The dataset and the validation are fully described in a Nature Scientific Data Descriptor https://www.nature.com/articles/s41597-019-0265-5

If you want to use this dataset in an interactive environment, then use this link https://mybinder.org/v2/gh/GeographerAtLarge/TravelTime/HEAD

The following text is a summary of the information in the above Data Descriptor.

The dataset is a suite of global travel-time accessibility indicators for the year 2015, at approximately one-kilometre spatial resolution for the entire globe. The indicators show an estimated (and validated), land-based travel time to the nearest city and nearest port for a range of city and port sizes.

The datasets are in GeoTIFF format and are suitable for use in Geographic Information Systems and statistical packages for mapping access to cities and ports and for spatial and statistical analysis of the inequalities in access by different segments of the population.

These maps represent a unique global representation of physical access to essential services offered by cities and ports.

The datasets travel_time_to_cities_x.tif (where x has values from 1 to 12) The value of each pixel is the estimated travel time in minutes to the nearest urban area in 2015. There are 12 data layers based on different sets of urban areas, defined by their population in year 2015 (see PDF report).

travel_time_to_ports_x (x ranges from 1 to 5)

The value of each pixel is the estimated travel time to the nearest port in 2015. There are 5 data layers based on different port sizes.

Format Raster Dataset, GeoTIFF, LZW compressed Unit Minutes

Data type Byte (16 bit Unsigned Integer)

No data value 65535

Flags None

Spatial resolution 30 arc seconds

Spatial extent

Upper left -180, 85

Lower left -180, -60 Upper right 180, 85 Lower right 180, -60 Spatial Reference System (SRS) EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)

Temporal resolution 2015

Temporal extent Updates may follow for future years, but these are dependent on the availability of updated inputs on travel times and city locations and populations.

Methodology Travel time to the nearest city or port was estimated using an accumulated cost function (accCost) in the gdistance R package (van Etten, 2018). This function requires two input datasets: (i) a set of locations to estimate travel time to and (ii) a transition matrix that represents the cost or time to travel across a surface.

The set of locations were based on populated urban areas in the 2016 version of the Joint Research Centre’s Global Human Settlement Layers (GHSL) datasets (Pesaresi and Freire, 2016) that represent low density (LDC) urban clusters and high density (HDC) urban areas (https://ghsl.jrc.ec.europa.eu/datasets.php). These urban areas were represented by points, spaced at 1km distance around the perimeter of each urban area.

Marine ports were extracted from the 26th edition of the World Port Index (NGA, 2017) which contains the location and physical characteristics of approximately 3,700 major ports and terminals. Ports are represented as single points

The transition matrix was based on the friction surface (https://map.ox.ac.uk/research-project/accessibility_to_cities) from the 2015 global accessibility map (Weiss et al, 2018).

Code The R code used to generate the 12 travel time maps is included in the zip file that can be downloaded with these data layers. The processing zones are also available.

Validation The underlying friction surface was validated by comparing travel times between 47,893 pairs of locations against journey times from a Google API. Our estimated journey times were generally shorter than those from the Google API. Across the tiles, the median journey time from our estimates was 88 minutes within an interquartile range of 48 to 143 minutes while the median journey time estimated by the Google API was 106 minutes within an interquartile range of 61 to 167 minutes. Across all tiles, the differences were skewed to the left and our travel time estimates were shorter than those reported by the Google API in 72% of the tiles. The median difference was −13.7 minutes within an interquartile range of −35.5 to 2.0 minutes while the absolute difference was 30 minutes or less for 60% of the tiles and 60 minutes or less for 80% of the tiles. The median percentage difference was −16.9% within an interquartile range of −30.6% to 2.7% while the absolute percentage difference was 20% or less in 43% of the tiles and 40% or less in 80% of the tiles.

This process and results are included in the validation zip file.

Usage Notes The accessibility layers can be visualised and analysed in many Geographic Information Systems or remote sensing software such as QGIS, GRASS, ENVI, ERDAS or ArcMap, and also by statistical and modelling packages such as R or MATLAB. They can also be used in cloud-based tools for geospatial analysis such as Google Earth Engine.

The nine layers represent travel times to human settlements of different population ranges. Two or more layers can be combined into one layer by recording the minimum pixel value across the layers. For example, a map of travel time to the nearest settlement of 5,000 to 50,000 people could be generated by taking the minimum of the three layers that represent the travel time to settlements with populations between 5,000 and 10,000, 10,000 and 20,000 and, 20,000 and 50,000 people.

The accessibility layers also permit user-defined hierarchies that go beyond computing the minimum pixel value across layers. A user-defined complete hierarchy can be generated when the union of all categories adds up to the global population, and the intersection of any two categories is empty. Everything else is up to the user in terms of logical consistency with the problem at hand.

The accessibility layers are relative measures of the ease of access from a given location to the nearest target. While the validation demonstrates that they do correspond to typical journey times, they cannot be taken to represent actual travel times. Errors in the friction surface will be accumulated as part of the accumulative cost function and it is likely that locations that are further away from targets will have greater a divergence from a plausible travel time than those that are closer to the targets. Care should be taken when referring to travel time to the larger cities when the locations of interest are extremely remote, although they will still be plausible representations of relative accessibility. Furthermore, a key assumption of the model is that all journeys will use the fastest mode of transport and take the shortest path.

This layer shows workers' place of residence by commute length. 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 percentage of commuters whose commute is 90 minutes or more. 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): B08303Data 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 travel time map was generated using the Pedestrian Evacuation Analyst model from the USGS. The travel time analysis uses ESRI's Path Distance tool to find the shortest distance across a cost surface from any point in the hazard zone to a safe zone. This cost analysis considers the direction of movement and assigns a higher cost to steeper slopes, based on a table contained within the model. The analysis also adds in the energy costs of crossing different types of land cover, assuming that less energy is expended walking along a road than walking across a sandy beach. To produce the time map, the evacuation surface output from the model is grouped into 1-minute increments for easier visualization. The times in the attribute table represent the estimated time to travel on foot to the nearest safe zone at the speed designated in the map title. The bridge or nobridge name in the map title identifies whether bridges were represented in the modeling or whether they were removed prior to modeling to estimate the impact on travel times from earthquake-damaged bridges.

Market Overview: The global High Precision Smart Travel Digital Map market is expected to reach XXX million by 2033, exhibiting a CAGR of xx% during the forecast period. This growth is attributed to the increasing adoption of autonomous vehicles, advancements in digital mapping technologies, and the growing demand for accurate and reliable navigation systems. The market is segmented based on type (online map, offline map) and application (commercial car, passenger car, others). North America and Asia-Pacific are the dominant markets, with key players including TomTom, Intel, NVIDIA, Sanborn, Waymo, and NavInfo Co., Ltd. Growth Drivers and Restrains: The key drivers of the High Precision Smart Travel Digital Map market include the advancements in artificial intelligence (AI) and machine learning (ML) technologies, which enable the creation of more accurate and detailed maps. Additionally, the increasing demand for real-time traffic information and the rise of ride-sharing services are further driving market growth. However, factors such as the high cost of implementation and the need for continuous updates pose challenges to the market growth.

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.

This layer contains 30 minute driving times from each SAMHSA treatment center in Tennessee. This map depicts the locations of SAMHSA Treatment Programs in Tennessee as of 09/18/2019. The map also contains 60 and 30 minute drive time analysis polygons and 30 minute walking analysis polygons.Data was downloaded from https://dpt2.samhsa.gov/treatment/ and geocoded in ArcGIS Online. Locations have not been verified. Drive and walking time polygons were generated in ArcGIS Online.

The global real-time maps market is projected to reach XXX million by 2033, expanding at a CAGR of XX% during 2025-2033. The growth is attributed to the increasing demand for Advanced Driver Assistance Systems (ADAS) and autonomous vehicles, along with the rising adoption of mobile mapping services. Crowdsourcing and centralized mode are the two prominent types of real-time maps, with crowdsourcing gaining popularity due to its cost-effectiveness and extensive data coverage. Key market players include HERE, TomTom, Google, Alibaba (AutoNavi), Navinfo, Mobileye, Sanborn, Baidu, and others. North America and Europe are well-established markets for real-time mapping, while the Asia Pacific region is expected to witness significant growth in the coming years. The increasing adoption of smartphones and the growing demand for geospatial data for various applications are driving the adoption of real-time maps in this region. The market is anticipated to witness ongoing technological advancements, including the integration of artificial intelligence (AI) and machine learning for improved map accuracy and personalization.

The global high-precision map solutions market is experiencing robust growth, driven by the burgeoning demand for autonomous driving technologies and advancements in urban planning initiatives. The market, currently valued at approximately $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033. This significant expansion is fueled by several key factors, including the increasing adoption of autonomous vehicles, the need for sophisticated navigation systems in smart cities, and the rising investments in infrastructure development globally. The demand for real-time map updates and dynamic map solutions is particularly strong, reflecting the need for up-to-the-minute accuracy in applications requiring precise location data. While the static map segment still holds a significant market share, the dynamic and real-time map segments are poised for substantial growth due to their critical role in enabling autonomous navigation and dynamic urban management. Technological advancements, such as improved sensor technologies (LiDAR, GPS, etc.) and the development of advanced algorithms for data processing and map generation, are further contributing to market growth. However, challenges remain, including the high cost of data acquisition and processing, concerns surrounding data security and privacy, and the need for standardization in map formats and data sharing protocols. Nevertheless, the long-term outlook for the high-precision map solutions market remains extremely positive, with continuous innovation and increased adoption expected to drive substantial market expansion throughout the forecast period. Competitive intensity is expected to increase with both established players and new entrants vying for market share.

The high-precision real-time map market is experiencing robust growth, driven by the increasing demand for autonomous driving, advanced driver-assistance systems (ADAS), and precise location-based services. The market's expansion is fueled by technological advancements in sensor technologies (LiDAR, radar, cameras), improved mapping techniques, and the proliferation of connected vehicles. Key applications include automotive driving, tracking & positioning, and mobile phones, with the automotive sector currently dominating due to the surge in autonomous vehicle development. The 3D segment is projected to witness significant growth, exceeding the 2D and 2.5D segments in the coming years, owing to its ability to provide more detailed and accurate representations of the environment, crucial for autonomous navigation and precise location services. Geographic regions like North America and Europe are currently leading the market, driven by early adoption of autonomous vehicle technologies and well-established infrastructure for data collection and processing. However, rapid technological advancements and government initiatives supporting autonomous driving are driving market expansion in the Asia-Pacific region, with China and India emerging as key growth markets. While data security and privacy concerns present potential restraints, the overall market outlook remains positive, with a projected compound annual growth rate (CAGR) indicating substantial market expansion through 2033. Competition among major players like TomTom, Google, and Baidu is intensifying, leading to continuous innovation and the development of more sophisticated and accurate mapping solutions. The market segmentation by type (2D, 2.5D, 3D) reveals a clear shift towards higher-dimensionality maps. While 2D maps still hold a significant share, 3D mapping technology is rapidly gaining traction due to its enhanced capabilities for autonomous navigation and detailed environmental modeling. The application-based segmentation underscores the importance of the automotive sector, particularly autonomous vehicles, as the primary driver of market growth. However, other sectors like mobile phones and tracking & positioning are also contributing significantly, fostering a diversified market landscape. The ongoing development of 5G and edge computing infrastructure further accelerates the market's growth by facilitating real-time data processing and transmission, enhancing the accuracy and responsiveness of high-precision real-time maps. The competitive landscape is characterized by both established mapping companies and emerging technology providers, driving innovation and potentially leading to further market consolidation in the coming years.

The friction (cost allocation/effort) surface was assembled using three primary input datasets on land surface characteristics that help or hinder travel speeds: land cover, roads and topography. Landcover data were from the ESA CCI Landcover map for Africa 2016, roads data were merged from Open-Street Map (OSM) and the MapwithAi project and topography was taken from the SRTM Digital Elevation Model. The costs for travel consider walking/pedestrian travel in this data, but the software is supplied with an easy to change set of travel speeds so they can be adapted easily to consider travel speeds reflecting motorised transportation use. We have reduced the walking speeds to reflect the fact that adults walking with children move approximately 22% slower. There are two friction surfaces provided, the first defines open water as a barrier to travel and so the speed allocated to this landcover is NA. The second defines open water with an associated speed (1 km/hr). To create a walking speed array, first the road walking speeds were used and then missing values were filled with landcover walking speed values. This walking speed array was multiplied by the slope impact grid. The speed for each cell was converted from kilometers per hour to meters per second. Finally, the time (in seconds) to walk across each cell was calculated. The outputs are 20-m spatial resolution geotiffs indicating the time to walk across each cell. They are subsequently used in the least cost path analysis to estimate travel time to the nearest health facilities. However,these friction surfaces can be used by others to estimate travel speed to other destinations in a GIS.

The self-driving 3D high-precision map market is experiencing rapid growth, driven by the increasing adoption of autonomous vehicles and advanced driver-assistance systems (ADAS). This market is projected to reach a substantial size, with a Compound Annual Growth Rate (CAGR) reflecting significant expansion over the forecast period of 2025-2033. Let's assume, based on typical growth rates observed in similar technological sectors and considering the considerable investment in autonomous vehicle technology, a market size of $5 billion in 2025 and a CAGR of 25% is reasonable. This implies a market value exceeding $20 billion by 2033. Key drivers include the rising demand for safer and more efficient transportation solutions, advancements in sensor technologies (LiDAR, radar, cameras), and the continuous development of sophisticated mapping algorithms. The market is segmented by crowdsourcing model versus centralized model and by application, encompassing levels L1/L2+ and L3 driving automation, alongside other emerging applications. Major players like TomTom, Google, Alibaba (AutoNavi), Navinfo, Mobileye, Baidu, and NVIDIA are actively shaping this landscape through innovative mapping solutions and strategic partnerships. The regional distribution shows significant concentration in North America and Asia Pacific, particularly in the United States and China, fueled by robust technological advancements and supportive government regulations. The market's growth trajectory is influenced by several trends, including the increasing availability of high-resolution satellite imagery and aerial photography for map creation, the development of real-time map updates based on vehicle data, and the integration of artificial intelligence (AI) for enhanced map accuracy and efficiency. However, challenges such as high data acquisition and processing costs, data privacy concerns, and the need for continuous map updates represent significant restraints. The future growth of the self-driving 3D high-precision map market will heavily depend on the continued progress in autonomous driving technology, the scalability of crowdsourcing solutions, and the ability to overcome regulatory and technological hurdles. The integration of 5G technology promises to further accelerate growth by enabling faster data transmission and real-time map updates, making the autonomous driving experience smoother and safer.

The high-precision map solutions market is experiencing significant growth, driven by the increasing demand for autonomous driving, advanced driver-assistance systems (ADAS), and precise urban planning. The market, currently estimated at $2 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This robust growth is fueled by several key factors. The automotive industry's heavy investment in autonomous vehicle technology is a major catalyst, requiring highly accurate and detailed maps for safe and efficient navigation. Furthermore, the burgeoning smart city initiatives worldwide necessitate precise mapping solutions for infrastructure management, traffic optimization, and emergency response systems. The market's segmentation into static, dynamic, and real-time maps caters to diverse application needs, each segment contributing to the overall market expansion. Static maps provide a foundational layer, while dynamic and real-time maps offer enhanced accuracy and responsiveness for applications requiring up-to-the-minute data. The key players in this market, including Albedo, Blackshark.ai, Skycatch, Inc., Momenta, Navmii, CivilMaps, Hyperspec AI, EyeVi Technologies, Red Leader Technologies, Inc., and DeepMap, are continuously innovating to improve map accuracy, data acquisition techniques, and data processing capabilities. Competition is intensifying, leading to increased investment in research and development, resulting in more sophisticated and cost-effective solutions. While data security and privacy concerns pose potential restraints, ongoing advancements in data encryption and anonymization techniques are mitigating these risks. Geographic expansion, particularly in developing economies with rapidly growing urban populations, presents significant opportunities for market growth. The diverse range of applications across various sectors, coupled with continuous technological advancements, points towards a promising future for the high-precision map solutions market.

The HD Map Autonomous Vehicle Market is projected to expand at a 19.38% CAGR during the forecast period, reaching a value of $4.06 billion by 2033. The market growth is attributed to the increasing adoption of autonomous vehicles, which require high-definition maps for accurate navigation and path planning. Moreover, government regulations mandating the use of HD maps in autonomous vehicles are further driving the market growth. Key trends shaping the market include the integration of sensor fusion technology for real-time map updates, the adoption of cloud-based mapping for efficient data management, and the development of in-vehicle mapping for enhanced personalization. Major players in the market include Renovo, Mapbox, Waymo, Intel, Atlanter, Qualcomm, Ground Truth, Tesla, Aurora, Mobileye, ZF Friedrichshafen, NVIDIA, Bosch, TomTom, and HERE Technologies. The market is segmented by application (navigation, obstacle detection, traffic management, fleet management), end use (personal vehicles, commercial fleets, public transport), technology (sensor fusion, cloud-based mapping, in-vehicle mapping, real-time updating), and vehicle type (passenger cars, light commercial vehicles, heavy commercial vehicles, buses). Recent developments include: , Recent developments in the HD Map Autonomous Vehicle Market indicate significant advances and investments from key players. Waymo is enhancing its mapping technology to improve safety and efficiency, while Intel is focusing on integration with its mobility solutions. Mapbox recently launched new mapping tools aimed at optimizing the development of autonomous vehicle applications. Qualcomm's innovations in automotive chipsets have also accelerated mapping processes. Tesla continues to refine its self-driving technology, pushing the boundaries of HD mapping in real-time navigation., Merger and acquisition activities have seen companies like NVIDIA exploring partnerships to bolster data processing capabilities, while Bosch has been expected to expand its mapping business through collaborations. Current market dynamics reflect a growing interest in precision mapping, necessitating companies to invest in advanced technologies and partnerships. The market’s valuation has witnessed an upward trend as demand for more accurate and reliable mapping systems intensifies, with players such as Aurora and Mobileye positioning themselves for future growth. As a result, market players are increasingly collaborating, forming alliances, and acquiring relevant companies to strengthen their offerings in this rapidly evolving sector., HD Map Autonomous Vehicle Market Segmentation Insights. Key drivers for this market are: Increased demand for autonomous vehicles, Growth in smart city initiatives; Expansion of V2X communication technology; Rising investment in AI integration; Enhanced safety regulations and standards. Potential restraints include: Technological advancements in mapping, Increasing demand for automation; Rising investment in autonomous vehicles; Regulatory support for self-driving cars; Growing partnerships in mapping ecosystem.

This is a collection of maps, layers, apps and dashboards that show population access to essential retail locations, such as grocery stores. Data sourcesPopulation data is from the 2010 U.S. Census blocks. Each census block has a count of stores within a 10 minute walk, and a count of stores within a ten minute drive. Census blocks known to be unpopulated are given a score of 0. The layer is available as a hosted feature layer.Grocery store locations are from SafeGraph, reflecting what was in the data as of October 2020. Access to the layer was obtained from the SafeGraph offering in ArcGIS Marketplace. For this project, ArcGIS StreetMap Premium was used for the street network in the origin-destination analysis work, because it already has the necessary attributes on each street segment to identify which streets are considered walkable, and supports a wide variety of driving parameters.The walkable access layer and drivable access layers are rasters, whose colors were chosen to allow the drivable access layer to serve as backdrop to the walkable access layer. Alternative versions of these layers are available. These pairs use different colors but are otherwise identical in content.Data PreparationArcGIS Network Analyst was used to set up a network street layer for analysis. ArcGIS StreetMap Premium was installed to a local hard drive and selected in the Origin-Destination workflow as the network data source. This allows the origins (Census block centroids) and destinations (SafeGraph grocery stores) to be connected to that network, to allow origin-destination analysis.The Census blocks layer contains the centroid of each Census block. The data allows a simple popup to be created. This layer's block figures can be summarized further, to tract, county and state levels.The SafeGraph grocery store locations were created by querying the SafeGraph source layer based on primary NAICS code. After connecting to the layer in ArcGIS Pro, a definition query was set to only show records with NAICS code 445110 as an initial screening. The layer was exported to a local disk drive for further definition query refinement, to eliminate any records that were obviously not grocery stores. The final layer used in the analysis had approximately 53,600 records. In this map, this layer is included as a vector tile layer.MethodologyEvery census block in the U.S. was assigned two access scores, whose numbers are simply how many grocery stores are within a 10 minute walk and a 10 minute drive of that census block. Every census block has a score of 0 (no stores), 1, 2 or more stores. The count of accessible stores was determined using Origin-Destination Analysis in ArcGIS Network Analyst, in ArcGIS Pro. A set of Tools in this ArcGIS Pro package allow a similar analysis to be conducted for any city or other area. The Tools step through the data prep and analysis steps. Download the Pro package, open it and substitute your own layers for Origins and Destinations. Parcel centroids are a suggested option for Origins, for example. Origin-Destination analysis was configured, using ArcGIS StreetMap Premium as the network data source. Census block centroids with population greater than zero were used as the Origins, and grocery store locations were used as the Destinations. A cutoff of 10 minutes was used with the Walk Time option. Only one restriction was applied to the street network: Walkable, which means Interstates and other non-walkable street segments were treated appropriately. You see the results in the map: wherever freeway overpasses and underpasses are present near a grocery store, the walkable area extends across/through that pass, but not along the freeway.A cutoff of 10 minutes was used with the Drive Time option. The default restrictions were applied to the street network, which means a typical vehicle's access to all types of roads was factored in.The results for each analysis were captured in the Lines layer, which shows which origins are within the cutoff of each destination over the street network, given the assumptions about that network (walking, or driving a vehicle).The Lines layer was then summarized by census block ID to capture the Maximum value of the Destination_Rank field. A census block within 10 minutes of 3 stores would have 3 records in the Lines layer, but only one value in the summarized table, with a MAX_Destination_Rank field value of 3. This is the number of stores accessible to that census block in the 10 minutes measured, for walking and driving. These data were joined to the block centroids layer and given unique names. At this point, all blocks with zero population or null values in the MAX_Destination_Rank fields were given a store count of 0, to help the next step.Walkable and Drivable areas are calculated into a raster layer, using Nearest Neighbor geoprocessing tool on the count of stores within a 10 minute walk, and a count of stores within a ten minute drive, respectively. This tool uses a 200 meter grid and interpolates the values between each census block. A census tracts layer containing all water polygons "erased" from the census tract boundaries was used as an environment setting, to help constrain interpolation into/across bodies of water. The same layer use used to "shoreline" the Nearest Neighbor results, to eliminate any interpolation into the ocean or Great Lakes. This helped but was not perfect.Notes and LimitationsThe map provides a baseline for discussing access to grocery stores in a city. It does not presume local population has the desire or means to walk or drive to obtain groceries. It does not take elevation gain or loss into account. It does not factor time of day nor weather, seasons, or other variables that affect a person's commute choices. Walking and driving are just two ways people get to a grocery store. Some people ride a bike, others take public transit, have groceries delivered, or rely on a friend with a vehicle. Thank you to Melinda Morang on the Network Analyst team for guidance and suggestions at key moments along the way; to Emily Meriam for reviewing the previous version of this map and creating new color palettes and marker symbols specific to this project. Additional ReadingThe methods by which access to food is measured and reported have improved in the past decade or so, as has the uses of such measurements. Some relevant papers and articles are provided below as a starting point.Measuring Food Insecurity Using the Food Abundance Index: Implications for Economic, Health and Social Well-BeingHow to Identify Food Deserts: Measuring Physical and Economic Access to Supermarkets in King County, WashingtonAccess to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their ConsequencesDifferent Measures of Food Access Inform Different SolutionsThe time cost of access to food – Distance to the grocery store as measured in minutes

The arrangement of water in the landscape affects the distribution of many species including the distribution of humans. This layer provides a landscape-scale estimate of the distance from large water bodies.Dataset SummaryThis layer provides access to a 250m cell-sized raster of distance to surface water. To facilitate mapping, the values are in units of pixels. To convert this value to meters multiply by 250. The layer was created by extracting surface water values from the World Lithology and World Land Cover layers to produce a surface water layer. The distance from water was calculated using the ArcGIS Euclidian Distance Tool. The layer was created by Esri in 2014.What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

Navigation Map Market Outlook

The global navigation map market size was valued at USD 20.5 billion in 2023 and is projected to reach USD 45.8 billion by 2032, exhibiting a Compound Annual Growth Rate (CAGR) of 9.3% during the forecast period. The primary growth factor propelling this market is the increasing integration of advanced mapping technologies in automotive and mobile device industries, aimed at enhancing navigation and user experience.

One of the key drivers of the navigation map market is the rapid technological advancements in digital mapping and Geographic Information Systems (GIS). Innovations such as real-time traffic updates, augmented reality (AR) navigation, and highly detailed 3D maps are significantly enhancing the functionality and user experience of navigation systems. These advancements are crucial for the development of autonomous driving technologies, which rely heavily on precise and real-time mapping data. The proliferation of smartphones equipped with GPS capabilities has also expanded the demand for high-quality digital maps, further fueling market growth.

Another significant growth factor is the increasing demand for navigation solutions in the automotive industry. As automakers strive to enhance driver safety and convenience, the integration of advanced navigation systems has become a standard feature in modern vehicles. The advent of connected cars, which communicate with external systems for real-time traffic and route information, is further driving the need for sophisticated navigation maps. Additionally, the growing trend of ride-hailing and logistics services has necessitated the use of accurate and efficient navigation solutions to optimize routes and improve operational efficiency.

The commercial sector is also contributing to the growth of the navigation map market. Businesses are increasingly relying on advanced mapping solutions to streamline their operations, manage logistics, and enhance customer service. For instance, companies in the e-commerce and delivery services sectors use navigation maps to ensure timely and efficient deliveries. Moreover, the government and public sector are adopting navigation maps for urban planning, disaster management, and public safety applications. These diverse applications across various sectors are collectively driving the demand for navigation maps, thereby contributing to market expansion.

Regionally, North America holds a significant share of the navigation map market, driven by the presence of major technology companies and high adoption rates of advanced navigation solutions. The Asia Pacific region is expected to exhibit the highest growth rate during the forecast period, attributed to the rapid urbanization, increasing smartphone penetration, and growing automotive industry in countries like China and India. Europe also represents a substantial market share, supported by stringent regulations on vehicle safety and the presence of leading automotive manufacturers. The Middle East & Africa and Latin America are gradually adopting advanced navigation technologies, presenting potential growth opportunities in these regions.

The evolution of High Precision Map technology is revolutionizing the navigation map market, particularly in the realm of autonomous vehicles and advanced driver-assistance systems. These maps provide an unparalleled level of detail, including lane-level accuracy and precise positioning, which are essential for the safe and efficient operation of self-driving cars. High Precision Maps are not only crucial for navigation but also for enhancing the overall driving experience by integrating real-time data and predictive analytics. This technology allows vehicles to anticipate road conditions, optimize routes, and improve fuel efficiency, thereby contributing to the broader goals of sustainability and safety in the automotive industry.

Product Type Analysis

The navigation map market is segmented into digital maps and paper maps, each catering to different user preferences and applications. Digital maps are the dominant segment, driven by the widespread use of smartphones, tablets, and in-car navigation systems. Digital maps offer real-time updates, interactive features, and the ability to integrate with other applications, making them highly popular among users. The continuous advancements in digital mapping technologies, such as 3D mapping, AR navigation, and real-time traffic information, are further enhancing the appeal and functionality of digi

The HD Live Map market is experiencing robust growth, projected to reach a market size of $1279 million in 2025 and exhibiting a remarkable Compound Annual Growth Rate (CAGR) of 24.8% from 2025 to 2033. This expansion is fueled by several key market drivers, including the increasing adoption of Advanced Driver-Assistance Systems (ADAS) and autonomous driving technologies in both commercial and military applications. The rising demand for precise and real-time location data for improved navigation, safety, and traffic management further contributes to this growth. Technological advancements, such as the development of high-resolution sensor technologies and improved data processing capabilities, are enhancing the accuracy and reliability of HD Live Maps, making them an indispensable component of next-generation vehicle systems. The market is segmented by crowdsourcing and centralized models, reflecting the varied approaches to data acquisition and map creation. Furthermore, application-based segmentation highlights the significant roles of commercial and military sectors, with the former encompassing automotive, logistics, and ride-sharing applications, while the latter emphasizes defense and security operations. Leading players such as TomTom, Google, Alibaba (AutoNavi), and Baidu are actively investing in R&D and strategic partnerships to consolidate their market positions. The competitive landscape is dynamic, with established players and emerging technology firms competing to deliver superior map data and services. The geographical distribution of the HD Live Map market is diverse, with North America and Asia Pacific expected to dominate due to significant investments in autonomous vehicle technology and robust infrastructure development. Europe is also a significant market, driven by strong government support for technological innovation and the growing adoption of connected car services. The market growth will be influenced by factors such as government regulations related to autonomous driving, the cost of data acquisition and processing, and the increasing integration of HD Live Maps into various smart city initiatives. The ongoing development of 5G networks and the rise of IoT devices are also expected to further stimulate market growth in the coming years. Continuous improvement in map accuracy and detail, coupled with wider industry adoption, will remain pivotal to the market's sustained expansion throughout the forecast period.

Real-time Maps Market Outlook

The global real-time maps market size was valued at approximately USD 5.3 billion in 2023 and is projected to reach USD 12.7 billion by 2032, growing at a compound annual growth rate (CAGR) of 10.2% during the forecast period. This robust growth is driven by increasing demand for accurate and instantaneous geographic data across various sectors, including transportation, logistics, automotive, and more.

One of the primary growth factors for the real-time maps market is the widespread adoption of smartphones and connected devices. The proliferation of these devices has created a surge in demand for navigation and location-based services, which rely heavily on real-time mapping technologies. Additionally, the advent of advanced technologies such as 5G and the Internet of Things (IoT) has further accelerated the need for real-time data, enhancing the accuracy and efficiency of mapping services. These technologies enable faster data transmission, thus providing users with up-to-date information in real-time.

Another significant growth factor is the increasing emphasis on smart city developments. Governments and urban planners across the globe are investing heavily in smart city projects, which require sophisticated mapping solutions for efficient traffic management, public transportation systems, and emergency services. Real-time maps are integral to these projects as they provide the necessary data for monitoring and managing urban infrastructure, thereby improving the quality of life for urban residents. Moreover, the integration of artificial intelligence (AI) and machine learning (ML) in mapping technologies is enhancing the capabilities of real-time maps, making them more predictive and adaptive.

The rise of autonomous vehicles is also a crucial driver for the real-time maps market. Autonomous and connected vehicles rely on real-time maps for navigation and timely decision-making. The automotive industry is investing significantly in mapping technologies to improve the safety and reliability of autonomous driving systems. Real-time maps provide these vehicles with up-to-date information about road conditions, traffic patterns, and potential hazards, which is essential for safe and efficient operation. This trend is expected to continue, driving further growth in the market.

Regionally, North America holds a significant share of the real-time maps market, driven by the presence of major technology companies and high adoption rates of advanced technologies. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The rapid urbanization, increasing investments in smart city projects, and the expanding automotive industry in countries like China and India are major contributors to this growth. Europe also presents substantial opportunities, particularly in the automotive and transportation sectors, where real-time mapping solutions are increasingly being adopted.

Component Analysis

The real-time maps market is segmented by component into software, hardware, and services. The software segment is expected to hold the largest market share during the forecast period. This can be attributed to the increasing demand for advanced mapping software that offers enhanced features such as real-time updates, high accuracy, and predictive analytics. Companies are continuously investing in software development to improve the functionality and user experience of their mapping solutions. The integration of AI and ML into mapping software is also a significant trend, enabling more intelligent and adaptive maps.

The hardware segment, though smaller compared to software, plays a crucial role in the real-time maps market. Hardware components such as GPS devices, sensors, and other tracking devices are essential for capturing and transmitting real-time data. The growth in the IoT market and advancements in sensor technologies have significantly improved the capabilities of hardware components, making them more accurate and reliable. This has led to increased adoption of real-time mapping hardware in various applications, particularly in the automotive and transportation sectors.

Services form the third component of the real-time maps market, encompassing a range of offerings such as consulting, implementation, and maintenance services. As companies adopt real-time mapping solutions, the demand for specialized services to ensure proper integration and functionality of these solutions has increased. Service providers offer expertise in customizing and optimizing mapping

The regional cities accessibility dataset is modelled as raster-based travel time/cost analysis. Individual cumulative travel time/cost maps were produced for major regional cities (>170k habitants) less than 100 km from the border. The following values were assumed: City - Country Namangan - UZB - 626,120 Samarkand - UZB - 546,303 Tashkent - UZB - 2,571,668 Mazar i sharif - AFG - 484,500 Fergana - UZB - 288,850 Kokand - UZB - 252,730 Altiarik - UZB - 210,515 Kunduz - AFG - 189,300 Termez - UZB - 179,572 Jizzax - UZB - 177,447 This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location).

The HD Live Map market is experiencing robust growth, projected to reach a market size of $6,016.4 million in 2025. While the provided CAGR is missing, considering the rapid advancements in autonomous driving technology, connected car infrastructure, and the increasing demand for precise location data across commercial and military applications, a conservative estimate for the CAGR during the forecast period (2025-2033) would be around 15%. This signifies a substantial expansion of the market, driven by factors such as the escalating adoption of Advanced Driver-Assistance Systems (ADAS) and autonomous vehicles, the rising need for real-time traffic updates in smart city initiatives, and the growing deployment of HD maps in military applications for enhanced situational awareness. The market segmentation reveals strong demand across both commercial and military sectors, with the crowdsourcing model gaining traction due to its cost-effectiveness and data aggregation capabilities. Key players like TomTom, Google, and Baidu are leading the market, continuously investing in R&D to enhance map accuracy, detail, and real-time updates. Regional variations are expected, with North America and Asia Pacific leading the market share due to higher technological adoption rates and substantial investments in infrastructure development. The continued growth in the HD Live Map market is further fueled by the increasing integration of 5G technology, which enables faster data transmission and real-time updates critical for autonomous driving and location-based services. Furthermore, government regulations promoting road safety and autonomous vehicle development are creating a favorable environment for market expansion. However, challenges such as high initial investment costs for map creation and maintenance, data security concerns, and the need for continuous map updates could potentially restrain growth to some extent. Nonetheless, the long-term outlook for the HD Live Map market remains positive, fueled by ongoing technological innovation and the increasing demand for precise location intelligence across diverse sectors.