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.

Mapping incident locations from a CSV file in a web map (YouTube video).View this short demonstration video to learn how to geocode incident locations from a spreadsheet in ArcGIS Online. In this demonstration, the presenter drags a simple .csv file into a browser-based Web Map and maps the appropriate address fields to display incident points allowing different types of spatial overlays and analysis. _Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

http://www.openstreetmap.org/images/osm_logo.png" alt=""> OpenStreetMap (openstreetmap.org) is a global collaborative mapping project, which offers maps and map data released with an open license, encouraging free re-use and re-distribution. The data is created by a large community of volunteers who use a variety of simple on-the-ground surveying techniques, and wiki-syle editing tools to collaborate as they create the maps, in a process which is open to everyone. The project originated in London, and an active community of mappers and developers are based here. Mapping work in London is ongoing (and you can help!) but the coverage is already good enough for many uses.

Browse the map of London on OpenStreetMap.org

Downloads:

The whole of England updated daily:

• england.osm.bz2 ~185M - .osm formatted raw XML data (compressed)
• england.shp.zip ~156M - ESRI shapefiles

For more details of downloads available from OpenStreetMap, including downloading the whole planet, see 'planet.osm' on the wiki.

Data access APIs:

Download small areas of the map by bounding-box. For example this URL requests the data around Trafalgar Square:
http://api.openstreetmap.org/api/0.6/map?bbox=-0.13062,51.5065,-0.12557,51.50969

Data filtered by "tag". For example this URL returns all elements in London tagged shop=supermarket:
http://www.informationfreeway.org/api/0.6/*[shop=supermarket][bbox=-0.48,51.30,0.21,51.70]

The .osm format

The format of the data is a raw XML represention of all the elements making up the map. OpenStreetMap is composed of interconnected "nodes" and "ways" (and sometimes "relations") each with a set of name=value pairs called "tags". These classify and describe properties of the elements, and ultimately influence how they get drawn on the map. To understand more about tags, and different ways of working with this data format refer to the following pages on the OpenStreetMap wiki.

• .osm - About the raw XML data format of OpenStreetMap
• Downloading data - More details of the API and extract download options
• Xapi - The extended api for filtering by tag
• Map Features - A list of tags, and what map features they represent
• Converting map data between formats - catalogue of tools for converting from .osm to other formats

Simple embedded maps

Rather than working with raw map data, you may prefer to embed maps from OpenStreetMap on your website with a simple bit of javascript. You can also present overlays of other data, in a manner very similar to working with google maps. In fact you can even use the google maps API to do this. See OSM on your own website for details and links to various javascript map libraries.

Help build the map!

The OpenStreetMap project aims to attract large numbers of contributors who all chip in a little bit to help build the map. Although the map editing tools take a little while to learn, they are designed to be as simple as possible, so that everyone can get involved. This project offers an exciting means of allowing local London communities to take ownership of their part of the map.

Read about how to Get Involved and see the London page for details of OpenStreetMap community events.

Sensitivity maps made by the ODONAT Grand Est network in 2018-2019.

The distribution of the species is represented from recent occurrence data (1999-2018 or 2009-2018 by species).

These are natural areas in which at least one observation of the species has been carried out in the recent period, as well as natural regions where the species is highly suspected (i.e. experts) or has older data.

In each of the natural regions with recent non-marginal observations, this presence is represented by the calculation of the proportion of 1 x 1 km meshes in which the species was observed. For an explanation of the method of calculation, refer to the Natural Regions Map Explanation Sheet.

Natural regions identify areas in which abiotic conditions (relief, geology, climate...) are relatively homogeneous.

In fact, the observation of a species in a natural region (even at a single location) provides a strong presumption of other favourable habitats elsewhere in the natural region.

Any observations shall be taken into account: they can be implanted populations, but also erratic individuals.

This layer represents the state of knowledge at the time of its realisation, it should not be considered exhaustive. The presence of the species outside the identified areas is possible.

Refer to the card reading instructions as well as PDF cards for more information.

This is a simple map that uses map services of the National Hydrography Dataset (NHD) and Watershed Boundaries Dataset (WBD) from The National Map (TNM). Additional layers of the current US Topo and older USA Topo service from Esri are included, but turned off by default. This map is useful as a simple viewer to see the content of the NHD and WBD. In the Search tool pulldown, the “nhd - Flowline - Large Scale” and “nhd - Waterbody - Large Scale” choices search based on ReachCode. The “wbd - 8-digit HU (Subbasin)” and “wbd - 12-digit HU (Subwatershed)” choices search based on the HU code (HUC).

This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition of SNOMED CT is the official source of SNOMED CT for use in US healthcare systems. The US Edition is a standalone release that combines the content of both the US Extension and the International release of SNOMED CT.

A Simple Map Reference set is used to represent one-to-one maps between SNOMED CT concepts and codes in another terminology, classification or code system.

Sensitivity maps made by the ODONAT Grand Est network in 2018-2019. The distribution of the species is represented from recent occurrence data (1999-2018 or 2009-2018 by species). These are natural areas in which at least one observation of the species has been carried out in the recent period, as well as natural regions where the species is highly suspected (i.e. experts) or has older data. In each of the natural regions with recent non-marginal observations, this presence is represented by the calculation of the proportion of 1 x 1 km meshes in which the species was observed. For an explanation of the method of calculation, refer to the Natural Regions Map Explanation Sheet. Natural regions identify areas in which abiotic conditions (relief, geology, climate...) are relatively homogeneous. In fact, the observation of a species in a natural region (even at a single location) provides a strong presumption of other favourable habitats elsewhere in the natural region. Any observations shall be taken into account: they can be implanted populations, but also erratic individuals.

This layer represents the state of knowledge at the time of its realisation, it should not be considered exhaustive. The presence of the species outside the identified areas is possible.

Refer to the card reading instructions as well as PDF cards for more information.

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)

VegMachine is an online tool that uses satellite imagery to summarise decades of change in Australia’s landscape. It’s simple to operate, easy to understand, and free to use.

With VegMachine you can: view satellite image land cover products; measure land cover change and fire scars; generate comprehensive ground cover monitoring reports and better understand the links between management, climate and vegetation cover.

With the White House release of guidelines for states to reopen and employees to gradually return to work, facilities are tasked with complex challenges. Managers must make decisions to ensure a safe work environment and adhere to social distancing requirements. Office layouts must be restructured for adequate spacing between workspaces and to allow for routing that minimizes close-proximity encounters. Clear communication with staff will also be a key factor: Which areas should be avoided? When has an area last be cleaned?The ArcGIS Indoors system from Esri can help answer these geospatially focused questions for reopening the workplace. With indoor maps and an indoor positioning system, managers can create a floor-plan level awareness of the workplace, one that will allow for safe reopening._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

These maps also referred to as “type A maps” represent for the year 2017 in the form of isophone curves, the areas exposed to more than 55 dB(A) according to the Lden indicator, with a step of 5 in 5 dB(A). They concern the road network of Côte-d’Or. Lden: sound level indicator means Level Day-Evening-Night. It corresponds to an equivalent 24-hour sound level in which evening and night noise levels are increased by 5 and 10 dB(A), respectively, to reflect greater discomfort during these periods.

GeoSearch is an interactive mapping application that makes it easy to find places in Canada, see them on a map, and get basic geographic and demographic data for them.

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.

In the fall of 2013, the Detroit Blight Removal Task Force commissioned Data Driven Detroit, the Michigan Nonprofit Association, and LOVELAND Technologies to conduct a survey of every parcel in the City of Detroit. The goal of the survey was to collect data on property condition and vacancy. The effort, called Motor City Mapping, leveraged relationships with the Rock Ventures family of companies and the Detroit Employment Solutions Corporation to assemble a dedicated team of over 200 resident surveyors, drivers, and quality control associates. Data collection occurred from December 4, 2013 until February 16, 2014, and the initiative resulted in survey information for over 370,000 parcels of land in the city of Detroit, identifying condition, occupancy, and use. The data were then extensively reviewed by the Motor City Mapping quality control team, a process that concluded on September 30, 2014. This file contains the official certified results from the Winter 2013/2014 survey, aggregated to 2010 Census Tracts for easy mapping and analysis. The topics covered in the dataset include totals and calculated percentages for parcels in the categories of illegal dumping, fire damage, structural condition, existence of a structure or accessory structure, and improvements on lots without structures.Metadata associated with this file includes field description metadata and a narrative summary documenting the process of creating the dataset.

TDEC is continuously striving to create better business practices through GIS and one way that we have found to provide information and answer some question is utilizing an interactive map. An interactive map is a display of geospatial data that allows you to manipulate and query the contents to get the information needed using a set of provided tools. Interactive maps are created using GIS software, and then distributed to users, usually over a computer network. The TDEC Land and Water interactive map will allow you to do simple tasks such as pan, zoom, measure and find a lat/long, while also giving you the capability of running simple queries to locate land and waters by name, entity, and number. With the ability to turn off and on back ground images such as aerial imagery (both black and white as well as color), we hope that you can find much utility in the tools provided.

Abstract This research investigates subjective user preference for using Floor Plans and Schematic Maps in an indoor environment, and how users locate and orient themselves when using these representations. We sought to verify the efficiency of these two kinds of digital maps and evaluate which elements found in physical environments and which elements found in the representations influence the user spatial orientation process. Users answered questions and performed orientation tasks which indicated their level of familiarity with the area being studied, their understanding of the symbology used, and their identification of Points of Interest (POI) in the environment. The initial results indicated a preference for the Schematic Map, because users thought that the symbology used on the map adopted was easy to understand.

Mapping of rivers and non-stream water points in Puy-de-Dôme prepared in accordance with the Government Instruction of 3 June 2015 on the mapping and identification of rivers and their maintenance and the Ministerial Orders of 04/05/2017 and Prefectural of 05/07/2017 on untreated areas.

Based on the definition of the watercourse (constitutes a stream, a flow of running water in a natural bed originally fed by a source and having a sufficient flow of much of the year) and the definition of water points (spray, beef and water body), a mapping project is proposed in the interactive map classifying the hydrographic sections and water surfaces of the IGN TOPO BD into four categories: — watercourses for the application of Articles L214-1 to L214-6 of the Environmental Code — the sections that need to be examined to determine whether they meet the definition of watercourse — non-stream water points for which an untreated area is to be set up — non-stream sections that need to be examined to determine whether they meet the definition of a water point within the meaning of the untreated area

Based on the definition of the watercourse (constitutes a stream, a flow of running water in a natural bed originally fed by a source and having a sufficient flow of much of the year) and the definition of water points (spray, beef and water body), a mapping project is proposed in the interactive map classifying the hydrographic sections and water surfaces of the IGN TOPO BD into four categories: — watercourses for the application of Articles L214-1 to L214-6 of the Environmental Code — the sections that need to be examined to determine whether they meet the definition of watercourse — non-stream water points for which an untreated area is to be set up — non-stream sections that need to be examined to determine whether they meet the definition of a water point within the meaning of the untreated area

This EnviroAtlas dataset shows the block group population that is within and beyond an easy walking distance (500m) of a park entrance. Park entrances were included in this analysis if they were within 5km of the EnviroAtlas community boundary. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas ) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets ).

Sensitivity maps made by the ODONAT Grand Est network in 2018-2019. The distribution of the species is represented from recent occurrence data (1999-2018 or 2009-2018 by species). These are natural areas in which at least one observation of the species has been carried out in the recent period, as well as natural regions where the species is highly suspected (i.e. experts) or has older data. In each of the natural regions with recent non-marginal observations, this presence is represented by the calculation of the proportion of 1 x 1 km meshes in which the species was observed. For an explanation of the method of calculation, refer to the Natural Regions Map Explanation Sheet. Natural regions identify areas in which abiotic conditions (relief, geology, climate...) are relatively homogeneous. In fact, the observation of a species in a natural region (even at a single location) provides a strong presumption of other favourable habitats elsewhere in the natural region. Any observations shall be taken into account: they can be implanted populations, but also erratic individuals.

This layer represents the state of knowledge at the time of its realisation, it should not be considered exhaustive. The presence of the species outside the identified areas is possible.

Refer to the card reading instructions as well as PDF cards for more information.

Sensitivity maps made by the ODONAT Grand Est network in 2018-2019. The distribution of the species is represented from recent occurrence data (1999-2018 or 2009-2018 by species). These are natural areas in which at least one observation of the species has been carried out in the recent period, as well as natural regions where the species is highly suspected (i.e. experts) or has older data. In each of the natural regions with recent non-marginal observations, this presence is represented by the calculation of the proportion of 1 x 1 km meshes in which the species was observed. For an explanation of the method of calculation, refer to the Natural Regions Map Explanation Sheet. Natural regions identify areas in which abiotic conditions (relief, geology, climate...) are relatively homogeneous. In fact, the observation of a species in a natural region (even at a single location) provides a strong presumption of other favourable habitats elsewhere in the natural region. Any observations shall be taken into account: they can be implanted populations, but also erratic individuals.

This layer represents the state of knowledge at the time of its realisation, it should not be considered exhaustive. The presence of the species outside the identified areas is possible.

Refer to the card reading instructions as well as PDF cards for more information.