Spatial data set of the plan FNP_Bothel (aggregation ) This is a utility service of aggregation of plan elements with one layer per XPlanung class. That of the last amendment is 26.08.2021. The scopes of the change plans are summarized in the Scopes layer.

This geospatial mobility dataset is used by our customers for many purposes, such as to understand mobility patterns in specific geographic areas or countries, to build their own mobility data models, understand visitation into their own or competitors premises, or test hypotheses around changes in visitation patterns over time.

The Intuizi Visitation Dataset comprises fully-consented mobile device data, de-identified at source by the entity which has legal consent to own/process such data, and on who’s behalf we work to create an de-identified dataset of Encrypted ID visitation/mobility data.

Spatial data set of the plan FNP Bardowick (Collection) It is a utility service of aggregation of plan elements with one layer per XPlanung class. That of the last change is the 28.02.2019. The scopes of the change plans are summarized in the Scopes layer.

Geospatial Data Clean-Room Market Outlook

According to our latest research, the global geospatial data clean-room market size in 2024 stands at USD 1.4 billion, driven by the surging need for secure and collaborative geospatial data environments across multiple industries. The market is projected to expand at a robust CAGR of 18.2% from 2025 to 2033, reaching a forecasted market size of USD 6.3 billion by 2033. This remarkable growth is fueled by increasing concerns over data privacy, the proliferation of location-based services, and the mounting regulatory requirements for secure data collaboration and analytics.

One of the primary growth factors for the geospatial data clean-room market is the exponential increase in the volume and variety of geospatial data generated by IoT devices, drones, satellites, and mobile applications. Organizations across sectors such as transportation, urban planning, and logistics are leveraging this data to derive actionable insights. However, the sensitive nature of location data and the need to comply with global privacy regulations such as GDPR and CCPA necessitate secure environments for data aggregation and analysis. Geospatial data clean-rooms provide a controlled and compliant infrastructure for multiple parties to collaborate on sensitive datasets without exposing raw data, thus unlocking value while minimizing risk.

Another significant driver is the digital transformation initiatives undertaken by governments and enterprises worldwide. As smart city projects and digital twin technologies gain traction, the demand for secure, scalable, and interoperable platforms to process and analyze geospatial data is surging. Clean-room solutions offer advanced capabilities such as federated analytics, privacy-preserving computation, and policy-driven data governance. These features are particularly crucial for sectors like healthcare, BFSI, and defense, where the confidentiality of location data is paramount. Additionally, the integration of artificial intelligence and machine learning algorithms within clean-room platforms is enhancing the accuracy and utility of geospatial analytics, further accelerating market adoption.

The geospatial data clean-room market is also benefiting from the evolving landscape of data monetization and data sharing partnerships. Companies are increasingly seeking ways to collaborate with external partners, suppliers, or governmental organizations to unlock new revenue streams and improve operational efficiency. Clean-rooms act as a trusted intermediary, enabling secure, permissioned access to geospatial datasets while preserving data sovereignty and intellectual property rights. This collaborative approach is fostering innovation across industries such as retail, energy, and utilities, where location intelligence can drive targeted marketing, resource optimization, and risk management.

From a regional perspective, North America currently dominates the geospatial data clean-room market, accounting for the largest revenue share, followed by Europe and the Asia Pacific. The presence of leading technology providers, stringent regulatory frameworks, and early adoption of advanced analytics solutions are key factors contributing to North America's leadership. Meanwhile, the Asia Pacific region is expected to witness the fastest growth over the forecast period, propelled by rapid urbanization, government investments in smart infrastructure, and the burgeoning digital economy. Europe remains a critical market due to its strong emphasis on data privacy and cross-border data collaboration initiatives.

Component Analysis

The component segment of the geospatial data clean-room market is categorized into software, services, and hardware. Software solutions form the backbone of clean-room platforms, offering functionalities such as data ingestion, anonymization, access control, and analytics. The software segment holds the largest market share, primarily due t

Our Geospatial Dataset connects people's movements to over 200M physical locations globally. These are aggregated and anonymized data that are only used to offer context for the volume and patterns of visits to certain locations. This data feed is compiled from different data sources around the world.

It includes information such as the name, address, coordinates, and category of these locations, which can range from restaurants and hotels to parks and tourist attractions

Location Intelligence Data Reach: Location Intelligence data brings the POI/Place/OOH level insights calculated on the basis of Factori’s Mobility & People Graph data aggregated from multiple data sources globally. In order to achieve the desired foot-traffic attribution, specific attributes are combined to bring forward the desired reach data. For instance, in order to calculate the foot traffic for a specific location, a combination of location ID, day of the week, and part of the day can be combined to give specific location intelligence data. There can be a maximum of 56 data records possible for one POI based on the combination of these attributes.

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method at a suitable interval (daily/weekly/monthly).

Use Cases: Credit Scoring: Financial services can use alternative data to score an underbanked or unbanked customer by validating locations and persona. Retail Analytics: Analyze footfall trends in various locations and gain an understanding of customer personas. Market Intelligence: Study various market areas, the proximity of points or interests, and the competitive landscape Urban Planning: Build cases for urban development, public infrastructure needs, and transit planning based on fresh population data. Marketing Campaign Strategy: Analyzing visitor demographics and behavior patterns around POIs, businesses can tailor their marketing strategies to effectively reach their target audience. OOH/DOOH Campaign Planning: Identify high-traffic locations and understand consumer behavior in specific areas, to execute targeted advertising strategies effectively. Geofencing: Geofencing involves creating virtual boundaries around physical locations, enabling businesses to trigger actions when users enter or exit these areas

Data Attributes Included: LocationID
name
website BrandID Phone streetAddress
city
state country_code zip lat lng poi_status
geoHash8 poi_id category category_id full_address address additional_categories url domain rating price_level rating_distribution is_claimed photo_url attributes brand_name brand_id status total_photos popular_times places_topics people_also_search work_hours local_business_links contact_info reviews_count naics_code naics_code_description sis_code sic_code_description shape_polygon building_id building_type building_name geometry_location_type geometry_viewport_northeast_lat geometry_viewport_northeast_lng geometry_viewport_southwest_lat geometry_viewport_southwest_lng geometry_location_lat geometry_location_lng calculated_geo_hash_8

The Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) provides a new level of detail in global topographic data. Previously, the best available global DEM was GTOPO30 with a horizontal grid spacing of 30 arc-seconds. The GMTED2010 product suite contains seven new raster elevation products for each of the 30-, 15-, and 7.5-arc-second spatial resolutions and incorporates the current best available global elevation data. The new elevation products have been produced using the following aggregation methods: minimum elevation, maximum elevation, mean elevation, median elevation, standard deviation of elevation, systematic subsample, and breakline emphasis. Metadata have also been produced to identify the source and attributes of all the input elevation data used to derive the output products. Many of these products will be suitable for various regional continental-scale land cover mapping, extraction of drainage features for hydrologic modeling, and geometric and radiometric correction of medium and coarse resolution satellite image data. The global aggregated vertical accuracy of GMTED2010 can be summarized in terms of the resolution and RMSE of the products with respect to a global set of control points (estimated global accuracy of 6 m RMSE) provided by the National Geospatial-Intelligence Agency (NGA). At 30 arc-seconds, the GMTED2010 RMSE range is between 25 and 42 meters; at 15 arc-seconds, the RMSE range is between 29 and 32 meters; and at 7.5 arc-seconds, the RMSE range is between 26 and 30 meters. GMTED2010 is a major improvement in consistency and vertical accuracy over GTOPO30, which has a 66 m RMSE globally compared to the same NGA control points. In areas where new sources of higher resolution data were available, the GMTED2010 products are substantially better than the aggregated global statistics; however, large areas still exist, particularly above 60 degrees North latitude, that lack good elevation data. As new data become available, especially in areas that have poor coverage in the current model, it is hoped that new versions of GMTED2010 might be generated and thus gradually improve the global model.

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) &...

BackgroundSpatial data are often aggregated by area to protect the confidentiality of individuals and aid the calculation of pertinent risks and rates. However, the analysis of spatially aggregated data is susceptible to the modifiable areal unit problem (MAUP), which arises when inference varies with boundary or aggregation changes. While the impact of the MAUP has been examined previously, typically these studies have focused on well-populated areas. Understanding how the MAUP behaves when data are sparse is particularly important for countries with less populated areas, such as Australia. This study aims to assess different geographical regions’ vulnerability to the MAUP when data are relatively sparse to inform researchers’ choice of aggregation level for fitting spatial models.MethodsTo understand the impact of the MAUP in Queensland, Australia, the present study investigates inference from simulated lung cancer incidence data using the five levels of spatial aggregation defined by the Australian Statistical Geography Standard. To this end, Bayesian spatial BYM models with and without covariates were fitted.Results and conclusionThe MAUP impacted inference in the analysis of cancer counts for data aggregated to coarsest areal structures. However, area structures with moderate resolution were not greatly impacted by the MAUP, and offer advantages in terms of data sparsity, computational intensity and availability of data sets.

Spatial data set of the plan FNP_Dinklage (Collection) This is a utility service for aggregating plan elements with one layer per XPlanung class. That of the last amendment is 05.12.2020. The scopes of the change plans are summarized in the Scopes layer.

Geospatial data about Nevada Aggregate Mines. Export to CAD, GIS, PDF, CSV and access via API.

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.

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.

The spatial point data is aggregated by the State of Colorado Governor's Office of Information Technology Geospatial Information Systems Team. This dataset represents an on-going relationship between the counties, municipalities and the State of Colorado to provide accurate updated address information. Updates are collected annually. This data layer reflects an evolving data model of the Colorado State Address Dataset (CSAD).

The Aggregate Resource Mapping Program (ARMP) began in 1984 when the Minnesota Legislature passed a law (Minnnesota Statutes, section 84.94) to:
- Identify and classify aggregate resources outside of the Twin Cities metropolitan area;
- Give aggregate resource information to local units of government and others for making comprehensive land-use and zoning plans;
- Introduce aggregate resource protection; and Promote orderly and environmentally sound development of the resource.

Provided here is a compilation of GIS data produced by the DNR's Aggregate Resource Mapping Program. Also provided is the aggregate resource GIS data from the 7-County Metropolitan Area mapped by the Minnesota Geological Survey (MGS). Please see the layer-specific metadata for each of the 9 layers for more details:

ARMP:
Compilation of Gravel Pits, Quarries, and Prospects
Compilation of Crushed Stone Resource Potential
Compilation of Geologic Field Observations
Compilation of Sand and Gravel Resource Potential
Compilation of DNR Test Holes
Status Map

7-County Metro Area:
Compilation of Pits and Quarries
Bedrock Aggregate Sources
Sand and Gravel Sources

Health data and environmental data are commonly collected at different levels of aggregation. A persistent challenge of using a spatial regression model to link these data is that their associations can vary as a function of aggregation. This results into ecological fallacy if association at one aggregation level is used for inferencing at another level. We address this challenge by presenting a hierarchically adaptable spatial regression model. In essence, the model extends the spatially varying coefficient model to allow the response to be count data at larger aggregation levels than that of the covariates. A Bayesian hierarchical approach is used for inferencing the model parameters. Robust inference and optimal prediction over geographical space and at different spatial aggregation levels are studied by simulated data sets. The spatial associations at different spatial supports are largely different, but can be efficiently inferred when prior knowledge of the associations is available. The model is applied to study hand, foot and mouth disease (HFMD) in Da Nang city, Viet Nam. Decrease in vegetated areas corresponds with elevated HFMD risks. A study to the identifiability of the parameters shows a strong need for a highly informative prior distribution. We conclude that the model is robust to the underlying aggregation levels of the calibrating data for association inference and it is ready for application in health geography.

Health data and environmental data are commonly collected at different levels of aggregation. A persistent challenge of using a spatial regression model to link these data is that their associations can vary as a function of aggregation. This results into ecological fallacy if association at one aggregation level is used for inferencing at another level. We address this challenge by presenting a hierarchically adaptable spatial regression model. In essence, the model extends the spatially varying coefficient model to allow the response to be count data at larger aggregation levels than that of the covariates. A Bayesian hierarchical approach is used for inferencing the model parameters. Robust inference and optimal prediction over geographical space and at different spatial aggregation levels are studied by simulated data sets. The spatial associations at different spatial supports are largely different, but can be efficiently inferred when prior knowledge of the associations is available. The model is applied to study hand, foot and mouth disease (HFMD) in Da Nang city, Viet Nam. Decrease in vegetated areas corresponds with elevated HFMD risks. A study to the identifiability of the parameters shows a strong need for a highly informative prior distribution. We conclude that the model is robust to the underlying aggregation levels of the calibrating data for association inference and it is ready for application in health geography.

The data used in the forthcoming “The modifiable areal unit problem in geospatial least-cost electrification modelling” publication.

The work describes how different methods of aggregation of population data effects the results produced by the Open Source Spatial Electrification Tool (OnSSET, https://github.com/OnSSET). In the initial study three countries have been assessed: Benin, Malawi and Namibia. The choice of countries is due to their different national population densities and starting electrification rates. The following repository includes three zipped files, one for each country, containing the 26 input files used in the study. These input files are generated with the QGIS tools published in the OnSSET repository (https://github.com/onsset). This data repository also contains a file describing the naming conventions for the results used and the summary files generated with OnSSET.

For more information on how to generate these datasets, please refer to the following GitHub repository https://github.com/babakkhavari/MAUP and the corresponding publication (To Be Added)

Spatial data set of the plan FNP_Löningen (Collection) This is a utility service for aggregating plan elements with one layer per XPlanung class. That of the last change is the 25.09.2020. The scopes of the change plans are summarized in the Scopes layer.

Air pollution exposure disparities by race/ethnicity and socioeconomic status have been analyzed using data aggregated at various spatial scales. Our research question is this: To what extent does the spatial scale of data aggregation impact the estimated exposure disparities? We compared disparities calculated using data spatially aggregated at five administrative scales (state, county, census tract, census block group, census block) in the contiguous United States in 2010. Specifically, for each of the five spatial scales, we calculated national and intraurban disparities in exposure to fine particles (PM2.5) and nitrogen dioxide (NO2) by race/ethnicity and socioeconomic characteristics using census demographic data and an empirical statistical air pollution model aggregated at that scale. We found, for both pollutants, that national disparity estimates based on state and county scale data often substantially underestimated those estimated using tract and finer scales; in contrast, national disparity estimates were generally consistent using tract, block group, and block scale data. Similarly, intraurban disparity estimates based on tract and finer scale data were generally well correlated for both pollutants across urban areas, although in some cases intraurban disparity estimates were substantially different, with tract scale data more frequently leading to underestimates of disparities compared to finer scale analyses.

This dataset is an aggregation of county-level tillage practices to the 8-digit hydrologic unit watershed. The original county-level data were collected by the Conservation Technology Information Center (CTIC) and is a proprietary dataset. The CTIC collects tillage data by conducting surveys about tillage systems for all counties in the United States. Watershed aggregations were done by overlying the 8-digit HUC polygons with a raster of county boundaries and a raster of the 2001 National Land Cover Data for land use 82 (cultivated land) to derive a county/land-use area weighting factor. The weighting factor was then applied to the county-level tillage data for the counties within each 8-digit HUC and summed to yield the total acreage of each tillage type within each 8-digit HUC watershed. Tillage systems include three types of conservation tillage (no-till, ridge-till, and mulch-till), reduced tillage, and intensive tillage. Total planted acreage for each tillage practice for each crop grown is reported to the CTIC. The dataset includes total planted acreage by tillage type for selected crops (corn, cotton, grain sorghum, soybeans, fallow, forage, newly established permanent pasture, spring and fall seeded small grains, and "other" crops) for 1989-2004. The CTIC did not collect data nationwide for 1999, 2001, and 2003. In addition, data were not collected for all counties every year. Missing data are coded with -9999. The companion WBDHUC8 geospatial dataset is available online: https://water.usgs.gov/lookup/getspatial?wbdhuc8.xml .