Tutorial document describing how to go about mapping crash data in Tyler Data & Insights. This was done using the Crash Data dataset.

CrimeMapTutorial is a step-by-step tutorial for learning crime mapping using ArcView GIS or MapInfo Professional GIS. It was designed to give users a thorough introduction to most of the knowledge and skills needed to produce daily maps and spatial data queries that uniformed officers and detectives find valuable for crime prevention and enforcement. The tutorials can be used either for self-learning or in a laboratory setting. The geographic information system (GIS) and police data were supplied by the Rochester, New York, Police Department. For each mapping software package, there are three PDF tutorial workbooks and one WinZip archive containing sample data and maps. Workbook 1 was designed for GIS users who want to learn how to use a crime-mapping GIS and how to generate maps and data queries. Workbook 2 was created to assist data preparers in processing police data for use in a GIS. This includes address-matching of police incidents to place them on pin maps and aggregating crime counts by areas (like car beats) to produce area or choropleth maps. Workbook 3 was designed for map makers who want to learn how to construct useful crime maps, given police data that have already been address-matched and preprocessed by data preparers. It is estimated that the three tutorials take approximately six hours to complete in total, including exercises.

GapMaps Live is an easy-to-use location intelligence platform available across 25 countries globally that allows you to visualise your own store data, combined with the latest demographic, economic and population movement intel right down to the micro level so you can make faster, smarter and surer decisions when planning your network growth strategy.

With one single login, you can access the latest estimates on resident and worker populations, census metrics (eg. age, income, ethnicity), consuming class, retail spend insights and point-of-interest data across a range of categories including fast food, cafe, fitness, supermarket/grocery and more.

Some of the world's biggest brands including McDonalds, Subway, Burger King, Anytime Fitness and Dominos use GapMaps Live Map Data as a vital strategic tool where business success relies on up-to-date, easy to understand, location intel that can power business case validation and drive rapid decision making.

Primary Use Cases for GapMaps Live Map Data include:

1. Retail Site Selection - Identify optimal locations for future expansion and benchmark performance across existing locations.
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers and where to find more of them.
3. Analyse your catchment areas at a granular grid levels using all the key metrics
4. Target Marketing: Develop effective marketing strategies to acquire more customers.
5. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)
6. Customer Profiling
7. Target Marketing
8. Market Share Analysis

Some of features our clients love about GapMaps Live Map Data include: - View business locations, competitor locations, demographic, economic and social data around your business or selected location - Understand consumer visitation patterns (“where from” and “where to”), frequency of visits, dwell time of visits, profiles of consumers and much more. - Save searched locations and drop pins - Turn on/off all location listings by category - View and filter data by metadata tags, for example hours of operation, contact details, services provided - Combine public data in GapMaps with views of private data Layers - View data in layers to understand impact of different data Sources - Share maps with teams - Generate demographic reports and comparative analyses on different locations based on drive time, walk time or radius. - Access multiple countries and brands with a single logon - Access multiple brands under a parent login - Capture field data such as photos, notes and documents using GapMaps Connect and integrate with GapMaps Live to get detailed insights on existing and proposed store locations.

The Google Maps dataset is ideal for getting extensive information on businesses anywhere in the world. Easily filter by location, business type, and other factors to get the exact data you need. The Google Maps dataset includes all major data points: timestamp, name, category, address, description, open website, phone number, open_hours, open_hours_updated, reviews_count, rating, main_image, reviews, url, lat, lon, place_id, country, and more.

This dataset is a series of digital map-posters accompanying the AdaptNRM Guide: Helping Biodiversity Adapt: supporting climate adaptation planning using a community-level modelling approach.

These represent supporting materials and information about the community-level biodiversity models applied to climate change. Map posters are organised by four biological groups (vascular plants, mammals, reptiles and amphibians), two climate change scenario (1990-2050 MIROC5 and CanESM2 for RCP8.5), and five measures of change in biodiversity.

The map-posters present the nationally consistent data at locally relevant resolutions in eight parts – representing broad groupings of NRM regions based on the cluster boundaries used for climate adaptation planning (http://www.environment.gov.au/climate-change/adaptation) and also Nationally.

Map-posters are provided in PNG image format at moderate resolution (300dpi) to suit A0 printing. The posters were designed to meet A0 print size and digital viewing resolution of map detail. An additional set in PDF image format has been created for ease of download for initial exploration and printing on A3 paper. Some text elements and map features may be fuzzy at this resolution.

Each map-poster contains four dataset images coloured using standard legends encompassing the potential range of the measure, even if that range is not represented in the dataset itself or across the map extent.

Most map series are provided in two parts: part 1 shows the two climate scenarios for vascular plants and mammals and part 2 shows reptiles and amphibians. Eight cluster maps for each series have a different colour theme and map extent. A national series is also provided. Annotation briefly outlines the topics presented in the Guide so that each poster stands alone for quick reference.

An additional 77 National maps presenting the probability distributions of each of 77 vegetation types – NVIS 4.1 major vegetation subgroups (NVIS subgroups) - are currently in preparation.

Example citations:

Williams KJ, Raisbeck-Brown N, Prober S, Harwood T (2015) Generalised projected distribution of vegetation types – NVIS 4.1 major vegetation subgroups (1990 and 2050), A0 map-poster 8.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

Williams KJ, Raisbeck-Brown N, Harwood T, Prober S (2015) Revegetation benefit (cleared natural areas) for vascular plants and mammals (1990-2050), A0 map-poster 9.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

This dataset has been delivered incrementally. Please check that you are accessing the latest version of the dataset. Lineage: The map posters show case the scientific data. The data layers have been developed at approximately 250m resolution (9 second) across the Australian continent to incorporate the interaction between climate and topography, and are best viewed using a geographic information system (GIS). Each data layers is 1Gb, and inaccessible to non-GIS users. The map posters provide easy access to the scientific data, enabling the outputs to be viewed at high resolution with geographical context information provided.

Maps were generated using layout and drawing tools in ArcGIS 10.2.2

A check list of map posters and datasets is provided with the collection.

Map Series: 7.(1-77) National probability distribution of vegetation type – NVIS 4.1 major vegetation subgroup pre-1750 #0x

8.1 Generalised projected distribution of vegetation types (NVIS subgroups) (1990 and 2050)

9.1 Revegetation benefit (cleared natural areas) for plants and mammals (1990-2050)

9.2 Revegetation benefit (cleared natural areas) for reptiles and amphibians (1990-2050)

10.1 Need for assisted dispersal for vascular plants and mammals (1990-2050)

10.2 Need for assisted dispersal for reptiles and amphibians (1990-2050)

11.1 Refugial potential for vascular plants and mammals (1990-2050)

11.1 Refugial potential for reptiles and amphibians (1990-2050)

12.1 Climate-driven future revegetation benefit for vascular plants and mammals (1990-2050)

12.2 Climate-driven future revegetation benefit for vascular reptiles and amphibians (1990-2050)

This map provides a guide to the data confidence of DPIE's soil related thematic map products in NSW. Examples of products this map supports includes Land and Soil Capability mapping, Inherent fertility of soils in NSW and Great Soil Group soil types in NSW.

Confidence classes are determined based on the data scale, type of mapping and information collected, accuracy of the attributes and quality assurance on the product.

Soil data confidence is described using a 4 class system between high and very low as outlined below.:

• Good (1) - All necessary soil and landscape data is available at a catchment scale (1:100,000 & 1:250,000) to undertake the assessment of LSC and other soil thematic maps.

• Moderate (2) - Most soil and landscape data is available at a catchment scale (1:100,000 - 1:250,000) to undertake the assessment of LSC and other soil thematic maps.

• Low (3) - Limited soil and landscape data is available at a reconnaissance catchment scale (1:100,000 & 1:250,000) which limits the quality of the assessment of LSC and other soil thematic maps.

• Very low (4) - Very limited soil and landscape data is available at a broad catchment scale (1:250,000 - 1:500,000) and the LSC and other soil thematic maps should be used as a guide only.

Online Maps: This dataset can be viewed using eSPADE (NSW’s soil spatial viewer), which contains a suite of soil and landscape information including soil profile data. Many of these datasets have hot-linked soil reports. An alternative viewer is the SEED Map; an ideal way to see what other natural resources datasets (e.g. vegetation) are available for this map area.

Reference: Department of Planning, Industry and Environment, 2020, Soil Data Confidence map for NSW, Version 4, NSW Department of Planning, Industry and Environment, Parramatta.

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands” from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Santa Barbara Channel map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these “ground-truth” surveying data are available from the CSMP Video and Photograph Portal at https://doi.org/10.5066/F7J1015K. The “seafloor character” data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The “potential habitats” polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles. The information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues. Web services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Santa Barbara Channel map area data layers. Data layers are symbolized as shown on the associated map sheets.

This dataset consists of cartographic data in digital line graph (DLG) form for the northeastern states (Connecticut, Maine, Massachusetts, New Hampshire, New York, Rhode Island and Vermont). Information is presented on two planimetric base categories, political boundaries and administrative boundaries, each available in two formats: the topologically structured format and a simpler format optimized for graphic display. These DGL data can be used to plot base maps and for various kinds of spatial analysis. They may also be combined with other geographically referenced data to facilitate analysis, for example the Geographic Names Information System.

This tutorial will teach you how to take time-series data from many field sites and create a shareable online map, where clicking on a field location brings you to a page with interactive graph(s).

The tutorial can be completed with a sample dataset (provided via a Google Drive link within the document) or with your own time-series data from multiple field sites.

Part 1 covers how to make interactive graphs in Google Data Studio and Part 2 covers how to link data pages to an interactive map with ArcGIS Online. The tutorial will take 1-2 hours to complete.

An example interactive map and data portal can be found at: https://temple.maps.arcgis.com/apps/View/index.html?appid=a259e4ec88c94ddfbf3528dc8a5d77e8

Overview

Empower your location data visualizations with our edge-matched polygons, even in difficult geographies.

Our self-hosted geospatial data cover administrative and postal divisions with up to 5 precision levels. All levels follow a seamless hierarchical structure with no gaps or overlaps.

The geospatial data shapes are offered in high-precision and visualization resolution and are easily customized on-premise.

Use cases for the Global Administrative Boundaries Database (Geospatial data, Map data)

• In-depth spatial analysis

• Clustering

• Geofencing

• Reverse Geocoding

• Reporting and Business Intelligence (BI)

Product Features

• Coherence and precision at every level

• Edge-matched polygons

• High-precision shapes for spatial analysis

• Fast-loading polygons for reporting and BI

• Multi-language support

For additional insights, you can combine the map data with:

• Population data: Historical and future trends

• UNLOCODE and IATA codes

• Time zones and Daylight Saving Time (DST)

Data export methodology

Our location data packages are offered in variable formats, including - .shp - .gpkg - .kml - .shp - .gpkg - .kml - .geojson

All geospatial data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Why companies choose our map data

• Precision at every level

• Coverage of difficult geographies

• No gaps, nor overlaps

Note: Custom geospatial data packages are available. Please submit a request via the above contact button for more details.

This application is intended for informational purposes only and is not an operational product. The tool provides the capability to access, view and interact with satellite imagery, and shows the latest view of Earth as it appears from space.For additional imagery from NOAA's GOES East and GOES West satellites, please visit our Imagery and Data page or our cooperative institute partners at CIRA and CIMSS.This website should not be used to support operational observation, forecasting, emergency, or disaster mitigation operations, either public or private. In addition, we do not provide weather forecasts on this site — that is the mission of the National Weather Service. Please contact them for any forecast questions or issues. Using the Maps​What does the Layering Options icon mean?The Layering Options widget provides a list of operational layers and their symbols, and allows you to turn individual layers on and off. The order in which layers appear in this widget corresponds to the layer order in the map. The top layer ‘checked’ will indicate what you are viewing in the map, and you may be unable to view the layers below.Layers with expansion arrows indicate that they contain sublayers or subtypes.Do these maps work on mobile devices and different browsers?Yes!Why are there black stripes / missing data on the map?NOAA Satellite Maps is for informational purposes only and is not an operational product; there are times when data is not available.Why are the North and South Poles dark?The raw satellite data used in these web map apps goes through several processing steps after it has been acquired from space. These steps translate the raw data into geospatial data and imagery projected onto a map. NOAA Satellite Maps uses the Mercator projection to portray the Earth's 3D surface in two dimensions. This Mercator projection does not include data at 80 degrees north and south latitude due to distortion, which is why the poles appear black in these maps. NOAA's polar satellites are a critical resource in acquiring operational data at the poles of the Earth and some of this imagery is available on our website (for example, here ).Why does the imagery load slowly?This map viewer does not load pre-generated web-ready graphics and animations like many satellite imagery apps you may be used to seeing. Instead, it downloads geospatial data from our data servers through a Map Service, and the app in your browser renders the imagery in real-time. Each pixel needs to be rendered and geolocated on the web map for it to load.How can I get the raw data and download the GIS World File for the images I choose?NOAA Satellite Maps offers an interoperable map service to the public. Use the camera tool to select the area of the map you would like to capture and click ‘download GIS WorldFile.’The geospatial data Map Service for the NOAA Satellite Maps GOES satellite imagery is located on our Satellite Maps ArcGIS REST Web Service ( available here ).We support open information sharing and integration through this RESTful Service, which can be used by a multitude of GIS software packages and web map applications (both open and licensed).Data is for display purposes only, and should not be used operationally.Are there any restrictions on using this imagery?NOAA supports an open data policy and we encourage publication of imagery from NOAA Satellite Maps; when doing so, please cite it as "NOAA" and also consider including a permalink (such as this one) to allow others to explore the imagery.For acknowledgment in scientific journals, please use:We acknowledge the use of imagery from the NOAA Satellite Maps application: LINKThis imagery is not copyrighted. You may use this material for educational or informational purposes, including photo collections, textbooks, public exhibits, computer graphical simulations and internet web pages. This general permission extends to personal web pages. About this satellite imageryWhat am I looking at in these maps?What am I seeing in the NOAA Satellite Maps 3D Scene?There are four options to choose from, each depicting a different view of the Earth using the latest satellite imagery available. The first three views show the Western Hemisphere and the Pacific Ocean, as captured by the NOAA GOES East (GOES-16) and GOES West (GOES-17) satellites. These images are updated approximately every 15 minutes as we receive data from the satellites in space. The three views show GeoColor, infrared and water vapor. See our other FAQs to learn more about what the imagery layering options depict.The fourth option is a global view, captured by NOAA’s polar-orbiting satellites (NOAA/NASA Suomi NPP and NOAA-20). The polar satellites circle the globe 14 times a day, taking in one complete view of the Earth in daylight every 24 hours. This composite view is what is projected onto the 3D map scene each morning, so you are seeing how the Earth looked from space one day ago.What am I seeing in the Latest 24 Hrs. GOES Constellation Map?In this map you are seeing the past 24 hours (updated approximately every 15 minutes) of the Western Hemisphere and Pacific Ocean, as seen by the NOAA GOES East (GOES-16) and GOES West (GOES-17) satellites. In this map you can also view three different ‘layers’. The three views show ‘GeoColor’ ‘infrared’ and ‘water vapor’.(Please note: GOES West imagery is currently only available in GeoColor. The infrared and water vapor imagery will be available in Spring 2019.)This maps shows the coverage area of the GOES East and GOES West satellites. GOES East, which orbits the Earth from 75.2 degrees west longitude, provides a continuous view of the Western Hemisphere, from the West Coast of Africa to North and South America. GOES West, which orbits the Earth at 137.2 degrees west longitude, sees western North and South America and the central and eastern Pacific Ocean all the way to New Zealand.What am I seeing in the Global Archive Map?In this map, you will see the whole Earth as captured each day by our polar satellites, based on our multi-year archive of data. This data is provided by NOAA’s polar orbiting satellites (NOAA/NASA Suomi NPP from January 2014 to April 19, 2018 and NOAA-20 from April 20, 2018 to today). The polar satellites circle the globe 14 times a day taking in one complete view of the Earth every 24 hours. This complete view is what is projected onto the flat map scene each morning.What does the GOES GeoColor imagery show?The 'Merged GeoColor’ map shows the coverage area of the GOES East and GOES West satellites and includes the entire Western Hemisphere and most of the Pacific Ocean. This imagery uses a combination of visible and infrared channels and is updated approximately every 15 minutes in real time. GeoColor imagery approximates how the human eye would see Earth from space during daylight hours, and is created by combining several of the spectral channels from the Advanced Baseline Imager (ABI) – the primary instrument on the GOES satellites. The wavelengths of reflected sunlight from the red and blue portions of the spectrum are merged with a simulated green wavelength component, creating RGB (red-green-blue) imagery. At night, infrared imagery shows high clouds as white and low clouds and fog as light blue. The static city lights background basemap is derived from a single composite image from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day Night Band. For example, temporary power outages will not be visible. Learn more.What does the GOES infrared map show?The 'GOES infrared' map displays heat radiating off of clouds and the surface of the Earth and is updated every 15 minutes in near real time. Higher clouds colorized in orange often correspond to more active weather systems. This infrared band is one of 12 channels on the Advanced Baseline Imager, the primary instrument on both the GOES East and West satellites. on the GOES the multiple GOES East ABI sensor’s infrared bands, and is updated every 15 minutes in real time. Infrared satellite imagery can be "colorized" or "color-enhanced" to bring out details in cloud patterns. These color enhancements are useful to meteorologists because they signify “brightness temperatures,” which are approximately the temperature of the radiating body, whether it be a cloud or the Earth’s surface. In this imagery, yellow and orange areas signify taller/colder clouds, which often correlate with more active weather systems. Blue areas are usually “clear sky,” while pale white areas typically indicate low-level clouds. During a hurricane, cloud top temperatures will be higher (and colder), and therefore appear dark red. This imagery is derived from band #13 on the GOES East and GOES West Advanced Baseline Imager.How does infrared satellite imagery work?The infrared (IR) band detects radiation that is emitted by the Earth’s surface, atmosphere and clouds, in the “infrared window” portion of the spectrum. The radiation has a wavelength near 10.3 micrometers, and the term “window” means that it passes through the atmosphere with relatively little absorption by gases such as water vapor. It is useful for estimating the emitting temperature of the Earth’s surface and cloud tops. A major advantage of the IR band is that it can sense energy at night, so this imagery is available 24 hours a day.What do the colors on the infrared map represent?In this imagery, yellow and orange areas signify taller/colder clouds, which often correlate with more active weather systems. Blue areas are clear sky, while pale white areas indicate low-level clouds, or potentially frozen surfaces. Learn more about this weather imagery.What does the GOES water vapor map layer show?The GOES ‘water vapor’ map displays the concentration and location of clouds and water vapor in the atmosphere and shows data from both the GOES East and GOES West satellites. Imagery is updated approximately every 15 minutes in

APISCRAPY, your premier provider of Map Data solutions. Map Data encompasses various information related to geographic locations, including Google Map Data, Location Data, Address Data, and Business Location Data. Our advanced Google Map Data Scraper sets us apart by extracting comprehensive and accurate data from Google Maps and other platforms.

What sets APISCRAPY's Map Data apart are its key benefits:

1. Accuracy: Our scraping technology ensures the highest level of accuracy, providing reliable data for informed decision-making. We employ advanced algorithms to filter out irrelevant or outdated information, ensuring that you receive only the most relevant and up-to-date data.

2. Accessibility: With our data readily available through APIs, integration into existing systems is seamless, saving time and resources. Our APIs are easy to use and well-documented, allowing for quick implementation into your workflows. Whether you're a developer building a custom application or a business analyst conducting market research, our APIs provide the flexibility and accessibility you need.

3. Customization: We understand that every business has unique needs and requirements. That's why we offer tailored solutions to meet specific business needs. Whether you need data for a one-time project or ongoing monitoring, we can customize our services to suit your needs. Our team of experts is always available to provide support and guidance, ensuring that you get the most out of our Map Data solutions.

Our Map Data solutions cater to various use cases:

1. B2B Marketing: Gain insights into customer demographics and behavior for targeted advertising and personalized messaging. Identify potential customers based on their geographic location, interests, and purchasing behavior.

2. Logistics Optimization: Utilize Location Data to optimize delivery routes and improve operational efficiency. Identify the most efficient routes based on factors such as traffic patterns, weather conditions, and delivery deadlines.

3. Real Estate Development: Identify prime locations for new ventures using Business Location Data for market analysis. Analyze factors such as population density, income levels, and competition to identify opportunities for growth and expansion.

4. Geospatial Analysis: Leverage Map Data for spatial analysis, urban planning, and environmental monitoring. Identify trends and patterns in geographic data to inform decision-making in areas such as land use planning, resource management, and disaster response.

5. Retail Expansion: Determine optimal locations for new stores or franchises using Location Data and Address Data. Analyze factors such as foot traffic, proximity to competitors, and demographic characteristics to identify locations with the highest potential for success.

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In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands” from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Point Sur to Point Arguello map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these “ground-truth” surveying data are available from the CSMP Video and Photograph Portal at https://doi.org/10.5066/F7J1015K. The “seafloor character” data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The “potential habitats” polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles. The information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues. Web services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Point Sur to Point Arguello map area data layers. Data layers are symbolized as shown on the associated map sheets.

This story map explains how to use heat mapping within smart mapping to show density within your maps in ArcGIS Online. You can easily select the heat map style to show where your data is spatially clustered. Go beyond the defaults to show density for an attribute, telling the story of an area that is statistically significant. Add the points layer back into the map with transparency as a reference to the heat map. This story map walks you through examples, which can help get you started with smart mapping heat maps. For more information, visit the Help Pages.

This specialized location dataset delivers detailed information about marina establishments. Maritime industry professionals, coastal planners, and tourism researchers can leverage precise location insights to understand maritime infrastructure, analyze recreational boating landscapes, and develop targeted strategies.

How Do We Create Polygons? -All our polygons are manually crafted using advanced GIS tools like QGIS, ArcGIS, and similar applications. This involves leveraging aerial imagery and street-level views to ensure precision. -Beyond visual data, our expert GIS data engineers integrate venue layout/elevation plans sourced from official company websites to construct detailed indoor polygons. This meticulous process ensures higher accuracy and consistency. -We verify our polygons through multiple quality checks, focusing on accuracy, relevance, and completeness.

What's More? -Custom Polygon Creation: Our team can build polygons for any location or category based on your specific requirements. Whether it’s a new retail chain, transportation hub, or niche point of interest, we’ve got you covered. -Enhanced Customization: In addition to polygons, we capture critical details such as entry and exit points, parking areas, and adjacent pathways, adding greater context to your geospatial data. -Flexible Data Delivery Formats: We provide datasets in industry-standard formats like WKT, GeoJSON, Shapefile, and GDB, making them compatible with various systems and tools. -Regular Data Updates: Stay ahead with our customizable refresh schedules, ensuring your polygon data is always up-to-date for evolving business needs.

Unlock the Power of POI and Geospatial Data With our robust polygon datasets and point-of-interest data, you can: -Perform detailed market analyses to identify growth opportunities. -Pinpoint the ideal location for your next store or business expansion. -Decode consumer behavior patterns using geospatial insights. -Execute targeted, location-driven marketing campaigns for better ROI. -Gain an edge over competitors by leveraging geofencing and spatial intelligence.

Why Choose LocationsXYZ? LocationsXYZ is trusted by leading brands to unlock actionable business insights with our spatial data solutions. Join our growing network of successful clients who have scaled their operations with precise polygon and POI data. Request your free sample today and explore how we can help accelerate your business growth.

The City of Fort Collins GIS Online Mapping tool (FCMaps) provide current, timely and local geographic information in an easy to use viewer. FCMaps is mobile friendly and will work well on tablets and smartphones as well as a desktop browser.

Here you will find tools to create your own maps. For example, you can find out what kind of zoning the lot next door has, locate an Art in Public Places project, confirm your Council district, map out a bike route on the City's bikeway system, check on the status of a current development project or locate a headstone in one of the City's cemeteries.

This dashboard defaults to a presentation of the crash points that will cluster the crash types and determine a predominant crash type. In the case two crash types have the same number of crashes for that type the predominant type will not be colored to either of the crash types. Clicking on the clusters will include a basic analysis of the cluster. These clusters are dynamic and will change as the user zooms in an out of the map. The clustering of crashes is functionality availalble in ArcGIS Online and the popups for the clusters is based on items that include elements configured with the Arcade language. Users interested in learning more about point clustering and the configuration of popups should read through some of the examples of the following ESRI Article (https://www.esri.com/arcgis-blog/products/arcgis-online/mapping/summarize-and-explore-point-clusters-with-arcade-in-popups/) . The dashboard itself does include a map widget that does allow the user to toggle the visibility of layers and/or click on the crashes within the map. The popups for single crashes can be difficult to see unless the map is expanded (click in upper right of map widget). There is a Review Crashes tab that allows for another display of details of a crash that may be easier for users.This dashboard includes selectors in both the header and sidebar. By default the sidebar is collapsed and would need to be expanded. The crash dataset used in the presentation includes columns with a prefix of the unit. The persons information associated to each unit would be based on the Person that was considered the driver. Crash data can be filtered by clicking on items in chart widgets. All chart widgets have been configured to allow multiple selections and these selections will then filter the crash data accordingly. Allowing for data to be filtered by clicking on widgets is an alternative approach to setting up individual selectors. Selectors can take up a lot of space in the header and sidebar and clicking on the widget items can allow you to explore different scenarios which may ultimately be setup as selectors in the future. The Dashboard has many widgets that are stacked atop each other and underneath these stacked widgets are controls or tabs that allow the user to toggle between different visualizations. The downside to allowing a user to filter based on the output of a widget is the need for the end user to keep track of what has been clicked and the need to go back through and unclick.Many of the Crash Data Elements are based on lookups that have a fairly large range of values to select. This can be difficult sometimes with charts and the fact that a user may be overwhelmed by the number of items be plotted. Some of these values could potentially benefit by grouping similar values. The crash data being used in this dashboard hasn't been post processed to simplify some of the groupings of data and represent the value as it would appear in the Crash System. This dashboard was put together to continue the discussion on what data elements should be included in the GIS Crash Dataset. At the moment there is currently one primary dataset that is used to present crash data in Map Services. There is lots of potential to extend this dataset to include additional elements or it might be beneficial to create different versions of the crash data. Having an examples like this one will hopefully help with the discussion. Workable examples of what works and doesn't work. There are lots of data elements in the Crash System that could allow for an even more detailed safety analysis. Some of the unit items included in the example for Minot Ave in Auburn are the following. This information is included for the first three units associated to any crash.Most Damaged AreaExtent of DamageUnit TypeDirection of Travel (Northbound, Southbound, Eastbound, Westbound)Pre-Crash ActionsSequence of Events 1-4Most Harmful Event Some of the persons items included in the example for Minot Ave in Auburn are the following. This information is included for the first three units associated to any crash and the person would be based on the driver.Condition at Time of CrashDriver Action 1Driver Action 2Driver DistractedAgeSexPerson Type (Driver/Owner(6), Driver(1))In addition to the Units and Persons information included above each crash includes the standard crash data elements which includesDate, Time, Day of Week, Year, Month, HourInjury Level (K,A,B,C,PD)Type of CrashTownname, County, MDOT RegionWeather ConditionsLight ConditionsRoad Surface ConditionsRoad GradeSchool Bus RelatedTraffic Control DeviceType of LocationWork Zone ItemsLocation Type (NODE, ELEMENT) used for LRS# of K, # of A, # of B, # of C, # of PD InjuriesTotal # of UnitsTotal # of PersonsFactored AADT (Only currently applicable for crashes along the roadway (ELEMENT)).Location of First Harmful EventTotal Injury Count for the CrashBoolean Y/N if Pedestrian or Bicycles are InvolvedContributing EnvironmentsContributing RoadRoute Number, Milepoint, Element ID, Node ID

The Sample can drive classification algorithms, thus is a prerequisite for accurate classification. Coastal areas are located in the transitional zone between land and sea, requiring more samples to describe diverse land covers. However, there are scarce studies sharing their sample datasets, leading to a repeat of the time-consuming and laborious sampling procedure. To alleviate the problem, we share a sample set with a total of 16,444 sample points derived from a study of mapping mangroves of China. The sample set contains a total of 10 categories, which are described as follows. 1) The mangroves refer to “true mangroves” (excluding the associate mangrove species). In sampling mangroves, we used the data from the China Mangrove Conservation Network (CMCN, http://www.china-mangrove.org/), a non-governmental organization aiming to promote mangrove ecosystems. The CMCN provides an interactive map that can be annotated by volunteers with text or photos to record mangrove status at a location. Although the locations were shifted due to coordinate system differences and positioning errors, mangroves could be found around the mangrove locations depicted by the CMCN’s map on Google Earth images. There is a total of 1887 mangrove samples. 2) The cropland is dominated by paddy rice. We collected a total 1383 points according to its neat arrangement based on Google Earth images. 3) Coastal forests neighboring mangroves are mostly salt-tolerant, such as Cocos nucifera Linn., Hibiscus tiliaceus Linn., and Cerbera manghas Linn. We collected a total 1158 samples according to their distance to the shoreline based on Google Earth images. 4) Terrestrial forests are forests far from the shoreline, and are intolerant to salt. By visual inspection on Google Earth, we sampled 1269 points based on their appearances and distances to the shoreline. 5) For the grass category, we collected 1282 samples by visual judgement on Google Earth. 6) Saltmarsh, dominated by Spartina alterniflora, covering large areas of tidal flats in China. We collected 2065 samples according to Google Earth images. 7) The tidal flats category was represented by 1517 samples, which were sampled using the most recent global tidal flat map for 2014–2016 and were visually corrected. 8) The “sand or rock” category refers to sandy and pebble beaches or rocky coasts exposed to air, which are not habitats of mangroves. We collected 1622 samples on Google Earth based on visual inspection. 9) For the permanent water category, samples were first randomly sampled from a threshold result of NDWI (> 0.2), and then were visually corrected. A total of 2056 samples were obtained. 10) As to the artificial impervious surfaces category, we randomly sampled from a threshold result corresponding to normal difference built-up index (NDBI) (> 0.1), and corrected them based on Google Earth. The artificial impervious surface category was represented by 2205 samples. This sample dataset covers the low-altitude coastal area of five Provinces (Hainan, Guangdong, Fujian, Zhejiang, and Taiwan), one Autonomous region (Guangxi), and two Special Administrative Regions (Macau and Hong Kong) (see “study_area.shp” in the zip for details). It can be used to train models for coastal land cover classification, and to evaluate classification results. In addition to mangroves, it can also be used in identifying tidal flats, mapping salt marsh, extracting water bodies, and other related applications.Compared with the V1 version, we added a validation dataset for mangrove maps (Mangrove map validation dataset.rar), and thus can evaluate mangrove maps under the same dataset, which benefit the comparison of different mangrove maps. The validation dataset contains 10 shp files, in which each shp file contains 600 mangrove samples (cls_new field = 1) and 600 non-mangrove samples (cls_new field = 0).Compared with the V2 version, we added two classes of forest near water and grass near water, in addition to suppress the prevalent misclassified patches due to the spectral similarity between mangroves and those classes.

Example datasets provided for testing the GeoTrace (QGIS) and Compass (CloudCompare) plugins. The first dataset consists of two 10x10 meter orthophoto and DEM tiles from a high-resolution UAV survey of Bingie Bingie point (New South Wales, Australia) containing abundant joint traces as well as several mafic dykes. The GeoTrace plugin for QGIS can be used to extract the joint and contact traces from this dataset.

The second dataset comprises a dense-point cloud from a UAV survey of granite pinnacles at Cape Woolamai (Victoria, Australia), and can be used to test/demonstrate the Compass plugin in CloudCompare.

Mapping of environmental variables often relies on map accuracy assessment through cross-validation with the data used for calibrating the underlying mapping model. When the data points are spatially clustered, conventional cross-validation leads to optimistically biased estimates of map accuracy. Several papers have promoted spatial cross-validation as a means to tackle this over-optimism. Many of these papers blame spatial autocorrelation as the cause of the bias and propagate the widespread misconception that spatial proximity of calibration points to validation points invalidates classical statistical validation of maps. In the paper related to these data, we present and evaluate alternative cross-validation approaches for assessing map accuracy from clustered sample data.

The study area is western Europe, constrained in the north at 52° latitude and at -10° and 24° longitude The projection is IGNF:ETRS89LAEA (Lambert azimuthal equal area projection).

Files:

agb.tif = above ground biomass (AGB) map from version 3 of the 2017 CCI-Biomass product (https://catalogue.ceda.ac.uk/uuid/5f331c418e9f4935b8eb1b836f8a91b8) AGBstack.tif = covariates used for predicting AGB aggArea.tif = coarse grid used for simulation in the model-based methods ocs.tif = soil organic carbon stock (OCS) map (0-30 cm) from Soilgrids (https://www.isric.org/explore/soilgrids) OCSstack.tif = covariates used for predicting OCS strata.xxx = 100 compact geo-strata (ESRI shape) created with the spcosa package; used for generating clustered samples TOTmask.tif = mask of the area covered by the covariates

Details and data sources of the covariates in AGBstack.tif and OCSstack.tif:

Name

Description

Source

Note

ai

Aridity Index

https://chelsa-climate.org/downloads/

    Version 2.1

bio1

Mean annual air temperature [°C]

    https://chelsa-climate.org/downloads/
    Version 2.1

bio5

Mean daily maximum air temperature of the warmest month [°C]

    https://chelsa-climate.org/downloads/
    Version 2.1

bio7

Annual range of air temperature [°C]

    https://chelsa-climate.org/downloads/
    Version 2.1

bio12

Annual precipitation [kg/m2]

    https://chelsa-climate.org/downloads/
    Version 2.1

bio15

Precipitation seasonality [kg/m2]

    https://chelsa-climate.org/downloads/
    Version 2.1

gdd10

Growing degree days heat sum above 10°C

    https://chelsa-climate.org/downloads/
    Version 2.1

clay

Clay content [g/kg] of the 0-5cm layer

https://soilgrids.org/

Only used for AGB

sand

Sand content [g/kg] of the 0-5cm layer

    https://soilgrids.org/
    as above

pH

Acidity (Ph(water)) of the 0-5cm layer

    https://soilgrids.org/
    as above

glc2017

Landcover 2017

https://land.copernicus.eu/global/products/lc, reclassified to: closed forest, open forest, natural non-forest veg., bare & sparse veg. cropland, built-up, water

Categorical variable

dem

Elevation

https://www.eea.europa.eu/data-and-maps/data/copernicus-land-monitoring-service-eu-dem

cosasp

Cosine of slope aspect

Computed with the terra package from elevation

    Computed @25m resolution; next aggregated to 0.5km

sinasp

Sine of slope aspect

    Computed with the terra package from elevation
    as above

slope

Slope

    Computed with the terra package from elevation
    as above

TPI

Topographic position index

    Computed with the terra package from elevation
    as above

TRI

Terrain ruggedness index

    Computed with the terra package from elevation
    as above

TWI

Topographic wetness index

Computed with SAGA from 500m resolution (aggregated) dem

gedi

Forest height

https://glad.umd.edu/dataset/gedi

Zone: NAFR

xcoord

X coordinate

Using a mask created from the other covariates

ycoord

Y coordinate

    Using a mask created from the other covariates

Dcoast

Distance from coast

Using a land mask created from the other covariates