and maximum reliability. Whether it's retrieving property data, conducting complex queries, or accessing real-time updates, our API delivers exceptional performance, empowering product and engineering teams to build high-performance applications and systems with ease. BatchData's APIs work for both residential real estate data and commercial real estate data.

Common Use Cases for BatchData Property Data Search API:

Powering Data-Driven Applications: Product and engineering teams can leverage BatchData Property Data Search API to power data-driven applications tailored for the real estate industry. Whether it's building real estate websites, mobile applications, or internal tools, our API offers comprehensive property data that can drive informed decision-making, enhance user experiences, and streamline operations.

Enabling Advanced Analytics: With BatchData, product and engineering leaders can unlock the power of advanced analytics and reporting capabilities. Our API provides access to rich property data, enabling analysts and researchers to uncover insights, identify trends, and make data-driven recommendations with confidence. Whether it's analyzing market trends, evaluating investment opportunities, or conducting competitive analysis, BatchData empowers teams to derive actionable insights from vast property datasets.

Optimizing Data Infrastructure: BatchData Property Data Search API can play a pivotal role in optimizing data infrastructure within organizations. By seamlessly integrating our API with existing data platforms and pipelines, product and engineering teams can streamline data workflows, improve data accessibility, and enhance overall data infrastructure efficiency. Our API's integration capabilities and performance features ensure that organizations can leverage property data seamlessly across their data ecosystem, driving operational excellence and innovation.

Conclusion: BatchData Property Data Search API stands at the forefront of real estate data solutions, offering product and engineering leaders a comprehensive, scalable, and high-performance API for accessing property data. With state-of-the-art infrastructure, seamless integration capabilities, clear developer documentation, and exceptional performance features, BatchData empowers teams to build data-driven applications, optimize data infrastructure, and unlock actionable insights with ease. As the real estate industry continues to evolve, BatchData remains committed to delivering innovative sol...

Get valuable legal information effortlessly with APISCRAPY's services – USA Court Data, USA Litigation Data, and US County Legal Data. Our user-friendly offerings include a handy Court Data API, providing you with all the legal details you need for your decision-making.

WONDER online databases include county-level Compressed Mortality (death certificates) since 1979; county-level Multiple Cause of Death (death certificates) since 1999; county-level Natality (birth certificates) since 1995; county-level Linked Birth / Death records (linked birth-death certificates) since 1995; state & large metro-level United States Cancer Statistics mortality (death certificates) since 1999; state & large metro-level United States Cancer Statistics incidence (cancer registry cases) since 1999; state and metro-level Online Tuberculosis Information System (TB case reports) since 1993; state-level Sexually Transmitted Disease Morbidity (case reports) since 1984; state-level Vaccine Adverse Event Reporting system (adverse reaction case reports) since 1990; county-level population estimates since 1970. The WONDER web server also hosts the Data2010 system with state-level data for compliance with Healthy People 2010 goals since 1998; the National Notifiable Disease Surveillance System weekly provisional case reports since 1996; the 122 Cities Mortality Reporting System weekly death reports since 1996; the Prevention Guidelines database (book in electronic format) published 1998; the Scientific Data Archives (public use data sets and documentation); and links to other online data sources on the "Topics" page.

This archive reproduces a figure titled "Figure 3.2 Boone County population distribution" from Wang and vom Hofe (2007, p.60). The archive provides a Jupyter Notebook that uses Python and can be run in Google Colaboratory. The workflow uses the Census API to retrieve data, reproduce the figure, and ensure reproducibility for anyone accessing this archive.The Python code was developed in Google Colaboratory, or Google Colab for short, which is an Integrated Development Environment (IDE) of JupyterLab and streamlines package installation, code collaboration, and management. The Census API is used to obtain population counts from the 2000 Decennial Census (Summary File 1, 100% data). Shapefiles are downloaded from the TIGER/Line FTP Server. All downloaded data are maintained in the notebook's temporary working directory while in use. The data and shapefiles are stored separately with this archive. The final map is also stored as an HTML file.The notebook features extensive explanations, comments, code snippets, and code output. The notebook can be viewed in a PDF format or downloaded and opened in Google Colab. References to external resources are also provided for the various functional components. The notebook features code that performs the following functions:install/import necessary Python packagesdownload the Census Tract shapefile from the TIGER/Line FTP Serverdownload Census data via CensusAPI manipulate Census tabular data merge Census data with TIGER/Line shapefileapply a coordinate reference systemcalculate land area and population densitymap and export the map to HTMLexport the map to ESRI shapefileexport the table to CSVThe notebook can be modified to perform the same operations for any county in the United States by changing the State and County FIPS code parameters for the TIGER/Line shapefile and Census API downloads. The notebook can be adapted for use in other environments (i.e., Jupyter Notebook) as well as reading and writing files to a local or shared drive, or cloud drive (i.e., Google Drive).

This GIS data set was derived from a variety of source documents, including plats of survey, subdivision plats, certified survey maps, records of deed and row plats. These documents were of varying scales and from dates ranging from the late 1940's to present. These maps provide a useful representation of the geometry of the tax parcels and is suitable for its intended purpose.This GIS data set was derived from a variety of source documents, including plats of survey, subdivision plats, certified survey maps, records of deed and row plats. These documents were of varying scales and from dates ranging from the late 1940's to present. These maps provide a useful representation of the geometry of the tax parcels and is suitable for its intended purpose. This data was created to serve as an inventory of property owners for use by various county departments to determine septic and building permitting, voter and tax information, planning, social services, and use for the general public.This data was created to serve as an inventory of property owners for use by various county departments to determine septic and building permitting, voter and tax information, planning, social services, and use for the general public.For more information https://gisdownload.buncombecounty.org/parcels.htm

If viewing this description on the Western Pennsylvania Regional Data Center’s open data portal (https://www.wprdc.org), this dataset is harvested on a weekly basis from Allegheny County’s GIS data portal (https://openac.alcogis.opendata.arcgis.com/). The full metadata record for this dataset can also be found on Allegheny County’s GIS portal. You can access the metadata record and other resources on the GIS portal by clicking on the “Explore” button (and choosing the “Go to resource” option) to the right of the “ArcGIS Open Dataset” text below.

Category: Civic Vitality and Governance

Organization: Allegheny County

Department: Geographic Information Systems Group; Department of Information Technology

Temporal Coverage: current

Data Notes:

Coordinate System: Pennsylvania State Plane South Zone 3702; U.S. Survey Foot

Development Notes: none

Other: none

Related Document(s): Data Dictionary (none)

Frequency - Data Change: As needed

Frequency - Publishing: As needed

Data Steward Name: Eli Thomas

Data Steward Email: gishelp@alleghenycounty.us

This data-set contains a link to Milwaukee County Transit System's Real-Time web page. Users will need to register for an account with MCTS to receive access to the API.

This layer shows health insurance coverage by type and by age group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percent uninsured. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B27010 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Demographic and socioeconomic data for North Carolina and all counties.

Real Property parcel characteristics for Allegheny County, PA. Includes information pertaining to land, values, sales, abatements, and building characteristics (if residential) by parcel. Disclaimer: Parcel information is provided from the Office of Property Assessments in Allegheny County. Content and availability are subject to change. Please review the Data Dictionary for details on included fields before each use. Property characteristics and values change due to a variety of factors such as court rulings, municipality permit processing and subdivision plans. Consequently the assessment system parcel data is continually changing. Please take the dynamic nature of this information into consideration before using it. Excludes name and contact information for property owners, as required by Ordinance 3478-07.

Orientation

The first two items listed below are slightly different versions of the most current property-assessments records. The first is optimized for faster download but has 1) a few fields (including PROPERTY_ZIP and MUNICODE) as integers instead of strings and 2) the date columns in two different formats. The second item downloads more slowly, is optimized for API queries, and has all dates in a standard YYYY-MM-DD format. Further down you can find useful links, documentation, and then archived versions of property assessments files.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2017, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS).

This layer shows Population. This is shown by state and county boundaries. This service contains the 2018-2022 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the point by Population Density and size of the point by Total Population. The size of the symbol represents the total count of housing units. Population Density was calculated based on the total population and area of land fields, which both came from the U.S. Census Bureau. Formula used for Calculating the Pop Density (B01001_001E/GEO_LAND_AREA_SQ_KM). To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2018-2022ACS Table(s): B01001, B09020Data downloaded from: Census Bureau's API for American Community Survey Date of API call: January 18, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

A list of all Montgomery County WiFi Hot Spots.

Update Frequency: Quarterly

Download In State Plane Projection Here. These address points were developed for a myriad of uses including general public geocoding in Lake County web sites and NG-911 emergency dispatch. All addresses are reviewed on a monthly basis against the United States Post Office delivery database to ensure that they are still active. New addresses are added at this time or earlier if they are made known to Lake County GIS by local data partners. Attributes DiscrpAgID through Elev reflect the NENA NG-911 / State of Illinois GIS Data model. Additional local data fields have also been included. The fields LSt_PreDir, LSt_Name, LSt_Type and LSt_PosDir are formatted according to United States Postal Service standards.Update Frequency:This dataset is updated on a weekly basis.

Washington State County Boundaries including Department of Natural Resources (DNR) county codes. This data is created from the WA Public Land Survey source data maintained by the DNR.WA County Boundaries Metadata

A log of dataset alerts open, monitored or resolved on the open data portal. Alerts can include issues as well as deprecation or discontinuation notices.

Wake County owned and operated parks.This dataset is updated when the parks owned by Wake County change. It is maintained by Wake County GIS.

The REST Web Services API provides a REST interface to retrieve Valuation Office data in real time in relation to post-Revaluation Rating Authority areas. The post-Revaluation Rating Authority areas are Carlow, Dun Laoghaire-Rathdown, Fingal, Kildare, Kilkenny, Laois, Leitrim, Longford, Offaly, Roscommon, Sligo, South Dublin and Westmeath County Councils and Dublin City Council, Waterford City and County Council and Limerick City and County Council.

The fields you can filter by are County/Local Authority, Category, Use, Area Per Floor, Floor Use, Address, Publication Date, Nav Total, Rateable Valuation, Level, Car Park, Property Number and X IG & Y IG. The data can be returned in CSV, JSON or GeoJSON. An API query URL can also be generated for reuse.

The Data Resource Preview below returns an example of all commercial and industrial properties in the “Carlow County Council” Rating Authority area that fall under the category of “Office” in JSON format.

The Valuation Office provides this API with the understanding that it is not guaranteed to be complete and is subject to change. The API is a work in progress and will be expanded further. We welcome your feedback, please submit suggestions to opendata@valoff.ie

This dataset is part of the Geographical repository maintained by Opendatasoft. This dataset contains data for counties and equivalent entities in United States of America. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities.Processors and tools are using this data. Enhancements Add ISO 3166-3 codes. Simplify geometries to provide better performance across the services. Add administrative hierarchy.

‘This dataset provides information regarding the total approved actual expenses incurred by Montgomery County government employees traveling non-locally (over 75 miles from the County’s Executive Office Building at 101 Monroe St. Rockville, MD) for official business, beginning on or after August 12, 2015. The dataset includes the name of traveling employee; the employee’s home department; travel start and end dates; destination; purpose of travel; and actual total expenses funded by the County. Update Frequency: Monthly