The morphological spatial pattern analysis derived from the Forest/Non-Forest Map 2000 (FMAP2000) using the MSPA algorithm at a spatial resolution of 25-m. Further details available in: Soille P, Vogt P, 2008. Morphological segmentation of binary patterns. Pattern Recognition Letters 30, 4:456-459, doi: 10.1016/j.patrec.2008.10.015

Datasets- simple_land_cover1.tif - an example land cover dataset presented in Figures 1 and 2- simple_landform1.tif - an example landform dataset presented in Figures 1 and 2- landcover_europe.tif - a land cover dataset with nine categories for Europe - landcover_europe.qml - a QGIS color style for the landcover_europe.tif dataset- landform_europe.tif - a landform dataset with 17 categories for Europe - landform_europe.qml - a QGIS color style for the landform_europe.tif dataset- map1.gpkg - a map of LTs in Europe constructed using the INCOMA-based method- map1.qml - a QGIS color style for the map1.gpkg dataset- map2.gpkg - a map of LTs in Europe constructed using the COMA method to identify and delineate pattern types in each theme separately- map2.qml - a QGIS color style for the map2.gpkg dataset- map3.gpkg - a map of LTs in Europe constructed using the map overlay method- map3.qml - a QGIS color style for the map3.gpkg dataset

Supplementary datasets used for calculating spatial pattern indicators as presented in research discussed in a paper provisionally entitled “Settlement and infrastructure patterns influence energy use and CO2 emissions almost as much as economic activity”. This repository contains spatially explicit data on (1) built-up patches and urban agglomerations (BL), (2) main infrastructure features (road and railway, R and RW) and (3) a reference inhabited land area (IH).

Ephedra sinica Stapf. is a shrubby plant widely used in traditional Chinese medicine due to its high level of medicinal value, thus, it is in high demand. Ephedrine (E) and pseudoephedrine (PE) are key medicinal components and quality indicators for E. sinica. These two ephedrine-type alkaloids are basic elements that exert the medicinal effect of E. sinica. Recently, indiscriminate destruction and grassland desertification have caused the quantity and quality of these pharmacological plants to degenerate. Predicting potentially suitable habitat for high-quality E. sinica is essential for its future conservation and domestication. In this study, MaxEnt software was utilized to map suitable habitats for E. sinica in Inner Mongolia based on occurrence data and a set of variables related to climate, soil, topography and human impact. The model parametrization was optimized by evaluating alternative combinations of feature classes and values of the regularization multiplier. Second, a geospatial quality model was fitted to relate E and PE contents to the same environmental variables and to predict their spatial patterns across the study area. Outputs from the two models were finally coupled to map areas predicted to have both suitable conditions for E. sinica and high alkaloid content. Our results indicate that E. sinica with high-quality E content was mainly distributed in the Horqin, Ulan Butong and Wulanchabu grasslands. E. sinica with high-quality PE content was primarily found in the Ordos, Wulanchabu and Ulan Butong grasslands. This study provides scientific information for the protection and sustainable utilization of E. sinica. It can also help to control and prevent desertification in Inner Mongolia.

Distribution map (raster format: geotiff) of Larix decidua, computed using the NFIs - EFDAC EForest European dataset of species presence/absence. The distribution is estimated by means of statistical interpolation (constrained spatial multi-frequency analysis, C-SMFA) Available years: 2000. The maps are available in the European Forest Data Center (EFDAC). The specific goal of EFDAC is to become a focal point for policy relevant forest data and information by hosting and pointing to relevant forest information as well as providing web-based tools for accessing information located in EFDAC.

Distribution map (raster format: geotiff) of Fagus sylvatica, computed using the NFIs - EFDAC EForest European dataset of species presence/absence. The distribution is estimated by means of statistical interpolation (constrained spatial multi-frequency analysis, C-SMFA) Available years: 2000. The maps are available in the European Forest Data Center (EFDAC). The specific goal of EFDAC is to become a focal point for policy relevant forest data and information by hosting and pointing to relevant forest information as well as providing web-based tools for accessing information located in EFDAC.

The data from the Digital Mountain Map of China depicts the spatial pattern and complex morphological characteristics of mountains in China from a macro scale, including the mountains’ spatial distribution, classification, morphological elements and area ratio. It is a set of basic data that can be used for mountain zoning, mountain genetic classification and resource environment correlation analysis. Mountains carry great natural resource supply, provide ecological service and regulation functions, and play an important part in eco-civilization construction and socioeconomic development in China. Lately，Prof. Li Ainong of the Institute of Mountain Hazards and Environment, CAS, developed this data set based on the spatial definition of mountains, an a topography adaptive slide window method for the relief amplitude. The data include: (1) Spatial distribution of mountains in China; (2) Mountain classification; (3) Main mountain ranges (with range alignment, relief grade and ridge morphology); (4)Main mountain peaks; (5)Mountain proportion table of the provinces/autonomous regions/municipalities of China; (6) Contour zoning data; (7) General situation of mountain formation; (8)Mountain division and zoning data; (9) List of main mountain peaks. The spatial resolution of the original DEM source is about 90m. And the boundaries of mountains have been revised with multisource remote sensing data, which has good spatial consistency with the relief shading map. The cartographic generalization accuracy of mountain ranges and relevant features is 1:1 000 000. Mountain features in this data set have higher spatial resolution and pertinence, which are available for the zonality of mountain environment and mountain hazards, and the spatial analysis for ecological, production and living spaces in mountain areas, surpporting macro decision-making on mountain areas' development in China. p

The output from OLS is a residual map. The model predicted average interest rates from average loan grades. The residual maps shows where the model did well, where it over-predicted, and where it under-predicted. The purple areas are locations where actual average interest rates were lower than the model predicted, while the green areas are locations where the actual interest rates were higher.The spatial pattern of residuals is not random. In particular, the entire sate of Mississippi has a large cluster of ZiP3 areas where the model predicted higher interest rates than were observed.There is a Learn Lesson that lets you to do this analysis yourself.

Forests provide numerous ecosystem services, such as timber yields, biodiversity protection and climate change mitigation. The type of management has an effect on the provision of these services. Often the demands for these services can lead to conflict – wood harvest can negatively impact biodiversity and climate change mitigation capacity. Although forest management differences are important, spatially explicit data is lacking, in particular on a global scale. We present here a first systematic approach which integrates existing data to map forest management globally through downscaling national and subnational forest data. In our forest management classification, we distinguished between two levels of forest management, with three categories each. Level 1 comprised primary, naturally regrown and planted forests. Level 2 distinguished between different forest uses. We gathered documented locations, where these forest categories were observed, from the literature and a database on ecological diversity. We then performed multinomial logit regression and estimated the effect of 21 socio-economic and bio-physical predictor variables on the occurrence of a forest category. Model results on significance and effect direction of predictor variables were in line with findings of previous studies. Soil and environmental properties, forest conditions and accessibility are important determinants of the occurrence of forest management types. Based on the model results, likelihood maps were calculated and used to spatially allocate national extents of level 1 and level 2 forest categories. When compared to previous studies, our maps showed higher agreement than random samples. Deviations between observed and predicted plantation locations were mostly below 10 km. Our map provides an estimation of global forest management patterns, enhancing previous methodologies and making the best use of data available. Next to having multiple applications, for example within global conservation planning or climate change mitigation analyses, it visualizes the currently available data on forest management on a global level.

This dataset contains all the spatial distributions predicted for the paper on monitoring programs of the Gulf of Mexico and the Gulf of Mexico Data Atlas, using statistical habitat models fitted to the survey data contained in the dataset whose UDI is "FL.x703.000:0002". The data provided in this dataset are PNG files showing the spatial patterns of probability of encounter of 61 fish and invertebrate functional groups/species/life stages of the Gulf of Mexico. The spatial distributions provided here are not for specific years, but rather long-term, average spatial distributions for the period 2000-present.

This dataset provides maps of the distribution of ecosystem functional types (EFTs) and the interannual variability of EFTs at 0.05 degree resolution across the conterminous United States (CONUS) for 2001 to 2014. EFTs are groupings of ecosystems based on their similar ecosystem functioning that are used to represent the spatial patterns and temporal variability of key ecosystem functional traits without prior knowledge of vegetation type or canopy architecture. Sixty-four EFTs were derived from the metrics of a 2001-2014 time-series of satellite images of the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) product MOD13C2. EFT diversity was calculated as the modal (most repeated) EFT and interannual variability was calculated as the number of unique EFTs for each pixel.

Escalation of human-elephant conflict (HEC) in India threatens its Asian elephant (Elephas maximus) population and victimizes local communities. India supports 60% of the total Asian elephant population in the world. Understanding HEC spatial patterns will ensure targeted mitigation efforts and efficient resource allocation to high-risk regions. This study deals with the spatial aspects of HEC in Keonjhar forest division, where 345 people were killed and 5,145 hectares of croplands were destroyed by elephant attacks during 2001–2018. We classified the data into three temporal phases (HEC1: 2001–2006, HEC2: 2007–2012, and HEC3: 2013–2018), in order to (1) derive spatial patterns of HEC; (2) identify the hotspots of HEC and its different types along with the number of people living in the high-risk zones; and (3) assess the temporal change in the spatial risk of HEC. Significantly dense clusters of HEC were identified in Keonjhar and Ghatgaon forest ranges throughout the 18 years, whereas Champua forest range became a prominent hotspot since HEC2. The number of people under HEC risk escalated from 14,724 during HEC1 and 34,288 in HEC2, to 65,444 people during HEC3. Crop damage was the most frequent form of HEC in the study area followed by house damage and loss of human lives. Risk mapping of HEC types and high priority regions that are vulnerable to HEC, provides a contextual background for researchers, policy makers and managers.

We present a map of arable land (both utilized and abandoned) together with a validation data set for eight countries of the former Soviet Union (fSU), namely Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Republic of Moldova, Russian Federation and Ukraine. The map has a spatial resolution of 10 arc-seconds and represents the year 2010. The map is based on the integration of a number of existing maps and a training data set collected using visual interpretation of very high resolution (VHR) imagery. The map can be used for carbon modelling, assessment of land use, land use change and evaluation of agriculture potential. An additional validation data set was collected through visual interpretation of VHR imagery by trained experts, and can be used for validation of other maps from this region. The overall accuracies of the map by region and country, i.e. Caucasus, Belarus, Kazakhstan, Republic of Moldova, Russian Federation and Ukraine, are 90±2%, 84±2%, 92±1%, 78±3%, 95±1%, 83±2%, respectively.

Information on global cropland distribution and agricultural production is critical for the world's agricultural monitoring and food security. Here, datasets of cropland extent and agricultural production are presented. A new Self-adapting Statistics Allocation Model (SASAM) to develop the global map of cropland distribution was proposed. Cropland area statistics are used to rank the input cropland maps, and then a scoring table is built to indicate the agreement among the input datasets.

This map displays urban forest patch layers for New York, NY; Philadelphia, PA; and Baltimore, MD. The map was created by the USDA Forest Service Service Northern Research Station in collaboration with Dr. Matthew Baker, UMBC. Each city's forest patch map was created using a high-resolution urban tree canopy map derived from LiDAR and NAIP imagery. Morphological spatial pattern analysis (MSPA) was used to distinguish forest patches from remaining distributed tree canopy in each city (see individual layer descriptions for more details). Patches are separated into two size classes: Forested Natural Areas are patches with greater core area and thickness than Groves. Patches are classified by land ownership categories using local parcel data available from each city. Ownership categories include: Federal, State, Municipal, Commercial/Industrial, Institutional, and Private Residential owners. Parcels with unknown ownership were assigned Municipal ownership. The intent of this map is to identify potential forested natural areas within cities across all types of land ownership, for the purposes of environmental policy, planning, and management.

Node of the Institute of Statistics and Cartography of Andalusia. Regional Government of Andalusia. WMS Population Mesh Service. Integrated in the Spatial Data Infrastructure of Andalusia following the guidelines of the Statistical and Cartographic System of Andalusia. WMS map service of spatial distribution of the population of Andalusia in cells of 250m x 250m. The information represented in these maps has been georeferenced from the location of the postal address where each of the inhabitants of Andalusia resides. To facilitate the representation of the information and to preserve statistical confidentiality, a regular mesh has been drawn with cells of 250 meters on the side, where all the information that corresponds in each case has been added. Information that could not be georeferenced has been estimated using spatial analysis techniques. On December 23, 2019, the demographic statistical information of the population data, corresponding to January 1, 2018, is presented. The website of the Institute of Statistics and Cartography of Andalusia offers a visualization service: "Spatial distribution of the population of Andalusia" for interactive consultation https://www.juntadeandalucia.es/institutodeestadisticaycartografia/distributionpob/index.htm

This forest patch layer was created using a 2017 high-resolution urban tree canopy map of New York City derived from LiDAR and NAIP imagery (https://data.cityofnewyork.us/Environment/Tree-Canopy-Change-2010-2017-/by9k-vhck). To identify forest area, tree canopy over impervious surfaces was first subtracted from the canopy layer, including buildings and roads identified from planimetric data available from New York City (https://opendata.cityofnewyork.us/). Morphological spatial pattern analysis (MSPA; Vogt et al. 2007) was then used to distinguish forest patches from remaining tree canopy using an edge parameter of 15 m based on observed changes in vegetation composition and structure (Baker, unpublished data). MSPA applies the edge parameter to distinguish interiors (i.e. ‘cores’) from surrounding edges, as well as five other morphometric primitives (i.e. branches, bridges, loops, and islets) that reflect how canopy is or is not connected to cores. Patches always include core areas and their surrounding edges, as well as any perforations. Patches are separated into two size classes: Forested Natural Areas are patches with greater core area and thickness than Groves.Patches are classified by land ownership categories using local parcel data available from New York City (https://data.cityofnewyork.us/City-Government/Primary-Land-Use-Tax-Lot-Output-PLUTO-/64uk-42ks/data). Ownership categories include: Federal, State, Municipal, Commercial/Industrial, Institutional, and Private Residential owners. Parcels with unknown ownership were assigned Municipal ownership.Vogt P, Riitters K H, Estreguil C, Kozak J, Wade T G and Wickham J D 2007 Mapping spatial patterns with morphological image processing Landsc. Ecol. 22 171–7. https://doi.org/10.1007/s10980-006-9013-2

Environmental variables used or not used in model, along with the respective relative contribution scores from the fitted MaxEnt model.

This dataset provides a map of the distribution of ecosystem functional types (EFTs) at 0.05 degree resolution across Mexico for 2001 to 2014. EFTs are groupings of ecosystems based on their similar ecosystem functioning that are used to represent the spatial patterns and temporal variability of key ecosystem functional traits without prior knowledge of vegetation type or canopy architecture. Sixty-four EFTs were derived from the metrics of a 2001-2014 time-series of satellite images of the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) product MOD13C2. EFT diversity was calculated as the modal (most repeated) EFT for each pixel.

Spatial GIS data layers and maps of modeled a) Pesticide Use Density (PUD) and b) Wetland Pesticide Occurrence Index (WPOI) of herbicides, fungicides an insecticides in the agricultural extent of the Canadian Prairie Pothole Region.