The Mobile Home Parks feature class/shapefile contains locations that represent mobile home, residential trailer, and recreational vehicle (RV) parks within the Continental United States and Alaska. The people residing in these housing types are the most vulnerable residential population to hurricanes, tornadoes, flooding and other natural disasters. According to the U.S. Census Bureau, 31% of the people that perished in tornadoes between 2009 and 2011 were residing in or fleeing from mobile homes. An inventory of mobile home park locations and the number of mobile homes within those parks is, therefore, essential for emergency preparedness, response, and evacuation. In the latest update 2281 records were added.

Expands the use of internal data for creating Geographic Information System (GIS) maps. SSA's Database Systems division developed a map users guide for GIS data object publishing and was made available in an internal Sharepoint site for access throughout the agency. The guide acts as the reference for publishers of GIS objects across the life-cycle in our single, central geodatabase implementation.

This resource is a member of a series. 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 All Roads Shapefile includes all features within the MTDB Super Class "Road/Path Features" distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB that begins with "S". This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, and stairways.

Detroit Street View (DSV) is an urban remote sensing program run by the Enterprise Geographic Information Systems (EGIS) Team within the Department of Innovation and Technology at the City of Detroit. The mission of Detroit Street View is ‘To continuously observe and document Detroit’s changing physical environment through remote sensing, resulting in freely available foundational data that empowers effective city operations, informed decision making, awareness, and innovation.’ LiDAR (as well as panoramic imagery) is collected using a vehicle-mounted mobile mapping system.

Due to variations in processing, index lines are not currently available for all existing LiDAR datasets, including all data collected before September 2020. Index lines represent the approximate path of the vehicle within the time extent of the given LiDAR file. The actual geographic extent of the LiDAR point cloud varies dependent on line-of-sight.

Compressed (LAZ format) point cloud files may be requested by emailing gis@detroitmi.gov with a description of the desired geographic area, any specific dates/file names, and an explanation of interest and/or intended use. Requests will be filled at the discretion and availability of the Enterprise GIS Team. Deliverable file size limitations may apply and requestors may be asked to provide their own online location or physical media for transfer.

LiDAR was collected using an uncalibrated Trimble MX2 mobile mapping system. The data is not quality controlled, and no accuracy assessment is provided or implied. Results are known to vary significantly. Users should exercise caution and conduct their own comprehensive suitability assessments before requesting and applying this data.

Sample Dataset: https://detroitmi.maps.arcgis.com/home/item.html?id=69853441d944442f9e79199b57f26fe3

Description Cellphone tower extract is a copy of the data found at https://ised-isde.canada.ca/site/spectrum-management-system/en/spectrum-management-system-data. Data is organized into a point layer of tower locations grouped by provider. All the providers transmitters are located in a table related by a unique TowerID.Dataset Usage General data layer for use when needed, for example to identify shortfalls in cell service for field work.Data Source Modified version of Innovation, Science and Economic Development Canada datasetData Criticality 1Sensitive Data NoCurator Greg SpiridonovCurator Job Title GIS SpecialistCurator Email greg.spiridonov@grey.caCurator Department IT / GISCurator Responsibilities Maintain,Oversight_Control_AccessMaintenance and Update Frequency MonthlyUpdate History New ZIP downloaded Nov 14 2023Published Map Service(s) https://gis.grey.ca/portal/home/item.html?id=718f08a731924856827f85178bb649cbPublicly Available Publicly availableOpen Data Published to Open DataOffline (sync) Not sure at this timeOther Comments Dataset relates to table GC_CellTower_TransmittersPermissionsAssign permissions to map service if published.Group PermissionsCurator Department ViewGrey County Staff ViewPublic View

Mobile home points throughout Pierce County. Each mobile home parcel is represented as an individual point. Additional information for each mobile home parcel can be acquired here. Please read metadata (https://matterhorn.piercecountywa.gov/GISmetadata/pdbatr_mobilehomes.html) for additional information. Any use or data download constitutes acceptance of the Terms of Use (https://matterhorn.piercecountywa.gov/disclaimer/PierceCountyGISDataTermsofUse.pdf).

This open data site is for exploring, accessing and downloading Kentucky-specific GIS data and discovering mapping apps. It provides simple access to information and tools that allow users to understand geospatial data. You can analyze and combine datasets using maps, as well as develop new web and mobile applications. Explore data by category, interact with web mapping applications, use Story Maps, or access our services directly. All data on the site is fed from a variety of authoritative sources.DO NOT DELETE OR MODIFY THIS ITEM. This item is managed by the ArcGIS Hub application. To make changes to this site, please visit https://hub.arcgis.com/admin/

This map presents transportation data, including highways, roads, railroads, and airports for the world.

The map was developed by Esri using Esri highway data; Garmin basemap layers; HERE street data for North America, Europe, Australia, New Zealand, South America and Central America, India, most of the Middle East and Asia, and select countries in Africa. Data for Pacific Island nations and the remaining countries of Africa was sourced from OpenStreetMap contributors. Specific country list and documentation of Esri's process for including OSM data is available to view.

It is about updating to GIS information database, Decision Support Tool (DST) in collaboration with IWMI. With the support of the Fish for Livelihoods field team and IPs (MFF, BRAC Myanmar, PACT Myanmar, and KMSS) staff, collection of Global Positioning System GPS location data for year-1 (2019-20) 1,167 SSA farmer ponds, and year-2 (2020-21) 1,485 SSA farmer ponds were completed with different GPS mobile applications: My GPS Coordinates, GPS Status & Toolbox, GPS Essentials, Smart GPS Coordinates Locator and GPS Coordinates. The Soil and Water Assessment Tool (SWAT) model that integrates climate change analysis with water availability will provide an important tool informing decisions on scaling pond adoption. It can also contribute to a Decision Support Tool to better target pond scaling. GIS Data also contribute to identify the location point of the F4L SSA farmers ponds on the Myanmar Map by fiscal year from 1 to 5.

Collaboration between the Department of Licensing and Consumer Protection (DLCP) and the District Department of Transportation (DDOT) DCMR 24-534. The regulations were effective October 1, 2013 (DCMR 24-5: Vending). The data accurate as of April 1, 2014. Meter numbers are either odd-numbered or even-numbered in sequence. Except for Capitol Riverfront, all meters are individual vs. multi-space meters.

SPR Recreation Division programming locations including Rec'N the Streets Mobile Recreation, Park Ambassadors, Summer Lunch & Playground, and others. In response to the COVID-19 pandemic, Recreation Division programming continues to offer outdoor opportunities to recreate under existing safety precautions. The mission of Rec'N the Streets in particular is: everyone should have access to recreation in their communities; for those in areas with health disparities and no access to these activities, we bring the recreation to them.Refresh Cycle: WeeklyFeature Class: DPR.MobileRec_PT

Land mobile private locations in Los Angeles CountyThis dataset is maintained through the County of Los Angeles Location Management System. The Location Management System is used by the County of Los Angeles GIS Program to maintain a single, comprehensive geographic database of locations countywide. For more information on the Location Management System, visit http://egis3.lacounty.gov/lms/.

BestPlace is an innovative retail data and analytics tool created explicitly for medium and enterprise-level CPG/FMCG companies. It's designed to revolutionize your retail data analysis approach by adding a strategic location-based perspective to your existing database. This perspective enriches your data landscape and allows your business to understand better and cater to shopping behavior. An In-Depth Approach to Retail Analytics Unlike conventional analytics tools, BestPlace delves deep into each store location details, providing a comprehensive analysis of your retail database. We leverage unique tools and methodologies to extract, analyze, and compile data. Our processes have been accurately designed to provide a holistic view of your business, equipping you with the information you need to make data-driven data-backed decisions. Amplifying Your Database with BestPlace At BestPlace, we understand the importance of a robust and informative retail database design. We don't just add new stores to your database; we enrich each store with vital characteristics and factors. These enhancements come from open cartographic sources such as Google Maps and our proprietary GIS database, all carefully collected and curated by our experienced data analysts. Store Features We enrich your retail database with an array of store features, which include but are not limited to: Number of reviews Average ratings Operational hours Categories relevant to each point Our attention to detail ensures your retail database becomes a powerful tool for understanding customer interactions and preferences. Geo-Analytical Factors Each store in your database is further enhanced with geo-analytical data. We analyze: Maximum pedestrian and vehicle traffic within a defined radius Number of households and average income within the catchment area vicinity Number of schools, hospitals, universities, competitors, stores, bars, clubs, and restaurants in the surrounding area Point attendance based on mobile device location data (ensuring GDPR compliance) Our refined retail data collection and analysis provides detailed shopping behavior insights, leading to in-depth shopper analytics and retail foot traffic data that support strategic planning and execution. The Power of Points of Interest (POI) Data At BestPlace, we harness the power of Point of Interest (POI) data (to bring you the most complete retail data set.) to bring your retail data to life. Our POI data collection process involves analyzing and categorizing foot traffic data, providing a comprehensive foot traffic dataset as a result. This data allows you to understand the ebb and flow of individuals around your store locations, suggesting invaluable insights for strategic planning and operational efficiency. Leveraging GIS Data Our GIS data collection process is meticulous and comprehensive. We tap into multiple GIS data sources, providing a wealth of data to enhance your retail analytics. This process allows us to equip your database with a broad range of geospatial features, including demographic and socioeconomic information from various census data for GIS applications. By including GIS data in your analysis, you gain a multi-dimensional perspective of your retail landscape, allowing for more strategic decision-making. The Advantages of Census Data BestPlace grants you direct access to a wealth of census data sets. This transforms your retail database into a more potent tool for decision-making, providing a deeper understanding of the demographics and socioeconomic factors surrounding your store locations. With the ability to download census data directly, you can enrich your retail data analysis with valuable insights about potential customers, giving you the upper hand in your strategic planning. Extensive Use Cases BestPlace's capabilities stretch across various applications, offering value in areas such as: Competition Analysis: Identify your competitors, analyze their performance, and understand your standing in the market with our extensive POI database and retail data analytics capabilities. New Location Search: Use our rich retail store database to identify ideal locations for store expansions based on foot traffic data, proximity to key points, and potential customer demographics. Location Comparison: Compare multiple store locations based on numerous factors and make informed decisions about where to focus your resources. Distribution Optimization: Leverage our FMCG data analytics and retail traffic analytics to optimize your distribution strategy and maximize ROI. Building Machine Learning Models: Integrate our all-purpose machine learning models into your business decision processes to enable more efficient and effective decision-making. (Integrate our all-purpose machine learning models to build your own in-house solutions with the help of our data.) Comprehensive Deliverables As a BestPlace client, you receive a comprehensive produc...

This file contains two items. First, the complete wireframes in Adobe XD format for the development of a GIS-Based mobile application for flood risk preparedness. Second, a video presenting the potential look and functioning of the iTYSA flood preparedness app with a mock-up created by Abby Muricho Onencan. The mock-up was created with Adobe XD and the demonstration was created with an inbuilt functionality for Adobe XD to develop videos.

The CDTFA Mobile app will enable you to find a sales and use tax rate, locate and contact our field offices, and conveniently access our website and online services. FEATURES • California Sales and Use Tax Rates: Find a tax rate by address, city, or your current location • CDTFA Field Offices: Get an office's address and other details, and with the tap of a button call an office or open its location in the Maps application to get driving directions • Website: View our website right within the app • Online Services: Access our online services directly with the tap of a button • And more features will be coming soon!

See the KDOT Mobile LiDAR Project Data Portal Home Page or the 2023 Billboard Faces Home Page for more details about the project and the delivered data.

Polygons of active and historic mobile home development in unincorporated Pierce County. Please read metadata (https://matterhorn.piercecountywa.gov/GISmetadata/pdbplandev_mobile_homes.html) for additional information. Any use or data download constitutes acceptance of the Terms of Use (https://matterhorn.piercecountywa.gov/disclaimer/PierceCountyGISDataTermsofUse.pdf).Please see provided hyperlinks for metadata and Terms of Use.

These data were automated to provide an accurate high-resolution historical shoreline of Mobile Bay, Alabama suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://www.fisheries.noaa.gov/inport/item/39808

This layer shows computer ownership and internet access by age and race. 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 of population age 18 to 64 in households with no computer. 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): B28005, B28003, B28009B, B28009C, B28009D, B28009E, B28009F, B28009G, B28009H, B28009I 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.

The representation of mobile homes in San Jose, CA.