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

Feature layer generated from running the Aggregate Points solutions. Points from Tax Account Points filtered by Residential / commercial properties were aggregated to Bins 0.3 and 0.8 miles with Stats for CityTaxValue, TotalTaxValue, LandValue and ImpVal

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.

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.

Aim: Addressing how woody plant species are distributed in space can reveal inconspicuous drivers that structure plant communities. The spatial structure of conspecifics varies not only at local scales across co-existing plant species but also at larger biogeographical scales with climatic parameters and habitat properties. The possibility that biogeographical drivers shape the spatial structure of plants, however, has not received sufficient attention. Location: Global synthesis. Time period: 1997 - 2022. Major taxa studied: Woody angiosperms and conifers. Methods: We carried out a quantitative synthesis to capture the interplay between local scale and larger scale drivers. We modelled conspecific spatial aggregation as a binary response through logistic models and Ripley’s L statistics and the distance at which the point process was least random with mixed effects linear models. Our predictors covered a range of plant traits, climatic predictors and descriptors of the habitat. Results: We hypothesized that plant traits, when summarized by local scale predictors, exceed in importance biogeographical drivers in determining the spatial structure of conspecifics across woody systems. This was only the case in relation to the frequency with which we observe aggregated distributions. The probability of observing spatial aggregation and the intensity of it was higher for plant species with large leaves but further depended on climatic parameters and mycorrhiza. Main Conclusions: Compared to climatic variables, plant traits perform poorly in explaining the spatial structure of woody plant species, even though leaf area is a decisive plant trait that is related to whether we observe homogenous spatial aggregation and its intensity. Despite the limited variance explained by our models, we found that the spatial structure of woody plants is subject to consistent biogeographical constraints and that these exceed beyond descriptors of individual species, which we captured here through leaf area. Methods 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) The authors named the plant species for which the univariate Ripley's K statistics had been described. (5) The landscape (for example a logging area) did not induce conspicuous point processes that could not be corrected within the analysis. We manually processed the remaining 240 studies through reading the main text which reduced the final number of eligible studies to 69. A list of those data sources can be found in Appendix Three. From those studies we extracted the following moderators and we fitted them as predictors in subsequent models: Mean annual temperature: continuous variable. When unreported, we extracted the variable based on coordinates from WorldClim (Fick & Hijmans, 2017). Total annual precipitation: continuous variable. When unreported, we extracted the variable based on coordinates from WorldClim (Fick & Hijmans, 2017). Latitude of the study location: continuous variable. When unreported, we extracted the information based on the closest location reported. Longitude of the study location: continuous variable. When unreported, we extracted the information based on the closest location reported. Site area: continuous variable. We extracted the site area from the studies and converted it into a unified unit, square meter. Tree species: categorical variable. Plant traits: we collected data on 7 traits: leaf area (i.e. the size of the leaves), seed mass, wood density, leaf mass per area, tree height, plant species biomass and stem specific density. We first gathered data on tree height, seed mass and leaf area from the subset of common species in TRY (Díaz et al., 2022). We subsequently searched for seed mass data the SID database (Royal Botanic Gardens Kew, 2023) and the ICRAF database for wood density data (Ketterings et al., 2001). In the cases we observed no records in those databases we checked the EOL database (http://eol.org.). For leaf area, leaf mass per area, tree height, plant species biomass and stem specific density, we extracted them from the EOL database (http://eol.org.). We opted with these traits to cover as many trait syndromes as possible but the main criterion which we used to decide on the traits was the feasibility of acquiring them for the plant species in our database. Woody system age: categorical variable. We classified non woody habitats, plantations and systems that had recently experienced serious disturbances as “young” whereas natural forests or woody stands that had reached maturity as “old”. Mycorrhiza type: categorical variable. We extracted mycorrhizal types for each species from Wang and Qiu (2006). In the cases that we could find no mycorrhizal classification information in the database at a species level we searched instead the database compiled by Delavaux et al. (2021) containing information at a genus level. We only extracted mycorrhizal classifications if these supported a single mycorrhizal type at a minimum probability of 85%. Otherwise, we left the plant species unclassified in relation to mycorrhiza. Ripley's L effect size: continuous variable. We first calculated for all distances the ratio between the (1) difference between the Ripley's L statistic and the width of the 95% CI envelope divided by two and (2) the difference between the upper and lower points of the envelope divided by two. A large absolute value suggests a strong deviation from randomness whereas any value below 1 suggest a random process. We identified the location where the absolute value of this ratio was maximum. Ripley's L statistic: continuous variable. We transformed Ripley´s K statistics (when they had not been transformed) into Ripley´s L statistics. We only used the value at the location where we observed the maximum in absolute value Ripley's K effect size. Distance when Ripley's L peaked: continuous variable describing the distance at which we observed the maximum in absolute value Ripley´s L effect size. Köppen climate zone: a categorical variable with 4 levels describing the main climatic zones based on the Köppen classification: A (tropical climates); B (arid climates); C (temperate climates); D (continental climates). We extracted those from the raster files published by Beck et al. (2018). In the cases that we observed multiple values in databases (referring here mainly to plant trait values) per species, we used the median value. In the cases when we had to digitize plots to extract data, we did so with Plot Digitizer v2.6.8.

Aggregate data files digitized from the published census volumes for 1851. 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.

This dataset represents the locations of licenced and permitted pits and quarries regulated by the Ministry of Natural Resources and Forestry (MNRF) under the Aggregate Resources Act, R.S.O. 1990.

Aggregate site data has been divided into active and inactive sites. Active sites may be further subdivided into partial surrenders. In partial surrenders, defined areas of a site are inactive while the rest of the site remains active.

The data includes:

site location and size licensee name approval type (licence or permit) operation type (pit or quarry) maximum annual tonnage limit the MNRF district responsible for the site

Use our interactive Pits and Quarries map to find active sites.

This data does not include aggregate sites regulated by the Ministry of Transportation.

Additional Documentation

Aggregate Site Authorized - User Guide (PDF)

Aggregate Site Authorized - Active - Data Description (PDF)

Aggregate Site Authorized - Active - Documentation (Word)

Status

On going: Data is continually being updated

Maintenance and Update Frequency

Continual: Data is repeatedly and frequently updated

Contact

Ryan Lenethen, Policy Analyst, ryan.lenethen@Ontario.ca

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.

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.

Developed by SOLARGIS (https://solargis.com) 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).

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 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.

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)

This spatial dataset represents the locations of abandoned aggregate sites (pits and quarries) that have not been rehabilitated for various reasons, including:

the site predates legislation that requires rehabilitation the site was revoked, and no rehabilitation has been completed

Information about active aggregate sites is available in related data classes and online using the interactive Pits and Quarries map.

Additional Documentation

  Aggregate Site Unrehabilitated - Data Description (PDF)
  Aggregate Site Unrehabilitated - Documentation (Word)

Status

On going: data is being continually updated

Maintenance and Update Frequency

As needed: data is updated as deemed necessary

Contact

Ryan Lenethen, Integration Branch, ryan.lenethen@ontario.ca

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.

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.

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 Eschede (Collection) This is a utility service of collaborating plan elements with one layer per XPlanung class. That of the last amendment is 07.10.2020. The scopes of the change plans are summarized in the Scopes layer.

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.

Negative values mean that spatial aggregation estimates for peak measures were smaller than spatial aggregation differences for onset measures. Bolded values denote mean estimates that we interpret to have statistical significance; that is, the 95% credible intervals did not overlap with zero.