Result of overlay analysis of all 28 map layers listed in Table 7.1, spanning flora and fauna, human use and cultural heritage interests. The maximum cell values are nine, reflecting that in these cells features from nine different map layers overlap. The summary analysis was performed as a so-called GIS overlay analysis using custom-made Python scripts in ArcGIS Pro 3.0.2. In principle, the different map layers presented in Chapters 4-6 were simply stacked on top of each other, and for each 250x250 m cell in a grid system covering the entire AOI, the number of map layers with features present in the cell were counted. Thus, a resulting cell value of e.g., 3 indicates that at the centre of the cell three different map layers have features present. In rare cases, an individual layer may have several features present at the cell centre, e.g., two cultural heritage zone 3 areas, but the layer will still only add a value of one to the overlay. Thus, it is the number of different layer with features present that is summarised, not the number of individual features.

The Bayinguoleng Mongolian Autonomous Prefecture of Xinjiang is a continental arid climate with abundant light and heat resources.“Korla Fragrant Pear”has become a pillar industry of economic forest and fruit in Bazhou. With the continuous expansion of planting scale, the disadvantages of industrial planting have become increasingly prominent, which has caused great obstacles to the sustainable green development of fragrant pear. In this study, GIS spatial overlay analysis and three-phase fruit resource data were used to explore the industrial resources of“Korla Fragrant Pear”in Bazhou. This data set is composed of six types of data : resource data, meteorological data, pest and disease data, elevation data, soil data and planting management data of“Korla Fragrant Pear”in Bazhou area. In order to ensure the accuracy of the data, the field personnel have been organized to use satellite images and ArcGIS to check and verify the survey data, and to revise the non-standard, incorrect and missing information to ensure that the inspection pass rate is more than 95 %. This data set provides a scientific theoretical basis for exploring the current situation of“Korla Fragrant Pear”industry, promoting the quality and efficiency of forest and fruit industry, and realizing the high-quality development of digital management of forest and fruit industry in Xinjiang.

Figure 7.1. Result of overlay analysis of all 51 map layers listed in Table 7.1, spanning flora and fauna, human use and cultural heritage interests. The maximum cell values are 14, reflecting that in these cells features from 14 different map layers overlap.

Contingency tables from spatial overlay analyses.

Figure 7.3. Result of overlay analysis of 34 map layers with mainly biologically relevant information (see column “Sub-analysis, biology” in Table 7.1 for included map layers).

1) Spatial data: This dataset includes raw shapefiles used for an overlay analysis of digitized imagery, pre- and post-herbicide treatment from a remotely piloted aircraft system to invasive Phragmites australis. Images were digitized in ArcGIS Pro (v. 3.1.0, ESRI Inc., Redlands, CA). Files include the targeted treatment area, pre-treatment (2022) digitized P. australis classification, and post-treatment (2023) digitized vegetation damage. Site codes correspond to the following sites: BDD- Baie du Doré, RPP- Rondeau Provincial Park, SL- Spongy Lake, WD- Wood Drive. Note: there is no post-treatment classification at Wood Drive. 2) Data: This dataset includes outputted area and calculated percentage values from the overlay analysis, maximum drift measurements, and vegetation transect data/metadata.

Ruoqiang County in Xinjiang is a warm temperate continental desert arid climate, rich in light and heat resources. Jujube has become a pillar industry of economic forest and fruit in Ruoqiang County. With the continuous expansion of planting scale, the disadvantages of industrial planting have become increasingly prominent, which has caused great obstacles to the sustainable green development of jujube industry.In this study, GIS spatial overlay analysis and three-phase fruit resource data were used to explore the resource situation of Ruoqiang jujube industry. This data set consists of six types of data : jujube resource data, meteorological data, pest data, soil data, planting management data and traceability data.In order to ensure the accuracy of the data, the field personnel have been organized to use satellite images and ArcGIS to check and verify the survey data, and to revise the non-standard, incorrect and missing information to ensure that the inspection pass rate is more than 95 %. This data set provides a scientific theoretical basis for exploring the current situation of Ruoqiang jujube industry, promoting the quality and efficiency of forest and fruit industry, and realizing the high-quality development of digital management of forest and fruit industry in Xinjiang.

RTB Maps is a cloud-based electronic Atlas. We used ArGIS 10 for Desktop with Spatial Analysis Extension, ArcGIS 10 for Server on-premise, ArcGIS API for Javascript, IIS web services based on .NET, and ArcGIS Online combining data on the cloud with data and applications on our local server to develop an Atlas that brings together many of the map themes related to development of roots, tubers and banana crops. The Atlas is structured to allow our participating scientists to understand the distribution of the crops and observe the spatial distribution of many of the obstacles to production of these crops. The Atlas also includes an application to allow our partners to evaluate the importance of different factors when setting priorities for research and development. The application uses weighted overlay analysis within a multi-criteria decision analysis framework to rate the importance of factors when establishing geographic priorities for research and development.Datasets of crop distribution maps, agroecology maps, biotic and abiotic constraints to crop production, poverty maps and other demographic indicators are used as a key inputs to multi-objective criteria analysis.Further metadata/references can be found here: http://gisweb.ciat.cgiar.org/RTBmaps/DataAvailability_RTBMaps.htmlDISCLAIMER, ACKNOWLEDGMENTS AND PERMISSIONS:This service is provided by Roots, Tubers and Bananas CGIAR Research Program as a public service. Use of this service to retrieve information constitutes your awareness and agreement to the following conditions of use.This online resource displays GIS data and query tools subject to continuous updates and adjustments. The GIS data has been taken from various, mostly public, sources and is supplied in good faith.RTBMaps GIS Data Disclaimer• The data used to show the Base Maps is supplied by ESRI.• The data used to show the photos over the map is supplied by Flickr.• The data used to show the videos over the map is supplied by Youtube.• The population map is supplied to us by CIESIN, Columbia University and CIAT.• The Accessibility map is provided by Global Environment Monitoring Unit - Joint Research Centre of the European Commission. Accessibility maps are made for a specific purpose and they cannot be used as a generic dataset to represent "the accessibility" for a given study area.• Harvested area and yield for banana, cassava, potato, sweet potato and yam for the year 200, is provided by EarthSat (University of Minnesota’s Institute on the Environment-Global Landscapes initiative and McGill University’s Land Use and the Global Environment lab). Dataset from Monfreda C., Ramankutty N., and Foley J.A. 2008.• Agroecology dataset: global edapho-climatic zones for cassava based on mean growing season, temperature, number of dry season months, daily temperature range and seasonality. Dataset from CIAT (Carter et al. 1992)• Demography indicators: Total and Rural Population from Center for International Earth Science Information Network (CIESIN) and CIAT 2004.• The FGGD prevalence of stunting map is a global raster datalayer with a resolution of 5 arc-minutes. The percentage of stunted children under five years old is reported according to the lowest available sub-national administrative units: all pixels within the unit boundaries will have the same value. Data have been compiled by FAO from different sources: Demographic and Health Surveys (DHS), UNICEF MICS, WHO Global Database on Child Growth and Malnutrition, and national surveys. Data provided by FAO – GIS Unit 2007.• Poverty dataset: Global poverty headcount and absolute number of poor. Number of people living on less than $1.25 or $2.00 per day. Dataset from IFPRI and CIATTHE RTBMAPS GROUP MAKES NO WARRANTIES OR GUARANTEES, EITHER EXPRESSED OR IMPLIED AS TO THE COMPLETENESS, ACCURACY, OR CORRECTNESS OF THE DATA PORTRAYED IN THIS PRODUCT NOR ACCEPTS ANY LIABILITY, ARISING FROM ANY INCORRECT, INCOMPLETE OR MISLEADING INFORMATION CONTAINED THEREIN. ALL INFORMATION, DATA AND DATABASES ARE PROVIDED "AS IS" WITH NO WARRANTY, EXPRESSED OR IMPLIED, INCLUDING BUT NOT LIMITED TO, FITNESS FOR A PARTICULAR PURPOSE. By accessing this website and/or data contained within the databases, you hereby release the RTB group and CGCenters, its employees, agents, contractors, sponsors and suppliers from any and all responsibility and liability associated with its use. In no event shall the RTB Group or its officers or employees be liable for any damages arising in any way out of the use of the website, or use of the information contained in the databases herein including, but not limited to the RTBMaps online Atlas product.APPLICATION DEVELOPMENT:• Desktop and web development - Ernesto Giron E. (GeoSpatial Consultant) e.giron.e@gmail.com• GIS Analyst - Elizabeth Barona. (Independent Consultant) barona.elizabeth@gmail.comCollaborators:Glenn Hyman, Bernardo Creamer, Jesus David Hoyos, Diana Carolina Giraldo Soroush Parsa, Jagath Shanthalal, Herlin Rodolfo Espinosa, Carlos Navarro, Jorge Cardona and Beatriz Vanessa Herrera at CIAT, Tunrayo Alabi and Joseph Rusike from IITA, Guy Hareau, Reinhard Simon, Henry Juarez, Ulrich Kleinwechter, Greg Forbes, Adam Sparks from CIP, and David Brown and Charles Staver from Bioversity International.Please note these services may be unavailable at times due to maintenance work.Please feel free to contact us with any questions or problems you may be having with RTBMaps.

The Southwestern Region is 20.6 million acres. There are six national forests in Arizona, five national forests and a national grassland in New Mexico, and one national grassland each in Oklahoma and the Texas panhandle.The region ranges in elevation from 1,600 feet above sea level and an annual rain fall of 8 inches in Arizona's lower Sonoran Desert to 13,171-foot high Wheeler Peak and over 35 inches of precipitation a year in northern New Mexico. Geographic Information Systems or GIS are computer systems, software and data used to analyze and display spatial or locational data about surface features. One of the strengths of GIS is the capability to overlay or compare multiple feature layers. A user can then analyze the relationship between the layers. Data, reports and maps produced through GIS are used by managers and resource specialists to make decisions about land management activities on National Forests. The National Forests of the Southwestern Region maintain and utilize GIS data for various features on the ground. Some of these datasets are made available for download through this page. Resources in this dataset:Resource Title: GIS Datasets. File Name: Web Page, url: https://www.fs.usda.gov/detail/r3/landmanagement/gis/?cid=STELPRDB5202474 Selected GIS datasets for the Southwestern Region are available for download from this page.Resource Software Recommended: ArcExplorer,url: http://www.esri.com/software/arcexplorer/index.html

The Growth Centers data on the Future Land Use Map were developed for the Division of Planning, RI Statewide Planning Program as part of an update to a state land use plan. These data are included in the Plan as Figure 121-02-(01), Future Land Use Map. The growth centers were an end product of a GIS overlay analysis of land suitability and scenario planning for future growth. Initially the factors for centers included 9 urban communities; Providence, East Providence, Pawtucket, Cranston, Central Falls, Warwick, West Warwick, Newport and Woonsocket as potential urban centers as opposed to identifying specific neighborhoods in those municipalities. Historical downtowns and traditional mixed-use central business cores in urban fringe / suburban communities were included as potential town centers, as well as, some of the historical village downtowns and some traditional mixed-use cores in rural communities. All communities in the State either include one or more existing or potential centers or are within the Urban Services Boundary on the map. The growth centers shown in these data were selected by the Statewide Planning staff, the Technical Committee and the State Planning Council through a series of discussions at public meetings, and comments received at public hearings and workshops in the final adoption of Land Use 2025 in 2006. Centers depicted on the Future Land Use 2025 map are illustrative of potential new centers that may be established. It is not a intended as a comprehensive inventory of existing centers. Other centers may be illustrated and or proposed in municipal comprehensive plans. Full descriptions of the methodology for the GIS analysis and scenario planning can be found within the Technical Appendix D to Land Use 2025, Geographic Analysis for Land Available and Suitable for Development for Land Use 2025. Land Use 2025: State Land Use Policies and Plan was published by the RI Statewide Planning Program on April 13, 2006. The Plan directs the state and communities to concentrate growth inside the Urban Services Boundary (USB) and within potential growth centers in rural areas. It establishes different development approaches for urban and rural areas. This Map has several purposes and applications: It is intended to be used as a policy guide for directing growth to areas most capable of supporting current and future developed uses and to direct growth away from areas less suited for development. Secondly, the Map is a guide to assist the state and communities in making land use policies. It is important to note the Map is a generalized portrayal of state land use policy. It is not a statewide zoning map. Zoning matters and individual land use decisions are the prerogative of local governments. Growth Centers are envisioned to be areas that will encourage development that is both contiguous to existing development with low fiscal and environmental impacts. They are intended to be compact developed areas (existing or new) containing a defined central core that accommodate community needs for residential and economic functions. Centers are intended to provide optimum use of land and services, and offer a choice of diverse housing stock, economic functions, and cultural and governmental uses. Density will vary greatly between centers subject to site constraints; however, it is intended that they will share the common characteristic of compact development that capitalizes on existing infrastructure. Centers should reflect traditional New England development patterns with a human scale of blocks, streets, open spaces that offer walkability and access to transit where available. In suburban areas, centers should be distinguished from surrounding sprawling development by a closer proximity between residential and non-residential uses. In rural areas, centers should be surrounded by natural areas, farmland, or open space, and may have a mixed-use and or commercial area in the core for neighborhood-scale goods and services. The land use element is the over arching element in Rhode Island's State Guide Plan. The Plan articulates goals, objectives and strategies to guide the current and future land use planning of municipalities and state agencies. The purpose of the plan is to guide future land use and to present policies under which state and municipal plans and land use activities will be reviewed for consistency with the State Guide Plan. The Map is a graphical representation of recommendations for future growth patterns in the State. The Map contains a USB that shows where areas with public services supporting urban development presently exist, or are likely to be provided, through 2025. Also included on the map are growth centers which are potential areas for development and redevelopment outside of the USB. These data will be updated when plan is updated or upon an amendment approved by the State Planning Council.

Overlays polygons as determined by the planning department of the City of Alexandria. Small Area Plans are the 18 geographic planning areas within the City that together create the City Master Plan. These master plans are guiding documents that provide community-based long-range planning and analysis regarding the physical development and appearance of neighborhoods across the City. Overlay plans are Supplemental plans and amendments to existing Small Area Plans that provide greater standards or regulations. Properties located within the boundaries are subject to the requirements and regulations per the overlay plan in addition to other City standards and policies. If the overlay plan is silent to or does not address a specific issue or topic, the underlying Small Area Plan applies.

The zoning overlay layer is suited for overlay analysis and is appropriate for use on small scale cartographics, general query and overlay operations. Overlay Districts are administrative district created by city ordinance. Sets within these districts require special approvals before issuance of any building permits. View detailed metadata.

[Metadata] Description: Hawaii County Zoning as of November 2023. Source: County of Hawaii, Planning Dept., November 8, 2023.

The City's Zoning Ordinance and Map are tools for land-use planning. Each zone and overlay classification guides the physical development of the City through a set of requirements set forth in the City of Worcester's Zoning Ordinance as amended. As part of the planning data layers, the zoning overlay districts map layer is an integral part of the City of Worcester Geographic Information System. Planning data layers are accessed by personnel in most City departments for basic applications such as viewing, querying, and map output production. More advanced user applications may focus on planning analysis, spatial analysis, presentation output, and review of proposed development. This feature class combines the previously separate Mixed Use Districts and Overlay Districts, with the addition of Local Historic Districts.This map data layer represents the zoning overlay districts for the City of Worcester, Massachusetts as defined by the Zoning Ordinance, Article III, as amended.OVERLAY DISTRICTS: MU: Mixed Use Development FP: Floodplain AE: Airport Environs WR: Water Resources Protection AOD: Arts Overlay District AROD: Adaptive Re-use Overlay District SPOD: Shrewsbury Street Parking Overlay District BCPOD :Blackstone Canal Parking Overlay District CPOD: Chandler Street Parking Overlay District GPOD: Grafton Street Parking Overlay District QPOD: Quinsigamond Village Parking Overlay District DSOD: Downtown/Blackstone Canal Sign Overlay District BSOD: Blackstone River Parkway Sign Overlay District USOD: Union Station View Corridor Sign Overlay District Informing Worcester is the City of Worcester's open data portal where interested parties can obtain public information at no cost

This hosted feature layer has been published in RI State Plane Feet NAD 83.The Urban Services Boundary was developed for the Rhode Island Statewide Planning Program as part of a GIS overlay analysis of land suitability and availability for Figure 121-02-(01), Rhode Island Future Land Use Map - 2025 of the Land use 2025: State Land Use Policies and Plan. This is the State of Rhode Island"s plan for conservation and development in the 21st century. The boundary represents the general extent of the areas within which public services supporting urban development presently exist, or are likely to be provided, through 2025. Within the boundary most land should be served by public water service and many areas will have public sewer service available as well. Public transit service should be generally available. Several watersheds and other sensitive resource areas were excluded from the boundary, indicating that protection of the resources involved must be a principle concern limiting future development intensity potential. Also included within the boundary, are other undeveloped areas which will have lower development intensities due to the presence of resources constraints and or limited infrastructure.The Land use 2025: State Land Use Policies and Plan, was published by the Rhode Island Statewide Planning Program on April 13, 2006. The intent of the Urban Services Boundary is to provide an indication for planning purposes of areas where a higher level of public services is anticipated to be available to accommodate more intensive development and redevelopment. Public services in areas outside the Urban Services Boundary are anticipated to be more limited, and planned development intensities should be lower. The Plan directs the state and communities to concentrate growth inside the boundary and within locally designated centers in rural areas, and to pursue different development approaches for urban and rural areas. The Future Land Use Map with the Urban Services Boundary describes the recommended 2025 future land use pattern for the State of Rhode Island. This map has several purposes and applications: It is intended as a policy guide for directing growth to areas most capable of supporting current and future developed uses (and conversely, away from areas less suited for development). In this regard, it is intended to inform state and local capital investment decisions so that investments may target and support growth in appropriate areas and discourage growth in inappropriate locations. Secondly, the Future Land Use Map is a guide to assist the state and communities in making land use policies. It is important to note the Map is a generalized portrayal of state land use policy. IT IS NOT A "STATEWIDE ZONING MAP" - zoning matters and individual land use decisions are the prerogative of local governments.

Background: Malaria continues to pose a major public health challenge in tropical regions. Despite significant efforts to control malaria in Tanzania, there are still residual transmission cases. Unfortunately, little is known about where these residual malaria transmission cases occur and how they spread. In Tanzania, for example, the transmission is heterogeneously distributed. In order to effectively control and prevent the spread of malaria, it is essential to understand the spatial distribution and transmission patterns of the disease. This study seeks to predict areas that are at high risk of malaria transmission so that intervention measures can be developed to accelerate malaria elimination efforts.

Methods: This study employs a geospatial-based model to predict and map out malaria risk area in Kilombero Valley. Environmental factors related to malaria transmission were considered and assigned valuable weights in the Analytic Hierarchy Process (AHP), an online system using a pairwise comparison technique. The malaria hazard map was generated by a weighted overlay of the altitude, slope, curvature, aspect, rainfall distribution, and distance to streams in Geographic Information Systems (GIS). Finally, the risk map was created by overlaying components of malaria risk including hazards, elements at risk, and vulnerability. Results: The study demonstrates that the majority of the study area falls under the moderate-risk level (61%), followed by the low-risk level (31%), while the high-malaria risk area covers a small area, which occupies only 8% of the total area. Conclusion: The findings of this study are crucial for developing spatially targeted interventions against malaria transmission in residual transmission settings. Predicted areas prone to malaria risk provide information that will inform decision-makers and policymakers for proper planning, monitoring, and deployment of interventions. Methods Data acquisition and description The study employed both primary and secondary data, which were collected from numerous sources based on the input required for the implementation of the predictive model. Data collected includes the locations of all public and private health centers that were downloaded free from the health portal of the United Republic of Tanzania, Ministry of Health, Community Development, Gender, Elderly, and Children, through the universal resource locator (URL) (http://moh.go.tz/hfrportal/). Human population data was collected from the 2012 population housing census (PHC) for the United Republic of Tanzania report. Rainfall data were obtained from two local offices; Kilombero Agricultural Training and Research Institute (KATRIN) and Kilombero Valley Teak Company (KVTC). These offices collect meteorological data for agricultural purposes. Monthly data from 2012 to 2017 provided from thirteen (13) weather stations. Road and stream network shapefiles were downloaded free from the MapCruzin website via URL (https://mapcruzin.com/free-tanzania-arcgis-maps-shapefiles.htm). With respect to the size of the study area, five neighboring scenes of the Landsat 8 OLI/TIRS images (path/row: 167/65, 167/66, 167/67, 168/66 and 168/67) were downloaded freely from the United States Geological Survey (USGS) website via URL: http://earthexplorer.usgs.gov. From July to November 2017, the images were selected and downloaded from the USGS Earth Explorer archive based on the lowest amount of cloud cover coverage as viewed from the archive before downloading. Finally, the digital elevation data with a spatial resolution of three arc-seconds (90m by 90m) using WGS 84 datum and the Geographic Coordinate System were downloaded free from the Shuttle Radar Topography Mission (SRTM) via URL (https://dds.cr.usgs.gov/srtm/version2_1/SRTM3/Africa/). Only six tiles that fall in the study area were downloaded, coded tiles as S08E035, S09E035, S10E035, S08E036, S09E036, S10E036, S08E037, S09E037 and S10E037. Preparation and Creation of Model Factor Parameters Creation of Elevation Factor All six coded tiles were imported into the GIS environment for further analysis. Data management tools, with raster/raster data set/mosaic to new raster feature, were used to join the tiles and form an elevation map layer. Using the spatial analyst tool/reclassify feature, the generated elevation map was then classified into five classes as 109–358, 359–530, 531–747, 748–1017 and >1018 m.a.s.l. and new values were assigned for each class as 1, 2, 3, 4 and 5, respectively, with regards to the relationship with mosquito distribution and malaria risk. Finally, the elevation map based on malaria risk level is levelled as very high, high, moderate, low and very low respectively. Creation of Slope Factor A slope map was created from the generated elevation map layer, using a spatial analysis tool/surface/slope feature. Also, the slope raster layer was further reclassified into five subgroups based on predefined slope classes using standard classification schemes, namely quantiles as 0–0.58, 0.59–2.90, 2.91–6.40, 6.41–14.54 and >14.54. This classification scheme divides the range of attribute values into equal-sized sub-ranges, which allow specifying the number of the intervals while the system determines where the breaks should be. The reclassified slope raster layer subgroups were ranked 1, 2, 3, 4 and 5 according to the degree of suitability for malaria incidence in the locality. To elaborate, the steeper slope values are related to lesser malaria hazards, and the gentler slopes are highly susceptible to malaria incidences. Finally, the slope map based on malaria risk level is leveled as very high, high, moderate, low and very low respectively. Creation of Curvature Factor Curvature is another topographical factor that was created from the generated elevation map using the spatial analysis tool/surface/curvature feature. The curvature raster layer was further reclassified into five subgroups based on predefined curvature class. The reclassified curvature raster layer subgroups were ranked to 1, 2, 3, 4 and 5 according to their degree of suitability for malaria occurrence. To explain, this affects the acceleration and deceleration of flow across the surface. A negative value indicates that the surface is upwardly convex, and flow will be decelerated, which is related to being highly susceptible to malaria incidences. A positive profile indicates that the surface is upwardly concave and the flow will be accelerated which is related to a lesser malaria hazard, while a value of zero indicates that the surface is linear and related to a moderate malaria hazard. Lastly, the curvature map based on malaria risk level is leveled as very high, high, moderate, low, and very low respectively.
Creation of Aspect Factor As a topographic factor associated with mosquito larval habitat formation, aspect determines the amount of sunlight an area receives. The more sunlight received the stronger the influence on temperature, which may affect mosquito larval survival. The aspect of the study area also was generated from the elevation map using spatial analyst tools/ raster /surface /aspect feature. The aspect raster layer was further reclassified into five subgroups based on predefined aspect class. The reclassified aspect raster layer subgroups were ranked as 1, 2, 3, 4 and 5 according to the degree of suitability for malaria incidence, and new values were re-assigned in order of malaria hazard rating. Finally, the aspect map based on malaria risk level is leveled as very high, high, moderate, low, and very low, respectively. Creation of Human Population Distribution Factor Human population data was used to generate a population distribution map related to malaria occurrence. Kilombero Valley has a total of 42 wards, the data was organized in Ms excel 2016 and imported into the GIS environment for the analysis, Inverse Distance Weighted (IDW) interpolation in the spatial analyst tool was applied to interpolate the population distribution map. The population distribution map was further reclassified into five subgroups based on potential to malaria risk. The reclassified map layer subgroups were ranked according to the vulnerability to malaria incidence in the locality such as areas having high population having the highest vulnerability and the less population having less vulnerable, and the new value was assigned as 1, 2, 3, 4 and 5, and then leveled as very high, high, moderate, low and very low malaria risk level, respectively. Creation of Proximity to Health Facilities Factor The distribution of health facilities has a significant impact on the malaria vulnerability of the population dwellings in the Kilombero Valley. The health facility layer was created by computing distance analysis using proximity multiple ring buffer features in spatial analyst tool/multiple ring buffer. Then the map layer was reclassified into five sub-layers such as within (0–5) km, (5.1–10) km, (10.1–20) km, (20.1–50) km and >50km. According to a WHO report, it is indicated that the human population who live nearby or easily accessible to health facilities is less vulnerable to malaria incidence than the ones who are very far from the health facilities due to the distance limitation for the health services. Later on, the new values were assigned as 1, 2, 3, 4 and 5, and then reclassified as very high, high, moderate, low and very low malaria risk levels, respectively. Creation of Proximity to Road Network Factor The distance to the road network is also a significant factor, as it can be used as an estimation of the access to present healthcare facilities in the area. Buffer zones were calculated on the path of the road to determine the effect of the road on malaria prevalence. The road shapefile of the study area was inputted into GIS environment and spatial analyst tools / multiple ring buffer feature were used to generate five buffer zones with the

This project maps the conversion from mid-20th century (1946-71) flood and sprinkler irrigation to sprinkler irrigation (center-pivot and other sprinkler), and other land types (fallow, crop, and flood remaining flood) in Montana, by 2019.

Over the past 50 years, many producers in Montana have made changes to their irrigation practice and infrastructure in an effort to increase irrigation efficiency, defined as the ratio of water consumed by crops to water diverted or pumped (consumed water ÷ diverted water). Changes in the method of irrigation, especially conversion from flood to sprinkler irrigation, may have significant on-farm benefits such as reduced labor and increased production. Conversion can have both beneficial and adverse impacts on streamflow and aquatic ecosystems depending on local site-specific hydrogeologic conditions and how irrigation water is managed. As part of the Montana Water Center’s effort to better understand the effects of increased irrigation efficiency in Montana (Lonsdale et al. 2020), historic conversion from flood to sprinkler irrigation was analyzed using available agricultural statistics, maps from state and federal sources, and an independent Geographic Information Systems (GIS) analysis.

The first Resource in this HydroShare Collection, "Conversion from Flood to Sprinkler Irrigation in Montana between Mid-20th Century and 2019", presents the GIS analysis and maps the amount and spatial distribution of conversion from flood to sprinkler irrigation, between the mid-20th century and 2019. Historic mid-20th century irrigation was mapped in detail from 1943-1965 by the State Engineer’s Office and from 1966-1971 by the Montana Water Resources Board—the predecessor of the Montana Department of Natural Resources and Conservation (DNRC). A scanned and georeferenced version of the Water Resources Surveys (WRS) was compared with maps of contemporary irrigated land (Montana Department of Revenue’s 2019 Final Land Unit Classification—DORFLU2019) to estimate the area of land converted from flood to sprinkler irrigation. Prior to GIS analysis, both datasets were edited to ensure valid comparison between irrigated field mapping conducted at the two points in time. To estimate the amount of conversion from flood to sprinkler irrigation, and other uses, the GIS layers (WRS flood and sprinkler 1946-1971 and DOR-FLU 2019) were overlain in ArcGIS; then the clipping erase functions were used to select the WRS flood and sprinkler parcels that were shown as sprinkler irrigated in 2019. Additional conversion classes were also mapped that represent the changes from WRS flood and sprinkler to cropland, hayland and fallow, and WRS sprinkler- remaining- sprinkler and flood remaining flood. Details of the analysis are provided in Appendix C. of the main report and which is located within HydroShare Resource: https://www.hydroshare.org/resource/15392cb3617b4519af6ae8972f603502/data/contents/Appendix_C._Methods_and_data_for_GIS_mapping_of_conversion_from_flood_to_sprinkler_irrigation.pdf

The second Resource in this Collection," Uncertainty analysis of irrigation conversion polygon areas", provides files used in the uncertainty analysis of polygon areas resulting from overlaying/clipping/erase GIS operations that map the irrigation system conversions from mid-20th century to 2019.There are several sources of uncertainty in the conversion mapping results. The first is that the analysis only accounts for changes that occurred between the WRS 1946-71 and DORFLU2019; it is possible that additional flood irrigation developed between the two points in time may have also been converted to sprinkler. Another source of uncertainty is due to GIS processing and overlay/clip/erase functions that create “sliver” polygons of apparent change due to misalignment of the WRS 1946-71 and DORFLU2019 layers (i.e., co-registration error). This was evaluated using the spatially distributed probabilistic (SDP) method of Leonard and others (2020) and found to be small—generally less than one percent of the area of conversion polygons. Digitizing error was evaluated indirectly and found to be about ±12 percent of the reported area values. The values sum in quadrature to provide an overall estimate of error in polygon area of 12%. Conversion from flood to sprinkler polygon areas presented in the main report, and associated error statistics, apply to the whole dataset at the statewide scale. For use at the basin scale (for example, HUC4 Upper Yellowstone, the end user should review the uncertainty estimate for specific conversion polygons and refine if necessary. Please see Appendix D. Uncertainty analysis.pdf for details of the analysis. All citations are included in the References.txt file and in the main report.

This Biomass and Carbon per Acre dataset was compiled for the 48-state CONUS Area in order to identify local, state and regional trends in forest cover and landscape composition. It can be used as a graphic, as inputs to GIS overlay procedures, or to summarize average or total amounts of forest attributes within analysis areas of interest. Through application of a nearest-neighbor imputation approach, mapped estimates of forest biomass density were developed for the contiguous United States using the annual forest inventory conducted by the USDA Forest Service Forest Inventory and Analysis (FIA) program, MODIS satellite imagery, and ancillary geospatial datasets. This data product contains the following 7 raster maps: Aboveground Forest Biomass, Belowground Forest Biomass, Forest Tree Bole Biomass, Forest Sapling Biomass, Forest Stump Biomass, Forest Top Biomass, Woodland Species Biomass. All layers have a 250 meter pixel resolution and values represent biomass pounds per acre. The paper on which these maps are based may be found here: https://dx.doi.org/10.2737/RDS-2013-0004 Access to full metadata and other information can be accessed here: https://dx.doi.org/10.2737/RDS-2013-0004This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

This dataset provides a complete and highly structured view of retail precincts across multiple regions, designed to support market analysis, location intelligence, retail expansion, and AI/ML modelling. It delivers information in multiple formats to accommodate a wide range of analytical, GIS, and business use cases, making it an essential resource for retail analysts, urban planners, investment teams, and data-driven decision-makers.

Included Data Files & Formats

1. Point File
2. All Precincts by Centroid (GeoJSON/Shapefile).
3. Each precinct is represented as a single point located at its geometric centroid.
4. Includes key attributes: id, precinct name. Ideal for quick visualisation, clustering, and spatial reference when boundary shapes are not required.

5. Supports applications such as proximity analysis, mapping, and location-based AI/ML models.

6. Polygon File – All Precincts by Polygon (GeoJSON/Shapefile)

7. Provides full precinct boundaries in polygon geometry for precise spatial representation.

8. Includes key attributes: id, precinct name.

9. Enables detailed GIS analysis, including area calculations, spatial overlays, and integration with mobility or demographic datasets.

10. Suitable for urban planning, retail network optimisation, trade area analysis, and catchment studies.

PDF – Precinct Reports (see attached sample) - Reports include comprehensive retail precinct insights across malls and high streets, showing retailer mix by category (F&B, Apparel, Fitness, Grocery, Health/Fitness, and more), catchment size, shopper origins, population, consuming class population and precinct ranking—designed to provide insights on store expansion opportunities. - Supports qualitative assessments, market research, and executive reporting.

1. Excel – Tabular Overview
2. Comprehensive spreadsheet with all precincts, including the following fields: ID, Precinct Name, Ranking, State, Country
3. Enables straightforward filtering, sorting, and integration with other datasets.
4. Useful for high-level analysis, reporting, and as a reference table for GIS mapping or AI models.

A discussion of recoding the cumulative probabilities to the ordinal scale.