Map Data Aggregation Platform Market Outlook

According to our latest research, the global map data aggregation platform market size in 2024 stands at USD 3.8 billion, with a robust compound annual growth rate (CAGR) of 14.2% projected through the forecast period. By 2033, the market is anticipated to reach approximately USD 12.2 billion, reflecting the rapid adoption of advanced geospatial technologies and the increasing demand for real-time mapping solutions. This impressive growth is primarily driven by the proliferation of location-based services, the expansion of smart city initiatives, and the integration of artificial intelligence and machine learning in map data processing.

The map data aggregation platform market is experiencing significant momentum due to the exponential rise in the use of mobile devices and connected vehicles, which generate vast quantities of location data daily. Organizations across various sectors are increasingly leveraging these platforms to gather, process, and analyze spatial information, enabling them to make informed decisions and optimize operations. The integration of IoT devices and the advent of 5G technology have further accelerated the collection and transmission of high-resolution geospatial data, enhancing the accuracy and timeliness of mapping solutions. Moreover, the growing need for seamless navigation, asset tracking, and personalized location-based advertising has created a fertile environment for the adoption of map data aggregation platforms.

Another major growth factor for the map data aggregation platform market is the surge in smart city projects worldwide, especially in emerging economies. Governments and municipal authorities are investing heavily in digital infrastructure to improve urban planning, transportation management, and public safety. By aggregating data from various sources such as satellite imagery, sensors, and user-generated content, these platforms provide actionable insights that support efficient resource allocation and enhance citizen engagement. Furthermore, the demand for real-time traffic updates, emergency response coordination, and predictive analytics in urban environments is fueling the need for advanced map data aggregation solutions.

The market is also witnessing a paradigm shift with the integration of artificial intelligence (AI) and machine learning (ML) algorithms into map data aggregation platforms. These technologies enable automated data cleansing, anomaly detection, and predictive modeling, significantly improving the quality and reliability of aggregated spatial data. As enterprises seek to harness the power of big data analytics for competitive advantage, the adoption of AI-driven map data platforms is expected to rise. Additionally, the increasing focus on data privacy and regulatory compliance is prompting vendors to develop secure and transparent aggregation processes, further boosting market confidence and adoption rates.

From a regional perspective, North America currently dominates the map data aggregation platform market, owing to the presence of major technology players, high digital literacy, and extensive investments in smart infrastructure. However, the Asia Pacific region is poised for the fastest growth, driven by rapid urbanization, expanding mobile internet penetration, and government-led digital transformation initiatives. Europe follows closely, with strong demand from transportation, utilities, and real estate sectors. Latin America and the Middle East & Africa are also emerging as promising markets, supported by growing investments in digital mapping and infrastructure modernization. Each region presents unique opportunities and challenges, shaping the competitive landscape and strategic priorities of market participants.

Component Analysis

The map data aggregation platform market is broadly segmented by component into software and services, each playing a critical role in the overall value chain. Software solutions form the backbone of map data aggregation, providing the necessary tools for data ingestion, normalization, visualization, and analytics. These platforms are designed to handle vast and heterogeneous data sources, ensuring seamless integration and high performance. The continuous evolution of software capabilities, including support for real-time data processing, cloud-native architectures, and advanced geospatial analytics, is driving market

Spatial data set of the plan FNP_Sottrum (Collection) This is a utility service for aggregating plan elements with one layer per XPlanung class. That of the last change is the 30.06.2018. The scopes of the change plans are summarized in the Scopes layer.

The Western and Central Fisheries Commission (WCPFC) have compiled a public domain version of aggregated catch and effort data using operational, aggregate and annual catch estimates data provided by Commission Members (CCMs) and Cooperating Non-members (CNMs). The data provided herein have been prepared for dissemination in accordance with the current “Rules and Procedures for the Protection, Access to, and Dissemination of Data Compiled by the Commission” or (“RAP”).

Paragraph 9 of the Rules and Procedures indicates that "Catch and Effort data in the public domain shall be made up of observations from a minimum of three vessels". However, the majority of aggregate data provided to WPCFC do not indicate how many vessels were active in each cell of data which would allow data to be directly filtered according to this rule. Instead, the individual cells where "effort" is less than or equal to the maximum value estimated to represent the activities of two vessels have been removed from the public domain data (the cells are retained with their time/area information, but all catch and effort information in these have been set to zero). Statistics showing how much data have been removed according to this RAP requirement are provided in the documentation for the longline and purse seine public domain data.

All public domain data have been aggregated by year/month and 5°x5° grid. Annex 2 of the RAP indicates that public domain aggregated catch/effort data can be made available at a higher resolution (e.g. data with a breakdown by vessel nation, and aggregated by 1°x1° grids for surface fisheries); however, if the public domain data were provided at these higher levels of resolution implementation of the RAP "three-vessel rule" with the current aggregate data set would result in too many cells being removed.

However, please note that the data that have been removed from the public domain dataset, available on this webpage, are still potentially accessible via other provisions of the RAP (refer to section 4.6 and para 34).

Each public domain zip file contains two files: (1) a CSV file containing the data; (2) a PDF file containing the field names/formats and the coverage with respect to the data file.

These data files were last updated on the 27th July 2020.

Aggregate data files digitized from the published census volumes for 1921. The files were downloaded from the University of Saskatchewan Historical Geographic Information Systems Lab. This data were developed as part of the The Canadian Peoples / Les populations canadiennes Project.

Spatial data set of the plan FNP_Bremervörde (Collection) This is a utility service for aggregating plan elements with one layer per XPlanung class. That of the last change is the 14.03.2020. The scopes of the change plans are summarized in the Scopes layer.

Developed by SOLARGIS (https://solargis.com) and provided by the Global Solar Atlas (GSA), this data resource contains optimum tilt to maximize yearly yield in (°) covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km). The data is hyperlinked under 'resources' with the following characteristics: OPTA - LTAy_AvgDailyTotals (GeoTIFF) Data format: GEOTIFF File size : 2.08 MB There are two temporal representation of solar resource and PVOUT data available: • Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals) • Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals) Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations: • LTAy_YearlyTotals = LTAy_DailyTotals * 365.25 • LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).

Spatial data set of the plan FNP_Schiffdorf (Collection) This is a utility service for the aggregation of plan elements with one layer per XPlanung class. That of the last change is the 21.12.2017. The scopes of the change plans are summarized in the Scopes layer.

This GIS dataset is a result of the compilation of all existing Alberta Geological Survey sand and gravel geology and resource data into digital format. Data sources include Alberta Geological Survey maps and reports produced between 1976 and 2006. References are provided as an attribute so the user can refer back to the original maps and reports. Attributes include study level, material description, references, area, sand and gravel thickness, and gravel and sand volumes. In 2009, data from newly mapped area NTS 83N/NE were added.

No Publication Abstract is Available

This GIS dataset is a result of the compilation of all existing Alberta Geological Survey sand and gravel geology and resource data into digital format. Data sources include Alberta Geological Survey maps and reports produced between 1976 and 2006. References are provided as an attribute so the user can refer back to the original maps and reports. Attributes include study level, material description, references, area, sand and gravel thickness, and gravel and sand volumes. In 2009, data from newly mapped area NTS 83N/NE were added.

On the 8th of September 2022 we carried out a search in the Web of Science with the search string “(Ripley's K function) AND (forest)”. The search yielded 356 hits. We screened those 356 studies for eligibility, first based on the suitability of their article titles and second based on their abstracts (Figure S1). The 240 eligible studies were subsequently screened manually upon reading the entire article based on the following inclusion criteria: (1) The study reported on univariate Ripley's K or L statistics or else it was possible to extract those from figures or maps. (2) The study had been carried out in a woody ecosystem or a rangeland. (3) The univariate Ripley’s K statistics described the distribution of individuals from a single plant species. (4) &...

ssurgoOnDemandThe purpose of these tools are to give users the ability to get Soil Survey Geographic Database (SSURGO) properties and interpretations in an efficient manner. They are very similiar to the United States Department of Agriculture - Natural Resource Conservation Service's distributed Soil Data Viewer (SDV), although there are distinct differences. The most important difference is the data collected with the SSURGO On-Demand (SOD) tools are collected in real-time via web requests to Soil Data Access (https://sdmdataaccess.nrcs.usda.gov/). SOD tools do not require users to have the data found in a traditional SSURGO download from the NRCS's official repository, Web Soil Survey (https://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm). The main intent of both SOD and SDV are to hide the complex relationships of the SSURGO tables and allow the users to focus on asking the question they need to get the information they want. This is accomplished in the user interface of the tools and the subsequent SQL is built and executed for the user. Currently, the tools packaged here are designed to run within the ESRI ArcGIS Desktop Application - ArcMap, version 10.1 or greater. However, much of the Python code is recyclable and could run within a Python intepreter or other GIS applications such as Quantum GIS with some modification.NOTE: The queries in these tools only consider the major components of soil map units.Within the SOD tools are 2 primary toolsets, descibed as follows:<1. AreasymbolThe Areasymbol tools collect SSURGO properties and interpretations based on a user supplied list of Soil Survey areasymbols (e.g. NC123). After the areasymbols have been collected, an aggregation method (see below) is selected . Tee aggregation method has no affect on interpretations other than how the SSURGO data aggregated. For soil properties, the aggregation method drives what properties can be run. For example, you can't run the weighted average aggregation method on Taxonomic Order. Similarly, for the same soil property, you wouldn't specify a depth range. The point here is the aggregation method affects what parameters need to be supplied for the SQL generation. It is important to note the user can specify any number of areasymbols and any number of interpretations. This is another distinct advantage of these tools. You could collect all of the SSURGO interpretations for every soil survey area (areasymbol) by executing the tool 1 time. This also demonstrates the flexibility SOD has in defining the geographic extent over which information is collected. The only constraint is the extent of soil survey areas selected to run (and these can be discontinuous).As SOD Areasymbol tools execute, 2 lists are collected from the tool dialog, a list of interpretations/properties and a list of areasymbols. As each interpretation/property is run, every areasymbol is run against the interpretation/property requested. For instance, suppose you wanted to collect the weighted average of sand, silt and clay for 5 soil survey areas. The sand property would run for all 5 soil survey areas and built into a table. Next the silt would run for all 5 soil survey areas and built into a table, and so on. In this example a total of 15 web request would have been sent and 3 tables are built. Two VERY IMPORTANT things here...A. All the areasymbol tools do is generate tables. They are not collecting spatial data.B. They are collecting stored information. They are not making calculations(with the exception of the weighted average aggregation method).<2. ExpressThe Express toolset is nearly identical to the Areasymbol toolset, with 2 exceptions.A. The area to collect SSURGO information over is defined by the user. The user digitizes coordinates into a 'feature set' after the tool is open. The points in the feature set are closed (first point is also the last) into a polygon. The polygon is sent to Soil Data Access and the features set points (polygon) are used to clip SSURGO spatial data. The geomotries of the clip operation are returned, along with the mapunit keys (unique identifier). It is best to keep the points in the feature set simple and beware of self intersections as they are fatal.B. Instead of running on a list of areasymbols, the SQL queries on a list of mapunit keys.The properties and interpretations options are identical to what was discussed for the Areasymbol toolset.The Express tools present the user the option of creating layer files (.lyr) where the the resultant interpretation/property are joined to the geometry and saved to disk as a virtual join. Additionally, for soil properties, an option exists to append all of the selected soil properties to a single table. In this case, if the user ran sand, silt, and clay properties, instead of 3 output tables, there is only 1 table with a sand column, a silt column, and a clay column.<Supplemental Information<sAggregation MethodAggregation is the process by which a set of component attribute values is reduced to a single value to represent the map unit as a whole.A map unit is typically composed of one or more "components". A component is either some type of soil or some nonsoil entity, e.g., rock outcrop. The components in the map unit name represent the major soils within a map unit delineation. Minor components make up the balance of the map unit. Great differences in soil properties can occur between map unit components and within short distances. Minor components may be very different from the major components. Such differences could significantly affect use and management of the map unit. Minor components may or may not be documented in the database. The results of aggregation do not reflect the presence or absence of limitations of the components which are not listed in the database. An on-site investigation is required to identify the location of individual map unit components. For queries of soil properties, only major components are considered for Dominant Component (numeric) and Weighted Average aggregation methods (see below). Additionally, the aggregation method selected drives the available properties to be queried. For queries of soil interpretations, all components are condisered.For each of a map unit's components, a corresponding percent composition is recorded. A percent composition of 60 indicates that the corresponding component typically makes up approximately 60% of the map unit. Percent composition is a critical factor in some, but not all, aggregation methods.For the attribute being aggregated, the first step of the aggregation process is to derive one attribute value for each of a map unit's components. From this set of component attributes, the next step of the aggregation process derives a single value that represents the map unit as a whole. Once a single value for each map unit is derived, a thematic map for soil map units can be generated. Aggregation must be done because, on any soil map, map units are delineated but components are not.The aggregation method "Dominant Component" returns the attribute value associated with the component with the highest percent composition in the map unit. If more than one component shares the highest percent composition, the value of the first named component is returned.The aggregation method "Dominant Condition" first groups like attribute values for the components in a map unit. For each group, percent composition is set to the sum of the percent composition of all components participating in that group. These groups now represent "conditions" rather than components. The attribute value associated with the group with the highest cumulative percent composition is returned. If more than one group shares the highest cumulative percent composition, the value of the group having the first named component of the mapunit is returned.The aggregation method "Weighted Average" computes a weighted average value for all components in the map unit. Percent composition is the weighting factor. The result returned by this aggregation method represents a weighted average value of the corresponding attribute throughout the map unit.The aggregation method "Minimum or Maximum" returns either the lowest or highest attribute value among all components of the map unit, depending on the corresponding "tie-break" rule. In this case, the "tie-break" rule indicates whether the lowest or highest value among all components should be returned. For this aggregation method, percent composition ties cannot occur. The result may correspond to a map unit component of very minor extent. This aggregation method is appropriate for either numeric attributes or attributes with a ranked or logically ordered domain.

1. Spatial capture-recapture (SCR) models are commonly used for analyzing data collected using non-invasive genetic sampling (NGS). Opportunistic NGS often leads to detections that do not occur at discrete detector locations. Therefore, spatial aggregation of individual detections into fixed detectors (e.g. center of grid cells) is an option to increase computing speed of SCR analyses. However, it may reduce precision and accuracy of parameter estimations.
2. Using simulations, we explored the impact that spatial aggregation of detections has on a trade-off between computing time and parameter precision and bias, under a range of biological conditions. We used three different observation models: the commonly used Poisson and Bernoulli models, as well as a novel way to partially aggregate detections (Partially Aggregated Binary model (PAB)) to reduce the loss of information after aggregating binary detections. The PAB model divides detectors into K subdetectors and models the frequency of subdetectors with more than one detection as a binomial response with a sample size of K. Finally, we demonstrate the consequences of aggregation and the use of the PAB model using NGS data from the monitoring of wolverine (Gulo gulo) in Norway.
3. Spatial aggregation of detections, while reducing computation time, does indeed incur costs in terms of reduced precision and accuracy, especially for the parameters of the detection function. SCR models estimated abundance with a low bias (< 10%) even at high degree of aggregation, but only for the Poisson and PAB models. Overall, the cost of aggregation is mitigated when using the Poisson and PAB models. At the same level of aggregation, the PAB observation models out-performs the Bernoulli model in terms of accuracy of estimates, while offering the benefits of a binary observation model (less assumptions about the underlying ecological process) over the count-based model.
4. We recommend that detector spacing after aggregation does not exceed 1.5 times the scale-parameter of the detection function in order to limit bias. We recommend the use of the PAB observation model when performing spatial aggregation of binary data as it can mitigate the cost of aggregation, compared to the Bernoulli model.

Spatial data set of the plan FNP Worpswede (Collection) It is a utility service of aggregation of plan elements with one layer per XPlanung class. That of the last change is the 10.01.2019. The scopes of the change plans are summarized in the Scopes layer.

In spite of the potentially groundbreaking environmental sentinel applications, studies of canine cancer data sources are often limited due to undercounting of cancer cases. This source of uncertainty might be further amplified through the process of spatial data aggregation, manifested as part of the modifiable areal unit problem (MAUP). In this study, we explore potential explanatory factors for canine cancer incidence retrieved from the Swiss Canine Cancer Registry (SCCR) in a regression modeling framework. In doing so, we also evaluate differences in statistical performance and associations resulting from a dasymetric refinement of municipal units to their portion of residential land. Our findings document severe underascertainment of cancer cases in the SCCR, which we linked to specific demographic characteristics and reduced use of veterinary care. These explanatory factors result in improved statistical performance when computed using dasymetrically refined units. This suggests that dasymetric mapping should be further tested in geographic correlation studies of canine cancer incidence and in future comparative studies involving human cancers.

Developed by SOLARGIS and provided by the Global Solar Atlas (GSA), this data resource contains diffuse horizontal irradiation (DIF) in kWh/m² covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km). The data is hyperlinked under 'resources' with the following characeristics: DIF LTAy_AvgDailyTotals (GeoTIFF) Data format: GEOTIFF File size : 198.94 MB There are two temporal representation of solar resource and PVOUT data available: • Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals) • Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals) Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations: • LTAy_YearlyTotals = LTAy_DailyTotals * 365.25 • LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).

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.

The spatial scan statistic method has been widely used for detecting disease clusters. Its results may be affected by scales, including the aggregation level of the input data and the population threshold used in the detection. Previous studies offered inconsistent findings, and few had considered both types of scales at the same time. Using 24 simulated datasets and two real disease datasets, we investigated the method’s sensitivity to the two types of scales. We aggregated the individual-level data into areal units of three levels, including county, town, and a 900 m grid. We detected clusters with three population thresholds, including 10%, 25%, and 50%. We used two measurements, distance between cluster centres and the Jaccard index, to quantify the consistency of clusters detected with different scale settings. We find: (1) the method is not greatly sensitive to the data aggregation level when the cluster is strong and in a place with high population density; (2) the method’s sensitivity to the population threshold is determined by the actual size of the true cluster; and (3) a regular grid with fine resolution is advantageous over the subjectively defined areal units. The process and findings may have broader meanings to similar spatial analyses.

This table contains aggregate counts by locations for different demographics served by the Streets to Stability Rescue Plan project. Columns include Reporting Period Start/End Dates, Location Name, Exit Destination Category, Exit Destination, Race, Ethnicity, Gender, Age Range, Disability and Participant Count.-- Additional Information: Category: ARPA Update Frequency: As Necessary-- Metadata Link: https://www.portlandmaps.com/metadata/index.cfm?&action=DisplayLayer&LayerID=61074

Developed by SOLARGIS (https://solargis.com) and provided by the Global Solar Atlas (GSA), this data resource contains direct normal irradiation (DNI) in kWh/m² covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km). The data is hyperlinked under 'resources' with the following characteristics: DNI - LTAy_AvgDailyTotals (GeoTIFF) Data format: GEOTIFF File size : 343.99 MB There are two temporal representation of solar resource and PVOUT data available: • Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals) • Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals) Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations: • LTAy_YearlyTotals = LTAy_DailyTotals * 365.25 • LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).