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).

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 greatly hindered 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 consists of six types of data : forest fruit resource data, meteorological data, pest data, elevation data, soil data and planting management data in Bazhou area. This data set provides a scientific theoretical basis for exploring the current situation of ‘Korla Fragrant Pear’ industry, promoting the quality and efficiency of fruit industry, and realizing the high-quality development of digital management of fruit industry in Xinjiang.

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

The air quality in Beijing, especially its PM2.5 level, has become of increasing public concern because of its importance and sensitivity related to health risks. A set of monitored PM2.5 data from 31 stations, released for the first time by the Beijing Environmental Protection Bureau, covering 37 days during autumn 2012, was processed using spatial interpolation and overlay analysis. Following analyses of these data, a distribution map of cumulative exceedance days of PM2.5 and a temporal variation map of PM2.5 for Beijing have been drawn. Computational and analytical results show periodic and directional trends of PM2.5 spreading and congregating in space, which reveals the regulation of PM2.5 overexposure on a discontinuous medium-term scale. With regard to the cumulative effect of PM2.5 on the human body, the harm from lower intensity overexposure in the medium term, and higher overexposure in the short term, are both obvious. Therefore, data of population distribution were integrated into the aforementioned PM2.5 spatial spectrum map. A spatial statistical analysis revealed the patterns of PM2.5 gross exposure and exposure probability of residents in the Beijing urban area. The methods and conclusions of this research reveal relationships between long-term overexposure to PM2.5 and people living in high-exposure areas of Beijing, during the autumn of 2012.

Suitability analysis is a landscape modeling process that is used to determine which locations are best suited for certain uses. The landscape planner specifies the environmental and cultural factors considered important to decision making, selects the appropriate data layers, weights them, and uses geoprocessing tools to filter the criteria and identify the best locations. For example, a planner may want to assess where habitats are located for endangered species and how close a site may be to suburban development. Or you may be researching a site for a new petroleum pipeline and need to figure out which sites would be suitable based on elevation, slope, endangered species habitats, and proximity to urban and suburban areas.This can be a time-consuming process with conventional desktop GIS tools. Landscape Modeler is a web-based application that makes the entire process more efficient. It allows you to use raster services to visualize information such as critical habitats, development risk, and fire potential, across the United States. This information can be used to research sites for urban development, housing developments, habitat locations, and other projects that require you to weigh several types of data against each other.

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.

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.

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.

Contingency tables from spatial overlay analyses.

The environmental sensitivity index analysis presented here is an overlay analysis based on the cumulative value of various environmental features in Sarpy County, which are weighted for their relative significance or sensitivity. The greater the environmental signifi cance or sensitivity of a given landscape, the higher the ESI score. Although beyond the scope of this study, ecosystem services and material fl ow frameworks are a different set of environmental analysis methods that could also be applied to quantify the fl ows of natural and human systems in Sarpy County. Ecosystem service and fl ow frameworks seek to defi ne fl ows, or temporal fl uxes of materials, through a given systems. The basic difference between overlay analysis and material fl ow analysis for the environment is in the unit of analysis; where the unit of analysis in a fl ow framework is a volume of material over time per area, and the unit of analysis in overlay modeling is an ordinally ranked value of environmental quality for a given purpose. European ecosystem services frameworks (translated as “landscape functions” out of the German Landschaftsfunktionen) have many similar categories to the traditional overlay modeling used here, and includes the added dimension of fl ow over time for materials within the landscape functions factor framework.Created by Vireo & submitted to Sarpy County 9/5/2013.Data current as of the last business day.

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 www.rtb.cgiar.org/RTBMaps

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

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.

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.

Analysis of ‘King County Tax Parcel Centroids with select City of Seattle geographic overlays’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/360b2b98-85f4-4a30-ae63-1b047824ef61 on 13 February 2022.

--- Dataset description provided by original source is as follows ---

--- Original source retains full ownership of the source dataset ---

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 study aims to identify potential locations for ecotourism development in the Batticaloa District, using GIS-AHP spatial analysis. Five thematic layers were considered throughout this analysis: landscape, protected area, topography, accessibility, and community characteristics. The thematic layers were given appropriate weights and integrated into the GIS through the weighted overlay analysis.

Data were extracted from the Rwanda soil dataset and analyzed using the geo-spatial tools of ArcGIS.aAND, Andisols; ALF, Alfisols; ENT, Entisols; INCEPT, Inceptisols; HIST, Histosols; MOLL, Mollisols; OX, Oxisols; ULT, Ultisols; VERT, Vertisols.bTotal Rwanda soil and Arabica coffee coverage per agro-ecological zone.cSoil and Arabica coffee coverage in percentage per agro-ecological zone over total Rwanda soil area and Arabica coffee area, respectively.Distribution of soil types (ha) and areas of Arabica coffee cultivation in the ten agro-ecological zones of Rwanda.

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