Demographic statistics broken down by zip code

The 2020 Zip Code Boundaries dataset contains boundary information for Zip Code Tabulation Areas (ZCTAs) as of the 2020 Census, sourced from the United States Census Bureau. ZCTAs are statistical representations of USPS ZIP Code service areas and are used for demographic analysis, data aggregation, and geographic reference purposes. This dataset includes boundary polygons for each ZCTA, allowing users to visualize and analyze ZIP Code boundaries within Montgomery County, Texas.Data Fields Included:ZCTA CodeAreaThis dataset is sourced from the United States Census Bureau.Data source: United States Census Bureau TIGER Data

The 2010 Zip Code Boundaries dataset contains boundary information for Zip Code Tabulation Areas (ZCTAs) as of the 2010 Census, sourced from the United States Census Bureau. ZCTAs are statistical representations of USPS ZIP Code service areas and are used for demographic analysis, data aggregation, and geographic reference purposes. This dataset includes boundary polygons for each ZCTA, allowing users to visualize and analyze ZIP Code boundaries within Montgomery County, Texas.Data Fields Included:ZCTA CodeAreaThis dataset is sourced from the United States Census Bureau.Data source: United States Census Bureau TIGER Data

This Zipcode GIS Layer is a spatial dataset that outlines the boundaries of ZIP code areas across York County, Pennsylvania. This layer is used in Geographic Information Systems (GIS) to support mapping, analysis, and decision-making based on location. Each ZIP code area is represented as a shape on the map and includes basic information such as the ZIP code, city, and state. This data is useful for a wide range of applications including business planning, public services, marketing, transportation, and emergency response. The Zipcode GIS Layer allows users to visualize and analyze geographic patterns, such as population distribution, service coverage, and regional trends. It can be used on its own or combined with other spatial data for more detailed studies.

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The Geospatial Imagery Analytics Marketsize was valued at USD 11.88 USD Billion in 2023 and is projected to reach USD 83.39 USD Billion by 2032, exhibiting a CAGR of 32.1 % during the forecast period.Geospatial analytics gathers, manipulates, and displays geographic information system (GIS) data and imagery including GPS and satellite photographs. Geospatial data analytics rely on geographic coordinates and specific identifiers such as street address and zip code. geospatial visualization enables businesses to better understand complex information and make informed decisions. They can quickly see patterns and trends and assess the impact of different variables by visualizing data in a spatial context. The field encompasses several techniques and algorithms, such as spatial interpolation, spatial regression, spatial clustering, and spatial autocorrelation analysis, which help extract insights from various geospatial data sources. The growing adoption of location-based services in various industries, including agriculture, defense, and urban planning, is driving the demand for geospatial imagery analytics. Recent developments include: August 2023: onX, a digital navigation company, partnered with Planet Labs PBC, a satellite imagery provider, to introduce a new feature called ‘Recent Imagery’. This feature offers onX app users updated satellite imagery maps every two weeks, enhancing the user experience across onX Hunt, onX Offroad, and onX Backcountry apps. This frequent data update helps outdoor enthusiasts access real-time information for safer and more informed outdoor activities., August 2023: Quant Data & Analytics, a provider of data products and enterprise solutions for real estate and retail, partnered with Satellogic Inc. to utilize Satellogic’s high-resolution satellite imagery to enhance property technology in Saudi Arabia and the Gulf region., April 2023: Astraea, a spatiotemporal data and analytics platform, introduced a new ordering service that grants customers scalable access to top-tier commercial satellite imagery from providers such as Planet Labs PBC and others., May 2022: Satellogic Inc. established a partnership with UP42. This geospatial developer platform enables direct access to Satellogic’s satellite tasking capabilities, including high-resolution multispectral and wide-area hyperspectral imagery, through the UP42 API-based platform., April 2022: TomTom International BV, a geolocation tech company, broadened its partnership with Maxar Technologies, a space solution provider. This expansion involves integrating high-resolution global satellite imagery from Maxar’s Vivid imagery base maps into TomTom’s product lineup, enhancing their visualization solutions for customers.. Key drivers for this market are: Growing Demand for Location-based Insights across Diverse Industries to Fuel Market Growth. Potential restraints include: Complexity and Cost Associated with Data Acquisition and Processing May Hamper Market Growth. Notable trends are: Growing Implementation of Touch-based and Voice-based Infotainment Systems to Increase Adoption of Intelligent Cars.

Now includes bridge to the 2016 census.

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Data is scraped from OpenWeather, National Weather Service, and Zip-Codes.com APIs to retrieve and display JSON weather information for U.S. cities. Additional information is scraped from the web and manipulated using the Beautiful Soup and Pandas libraries. | Column | Description | | --- | --- | | City | The name of the city. | | State | The state in which the city is located.. | |Date | The date on which the information was requested.| |Time| The time at which the information was requested.| |Weather | A general description of the weather at the current location.| | Current Temperature (Farenheit) |The current temperature of the location in Farenheit. | | High (Farenheit) |The current maximum recorded temperature at the current location.| |Low (Farenheit) | The current minimum recorded temperature at the current location.| | Atmospheric Pressure (hPa) | The atmospheric pressure of the current location. | |Humidity (Percentage) |The relative humidity of the current location. |

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Latest release of source code. A zip file of the source code from release 0.1.8. Accessed 4 May 2018. (ZIP 3513 kb)

** A Newer Version of this data is available here: https://dallasgis.maps.arcgis.com/home/item.html?id=0a2fde8aa7404187917488bafcbc77e6The United States Postal Service (USPS) does not define ZIP codes as fixed geographic boundaries, such as polygons on a map. Instead, ZIP codes are structured as collections of carrier routes designed to optimize mail delivery. These routes are established based on logistical considerations, such as population density, delivery efficiency, and infrastructure changes, rather than adhering to precise geographic outlines.When ZIP codes are mapped, the resulting visualization is essentially an estimation of these delivery routes. However, these approximations are inherently subject to change, as the Postal Service frequently adjusts routes to accommodate new developments, address shifts in demand, or enhance operational efficiency. Consequently, any representation of ZIP codes on a map should be understood as a general reference and not as an exact or permanent delineation.National ZipCodes: https://dallasgis.maps.arcgis.com/home/item.html?id=0a2fde8aa7404187917488bafcbc77e6

This dataset includes a directory with the data of the Post Offices of Zaragoza. Through the Visualisation Service, the City of Zaragoza provides a visualisation showing data on Post Offices. The dataset contains location information (address, postal code, latitude and longitude), contact (email and telephone), schedule and url to the website of each center. The search engine allows you to do searches by filtering by neighborhood. You can also see the results in the form of a map.

Abstract��� Combining lidar in camera-based simultaneous localization and mapping (SLAM) is an effective method in improving overall accuracy, especially at outdoor large scale scenes. Recent development of low-cost lidars (e.g. Livox lidar) enable us to explore such SLAM systems with lower budget and higher performance. In this paper we propose CamVox by adapting Livox lidars into visual SLAM (ORB-SLAM2) by exploring the lidars��� unique features. Based on the unique scan pattern of Livox lidars, we propose an automatic lidarcamera calibration method that will work in uncontrolled scenes. The long depth detection range also benefit a more accurate mapping. Comparison of CamVox with visual SLAM (VINS-mono) and lidar SLAM (LOAM) are evaluated on the same dataset to demonstrate the performance. We open sourced our hardware, code and dataset on GitHub. (https://github.com/ISEE-Technology/CamVox) This contains our dataset in SUSTech campus with loop closure (CamVox.bag) and the Lidar-camera Synchronization ARM(stm32) code (synchronization.zip ).

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