8 datasets found
  1. 13.3 Distance Analysis Using ArcGIS

    • training-iowadot.opendata.arcgis.com
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    Updated Mar 4, 2017
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    Iowa Department of Transportation (2017). 13.3 Distance Analysis Using ArcGIS [Dataset]. https://training-iowadot.opendata.arcgis.com/documents/IowaDOT::13-3-distance-analysis-using-arcgis/about
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    Dataset updated
    Mar 4, 2017
    Dataset authored and provided by
    Iowa Department of Transportationhttps://iowadot.gov/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    One important reason for performing GIS analysis is to determine proximity. Often, this type of analysis is done using vector data and possibly the Buffer or Near tools. In this course, you will learn how to calculate distance using raster datasets as inputs in order to assign cells a value based on distance to the nearest source (e.g., city, campground). You will also learn how to allocate cells to a particular source and to determine the compass direction from a cell in a raster to a source.What if you don't want to just measure the straight line from one place to another? What if you need to determine the best route to a destination, taking speed limits, slope, terrain, and road conditions into consideration? In cases like this, you could use the cost distance tools in order to assign a cost (such as time) to each raster cell based on factors like slope and speed limit. From these calculations, you could create a least-cost path from one place to another. Because these tools account for variables that could affect travel, they can help you determine that the shortest path may not always be the best path.After completing this course, you will be able to:Create straight-line distance, direction, and allocation surfaces.Determine when to use Euclidean and weighted distance tools.Perform a least-cost path analysis.

  2. n

    Sea level rise, groundwater rise, and contaminated sites in the San...

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    • datadryad.org
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    Updated May 22, 2023
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    Kristina Hill; Daniella Hirschfeld; Caroline Lindquist; Forest Cook; Scott Warner (2023). Sea level rise, groundwater rise, and contaminated sites in the San Francisco Bay Area, and Superfund Sites in the contiguous United States [Dataset]. http://doi.org/10.6078/D15X4N
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    zipAvailable download formats
    Dataset updated
    May 22, 2023
    Dataset provided by
    Utah State University
    UNSW Sydney
    University of California, Berkeley
    Authors
    Kristina Hill; Daniella Hirschfeld; Caroline Lindquist; Forest Cook; Scott Warner
    License

    https://spdx.org/licenses/CC0-1.0.htmlhttps://spdx.org/licenses/CC0-1.0.html

    Area covered
    San Francisco Bay Area, United States
    Description

    Rising sea levels (SLR) will cause coastal groundwater to rise in many coastal urban environments. Inundation of contaminated soils by groundwater rise (GWR) will alter the physical, biological, and geochemical conditions that influence the fate and transport of existing contaminants. These transformed products can be more toxic and/or more mobile under future conditions driven by SLR and GWR. We reviewed the vulnerability of contaminated sites to GWR in a US national database and in a case comparison with the San Francisco Bay region to estimate the risk of rising groundwater to human and ecosystem health. The results show that 326 sites in the US Superfund program may be vulnerable to changes in groundwater depth or flow direction as a result of SLR, representing 18.1 million hectares of contaminated land. In the San Francisco Bay Area, we found that GWR is predicted to impact twice as much coastal land area as inundation from SLR alone, and 5,297 state-managed sites of contamination may be vulnerable to inundation from GWR in a 1-meter SLR scenario. Increases of only a few centimeters of elevation can mobilize soil contaminants, alter flow directions in a heterogeneous urban environment with underground pipes and utility trenches, and result in new exposure pathways. Pumping for flood protection will elevate the salt water interface, changing groundwater salinity and mobilizing metals in soil. Socially vulnerable communities are more exposed to this risk at both the national scale and in a regional comparison with the San Francisco Bay Area. Methods Data Dryad This data set includes data from the California State Water Resources Control Board (WRCB), the California Department of Toxic Substances Control (DTSC), the USGS, the US EPA, and the US Census. National Assessment Data Processing: For this portion of the project, ArcGIS Pro and RStudio software applications were used. Data processing for superfund site contaminants in the text and supplementary materials was done in RStudio using R programming language. RStudio and R were also used to clean population data from the American Community Survey. Packages used include: Dplyr, data.table, and tidyverse to clean and organize data from the EPA and ACS. ArcGIS Pro was used to compute spatial data regarding sites in the risk zone and vulnerable populations. DEM data processed for each state removed any elevation data above 10m, keeping anything 10m and below. The Intersection tool was used to identify superfund sites within the 10m sea level rise risk zone. The Calculate Geometry tool was used to calculate the area within each coastal state that was occupied by the 10m SLR zone and used again to calculate the area of each superfund site. Summary Statistics were used to generate the total proportion of superfund site surface area / 10m SLR area for each state. To generate population estimates of socially vulnerable households in proximity to superfund sites, we followed methods similar to that of Carter and Kalman (2020). First, we generated buffers at the 1km, 3km, and 5km distance of superfund sites. Then, using Tabulate Intersection, the estimated population of each census block group within each buffer zone was calculated. Summary Statistics were used to generate total numbers for each state. Bay Area Data Processing: In this regional study, we compared the groundwater elevation projections by Befus et al (2020) to a combined dataset of contaminated sites that we built from two separate databases (Envirostor and GeoTracker) that are maintained by two independent agencies of the State of California (DTSC and WRCB). We used ArcGIS to manage both the groundwater surfaces, as raster files, from Befus et al (2020) and the State’s point datasets of street addresses for contaminated sites. We used SF BCDC (2020) as the source of social vulnerability rankings for census blocks, using block shapefiles from the US Census (ACS) dataset. In addition, we generated isolines that represent the magnitude of change in groundwater elevation in specific sea level rise scenarios. We compared these isolines of change in elevation to the USGS geological map of the San Francisco Bay region and noted that groundwater is predicted to rise farther inland where Holocene paleochannels meet artificial fill near the shoreline. We also used maps of historic baylands (altered by dikes and fill) from the San Francisco Estuary Institute (SFEI) to identify the number of contaminated sites over rising groundwater that are located on former mudflats and tidal marshes. The contaminated sites' data from the California State Water Resources Control Board (WRCB) and the Department of Toxic Substances (DTSC) was clipped to our study area of nine-bay area counties. The study area does not include the ocean shorelines or the north bay delta area because the water system dynamics differ in deltas. The data was cleaned of any duplicates within each dataset using the Find Identical and Delete Identical tools. Then duplicates between the two datasets were removed by running the intersect tool for the DTSC and WRCB point data. We chose this method over searching for duplicates by name because some sites change names when management is transferred from DTSC to WRCB. Lastly, the datasets were sorted into open and closed sites based on the DTSC and WRCB classifications which are shown in a table in the paper's supplemental material. To calculate areas of rising groundwater, we used data from the USGS paper “Projected groundwater head for coastal California using present-day and future sea-level rise scenarios” by Befus, K. M., Barnard, P., Hoover, D. J., & Erikson, L. (2020). We used the hydraulic conductivity of 1 condition (Kh1) to calculate areas of rising groundwater. We used the Raster Calculator to subtract the existing groundwater head from the groundwater head under a 1-meter of sea level rise scenario to find the areas where groundwater is rising. Using the Reclass Raster tool, we reclassified the data to give every cell with a value of 0.1016 meters (4”) or greater a value of 1. We chose 0.1016 because groundwater rise of that little can leach into pipes and infrastructure. We then used the Raster to Poly tool to generate polygons of areas of groundwater rise.

  3. n

    Data from: Patch size and vegetation structure drive changes to...

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    Updated Jan 28, 2021
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    Harrison Jones; Scott Robinson (2021). Patch size and vegetation structure drive changes to mixed-species flock diversity and composition across a gradient of fragment sizes in the Western Andes of Colombia [Dataset]. http://doi.org/10.5061/dryad.80gb5mkng
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    zipAvailable download formats
    Dataset updated
    Jan 28, 2021
    Dataset provided by
    University of Florida
    Authors
    Harrison Jones; Scott Robinson
    License

    https://spdx.org/licenses/CC0-1.0.htmlhttps://spdx.org/licenses/CC0-1.0.html

    Area covered
    Colombia, Andes
    Description

    This data set represents a series of 502 mixed-species bird flock compositions, and derived taxonomic, functional, and phylogenetic diversity indices, that were gathered along a gradient of forest fragment sizes (range = 10-173 ha) in the Colombian Western Andes. We sampled mixed-species flocks using transect surveys along 14 transects in 8 fragments and a continuous forest reference site in the same landscape and at the same elevation (~1900-2200 m.a.s.l.). We also used buffer analysis to quantify the proportion of forest cover and forest edge within 1 km of each transect, and calculated local vegetation density and complexity, as well as distance from edge, for each 100-meter transect segment (n = 70 segments). Flock composition data observed on a transect were used to calculate overall species richness and flock size as well as two indices of functional and phylogenetic diversity; we calculated the stadardized effect size (SES) of each measure to account for the correlation between these measures and species richness. We also provide the raw counts of each species for each flock composition. These data were used for the analyses in Jones and Robinson (2020).

    Methods Study System and Sites

    We conducted all fieldwork in subtropical humid forests located within the municipality of El Cairo, Valle del Cauca department in Colombia. The study region is part of the Serrania de los Paraguas in the Western Andes mountain range, a center of avian threatened species diversity and endemism within Colombia. The study landscape in this municipality consists of a patchwork of forest fragments embedded in a matrix of cattle pasture, regenerating scrub, and coffee farms. Within this landscape, we selected eight fragments representing a gradient in patch sizes (range 10 to 170 ha). Sites are in the same altitudinal belt (1900-2200 m.a.s.l.) and matrix type (cattle pasture) to control for effects of altitude and matrix type on flock size and composition. Within-patch disturbance is common in fragmented Andean forests in Colombia, particularly illegal selective logging, which in our landscape typically occurred as removal of select old-growth trees for lumber by landowners; logging histories varied considerably from historical to ongoing, and extensive to limited, both within and between patches. We established 500-meter transects through forest interior (n = 14 total transects) which were opportunistically placed on existing trails, at variable distances from the edge of the fragments. We further divided each transect into 100-meter segments to account for heterogeneity in vegetation structure within transects. We accounted for edge effects by measuring the distance to forest edge of each transect segment.

    We stratified forest fragments into large (≥ 100 ha), medium (~30-50 ha), and small (≤ 20 ha) size categories and selected a minimum of two replicates of each; these represent the range of fragment sizes available in our study landscape. We also included a non-fragmented reference site (Reserva Natural Comunitária Cerro El Inglés, ~750 ha) connected to over 10,000 ha of continuous forest to the north and west along the spine of the Serranía de los Paraguas. We only selected fragments with primary or late-successional secondary forest; vegetation structure and canopy height varied substantially between patches based on intensities of selective logging and land-use histories (see above). Fragments were all separated by ≥ 100 meters to minimize among-patch movement of birds, and all transects in different fragments were at least 250 meters apart.

    Transect Surveys for Mixed-species Flocks

    We performed transect surveys for mixed-species flocks, adapted from Goodale et al. (2014), in forest fragments from June-August 2017 (boreal migrants absent) and January-March 2018 (boreal migrants present). Both sampling periods corresponded to a dry season in the Western Andes, which has a bimodal two-dry, two-wet seasonality pattern. For each transect, we spent two and a half sequential field days performing continuous transect surveys; we conducted surveys in small fragments, large fragments, and continuous forest sites in random order to avoid a temporal bias in sampling. Surveys were distributed across the morning (7:30-11:30) and evening (15:00-17:30) hours. Transects were walked slowly and continuously by 2-3 observers, including local birdwatchers familiar with all species (Harrison Jones present for all surveys); flocking birds were identified by both sight and sound. When we encountered a flock, we noted the time of day and transect segment in which it was observed and spent up to a maximum of 45 minutes characterizing it with 10x binoculars. At least 5 minutes were spent with each flock, following it if possible. Because detection of species in flocks was imperfect, we only included a flock observation in the analysis if we felt that at least 80% of the individuals were observed (e.g. after spending several minutes of continuous observation at the end of the survey period without observing a new species or individual); incomplete flock observations were not included in analyses. We feel that our survey methodology accurately described flock composition because birds moved and called frequently in flocks, leading to high detectability. We noted the start and end time of each survey, and the presence of incomplete flocks to calculate flock encounter rate. We also supplemented the transect surveys with data from flocks opportunistically observed on a transect while performing other fieldwork. Some flocks in the data set represent flock compositions recorded near but not on a transect; these compositions have no associated transect segment.

    Calculation of Landscape-level Variables

        We obtained landscape-level variables for analyses using geographic information software (GIS) analysis in ArcGIS (ArcMap 10.3.1; Esri; Redlands, CA). To quantify landscape composition and configuration, we buffered each transect (n = 14) by 1 km; buffers extended from the entire length of the transect. We then calculated measures of landscape composition and configuration using a recent land-cover/use categorization made by the Corporación Autónoma Regional del Valle del Cauca, converted to a 25-m cell-size raster. To quantify landscape composition, we calculated percentages of the forest-cover type within each buffer using the ‘isectpolyrst’ tool in Geospatial Modelling Environment (version 0.7.4.0). We measured landscape configuration for each transect as edge density, or length of all forest edges (in meters) divided by total buffer area (in hectares). The distance to edge was calculated in meters for each 100-meter transect segment (n = 70) as the shortest straight-line distance between the center point of the segment and the nearest edge of the fragment. 
    

    Vegetation Measurements and Principal Component Analysis

        We measured vegetation structure in each 100-m transect segment used for flock sampling. Vegetation measurements were made from June-August 2017; based on our observations of vegetation, we assumed variation between the two sampling periods was minimal. We used the sampling methodology of James and Shugart (1970), following the modifications made by Stratford and Stouffer (2013), and further modified to be used with belt transects. Broadly, the methodology comprises two components for every 100-meter transect segment: (1) the quantification of canopy cover, ground cover, canopy height, and foliage height diversity of vegetation using point sampling every 10 meters and (2) the quantification of shrub, vine, fern, palm, and tree fern and tree density using 3 meter-wide belt sampling.
    

    For the point sampling, we measured eight variables at ten-meter intervals, for 10 points per 100-meter segment. As a measure of foliage height diversity along the transect, we noted the presence or absence of live vegetation at five heights: <0.5 m, >0.5–3 m, >3–10 m, >10–20 m, and >20 m. Above 3 meters, we used a rangefinder to determine heights while sighting through a tube with crosshairs. Canopy and ground cover were calculated to the nearest 1/8th of the field of view by sighting through a vertical canopy densiometer (GRS Densiometer, Geographic Resource Solutions, Arcata, CA). For each segment, we averaged values for canopy cover, and ground cover, and calculated the proportion of points at which vegetation was present for each height category. For the belt transect sampling, we surveyed vegetation along the same transects and calculated densities for each 100-m transect interval. We counted all shrubs, vines, ferns, tree ferns, and palms encountered on 1.5 meters to either side. Secondly, we counted all trees (woody vegetation > 2 m in height) within 1.5 meters of the transect and measured their diameter at breast height (DBH). Trees were later categorized into six DBH size classes for analysis: 1-7 cm, 8-15 cm, 16-23 cm, 24-30 cm, 31-50 cm, and > 50 cm. We additionally recorded the largest tree’s DBH.

        To quantify foliage height diversity, we calculated the Shannon Diversity Index of the proportion of points with vegetation present in each of the five height bands for each segment (n = 70 segments). To reduce redundancy and minimize correlation between variables, we (separately) ordinated our tree DBH and understory plant density data using principal component analysis (PCA: McGarigal et al. 2000) for each 100-meter transect segment. We column (Z score) standardized data prior to ordination to account for differences in the units of measurement and used the covariance matrix to run the PCA. The principal components were interpreted using the significance of the principal component loadings. The PCA was run in R (version 3.5.1) using the princomp function in the stats package. The Shannon Index was calculated using the diversity function of the vegan package
    
  4. Data from: Geospatial based model for malaria risk prediction in Kilombero...

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    Updated Jul 7, 2023
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    Stephen Mwangungulu; Emmanuel Kaindoa; Dorothea Deus; Zakaria Ngereja (2023). Geospatial based model for malaria risk prediction in Kilombero Valley, south-eastern Tanzania [Dataset]. http://doi.org/10.5061/dryad.d51c5b081
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    zipAvailable download formats
    Dataset updated
    Jul 7, 2023
    Dataset provided by
    Ifakara Health Institutehttp://www.ihi.or.tz/
    Ardhi University
    Authors
    Stephen Mwangungulu; Emmanuel Kaindoa; Dorothea Deus; Zakaria Ngereja
    License

    https://spdx.org/licenses/CC0-1.0.htmlhttps://spdx.org/licenses/CC0-1.0.html

    Area covered
    Tanzania
    Description

    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

  5. a

    Urban Park Size (Southeast Blueprint Indicator)

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    Updated Jul 15, 2024
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    U.S. Fish & Wildlife Service (2024). Urban Park Size (Southeast Blueprint Indicator) [Dataset]. https://hub.arcgis.com/content/fws::urban-park-size-southeast-blueprint-indicator-2024/about?uiVersion=content-views
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    Dataset updated
    Jul 15, 2024
    Dataset authored and provided by
    U.S. Fish & Wildlife Service
    Area covered
    Description

    Reason for Selection Protected natural areas in urban environments provide urban residents a nearby place to connect with nature and offer refugia for some species. They help foster a conservation ethic by providing opportunities for people to connect with nature, and also support ecosystem services like offsetting heat island effects (Greene and Millward 2017, Simpson 1998), water filtration, stormwater retention, and more (Hoover and Hopton 2019). In addition, parks, greenspace, and greenways can help improve physical and psychological health in communities (Gies 2006). Urban park size complements the equitable access to potential parks indicator by capturing the value of existing parks.Input DataSoutheast Blueprint 2024 extentFWS National Realty Tracts, accessed 12-13-2023Protected Areas Database of the United States(PAD-US):PAD-US 3.0 national geodatabase -Combined Proclamation Marine Fee Designation Easement, accessed 12-6-20232020 Census Urban Areas from the Census Bureau’s urban-rural classification; download the data, read more about how urban areas were redefined following the 2020 censusOpenStreetMap data “multipolygons” layer, accessed 12-5-2023A polygon from this dataset is considered a beach if the value in the “natural” tag attribute is “beach”. Data for coastal states (VA, NC, SC, GA, FL, AL, MS, LA, TX) were downloaded in .pbf format and translated to an ESRI shapefile using R code. OpenStreetMap® is open data, licensed under theOpen Data Commons Open Database License (ODbL) by theOpenStreetMap Foundation (OSMF). Additional credit to OSM contributors. Read more onthe OSM copyright page.2021 National Land Cover Database (NLCD): Percentdevelopedimperviousness2023NOAA coastal relief model: volumes 2 (Southeast Atlantic), 3 (Florida and East Gulf of America), 4 (Central Gulf of America), and 5 (Western Gulf of America), accessed 3-27-2024Mapping StepsCreate a seamless vector layer to constrain the extent of the urban park size indicator to inland and nearshore marine areas <10 m in depth. The deep offshore areas of marine parks do not meet the intent of this indicator to capture nearby opportunities for urban residents to connect with nature. Shallow areas are more accessible for recreational activities like snorkeling, which typically has a maximum recommended depth of 12-15 meters. This step mirrors the approach taken in the Caribbean version of this indicator.Merge all coastal relief model rasters (.nc format) together using QGIS “create virtual raster”.Save merged raster to .tif and import into ArcPro.Reclassify the NOAA coastal relief model data to assign areas with an elevation of land to -10 m a value of 1. Assign all other areas (deep marine) a value of 0.Convert the raster produced above to vector using the “RasterToPolygon” tool.Clip to 2024 subregions using “Pairwise Clip” tool.Break apart multipart polygons using “Multipart to single parts” tool.Hand-edit to remove deep marine polygon.Dissolve the resulting data layer.This produces a seamless polygon defining land and shallow marine areas.Clip the Census urban area layer to the bounding box of NoData surrounding the extent of Southeast Blueprint 2024.Clip PAD-US 3.0 to the bounding box of NoData surrounding the extent of Southeast Blueprint 2024.Remove the following areas from PAD-US 3.0, which are outside the scope of this indicator to represent parks:All School Trust Lands in Oklahoma and Mississippi (Loc Des = “School Lands” or “School Trust Lands”). These extensive lands are leased out and are not open to the public.All tribal and military lands (“Des_Tp” = "TRIBL" or “Des_Tp” = "MIL"). Generally, these lands are not intended for public recreational use.All BOEM marine lease blocks (“Own_Name” = "BOEM"). These Outer Continental Shelf lease blocks do not represent actively protected marine parks, but serve as the “legal definition for BOEM offshore boundary coordinates...for leasing and administrative purposes” (BOEM).All lands designated as “proclamation” (“Des_Tp” = "PROC"). These typically represent the approved boundary of public lands, within which land protection is authorized to occur, but not all lands within the proclamation boundary are necessarily currently in a conserved status.Retain only selected attribute fields from PAD-US to get rid of irrelevant attributes.Merged the filtered PAD-US layer produced above with the OSM beaches and FWS National Realty Tracts to produce a combined protected areas dataset.The resulting merged data layer contains overlapping polygons. To remove overlapping polygons, use the Dissolve function.Clip the resulting data layer to the inland and nearshore extent.Process all multipart polygons (e.g., separate parcels within a National Wildlife Refuge) to single parts (referred to in Arc software as an “explode”).Select all polygons that intersect the Census urban extent within 0.5 miles. We chose 0.5 miles to represent a reasonable walking distance based on input and feedback from park access experts. Assuming a moderate intensity walking pace of 3 miles per hour, as defined by the U.S. Department of Health and Human Service’s physical activity guidelines, the 0.5 mi distance also corresponds to the 10-minute walk threshold used in the equitable access to potential parks indicator.Dissolve all the park polygons that were selected in the previous step.Process all multipart polygons to single parts (“explode”) again.Add a unique ID to the selected parks. This value will be used in a later step to join the parks to their buffers.Create a 0.5 mi (805 m) buffer ring around each park using the multiring plugin in QGIS. Ensure that “dissolve buffers” is disabled so that a single 0.5 mi buffer is created for each park.Assess the amount of overlap between the buffered park and the Census urban area using “overlap analysis”. This step is necessary to identify parks that do not intersect the urban area, but which lie within an urban matrix (e.g., Umstead Park in Raleigh, NC and Davidson-Arabia Mountain Nature Preserve in Atlanta, GA). This step creates a table that is joined back to the park polygons using the UniqueID.Remove parks that had ≤10% overlap with the urban areas when buffered. This excludes mostly non-urban parks that do not meet the intent of this indicator to capture parks that provide nearby access for urban residents. Note: The 10% threshold is a judgement call based on testing which known urban parks and urban National Wildlife Refuges are captured at different overlap cutoffs and is intended to be as inclusive as possible.Calculate the GIS acres of each remaining park unit using the Add Geometry Attributes function.Buffer the selected parks by 15 m. Buffering prevents very small and narrow parks from being left out of the indicator when the polygons are converted to raster.Reclassify the parks based on their area into the 7 classes seen in the final indicator values below. These thresholds were informed by park classification guidelines from the National Recreation and Park Association, which classify neighborhood parks as 5-10 acres, community parks as 30-50 acres, and large urban parks as optimally 75+ acres (Mertes and Hall 1995).Assess the impervious surface composition of each park using the NLCD 2021 impervious layer and the Zonal Statistics “MEAN” function. Retain only the mean percent impervious value for each park.Extract only parks with a mean impervious pixel value <80%. This step excludes parks that do not meet the intent of the indicator to capture opportunities to connect with nature and offer refugia for species (e.g., the Superdome in New Orleans, LA, the Astrodome in Houston, TX, and City Plaza in Raleigh, NC).Extract again to the inland and nearshore extent.Export the final vector file to a shapefile and import to ArcGIS Pro.Convert the resulting polygons to raster using the ArcPy Feature to Raster function and the area class field.Assign a value of 0 to all other pixels in the Southeast Blueprint 2024 extent not already identified as an urban park in the mapping steps above. Zero values are intended to help users better understand the extent of this indicator and make it perform better in online tools.Use the land and shallow marine layer and “extract by mask” tool to save the final version of this indicator.Add color and legend to raster attribute table.As a final step, clip to the spatial extent of Southeast Blueprint 2024.Note: For more details on the mapping steps, code used to create this layer is available in theSoutheast Blueprint Data Downloadunder > 6_Code. Final indicator valuesIndicator values are assigned as follows:6= 75+ acre urban park5= 50 to <75 acre urban park4= 30 to <50 acre urban park3= 10 to <30 acre urban park2=5 to <10acreurbanpark1 = <5 acre urban park0 = Not identified as an urban parkKnown IssuesThis indicator does not include park amenities that influence how well the park serves people and should not be the only tool used for parks and recreation planning. Park standards should be determined at a local level to account for various community issues, values, needs, and available resources.This indicator includes some protected areas that are not open to the public and not typically thought of as “parks”, like mitigation lands, private easements, and private golf courses. While we experimented with excluding them using the public access attribute in PAD, due to numerous inaccuracies, this inadvertently removed protected lands that are known to be publicly accessible. As a result, we erred on the side of including the non-publicly accessible lands.The NLCD percent impervious layer contains classification inaccuracies. As a result, this indicator may exclude parks that are mostly natural because they are misclassified as mostly impervious. Conversely, this indicator may include parks that are mostly impervious because they are misclassified as mostly

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    Caribbean Low-Urban Historic Landscapes (Southeast Blueprint Indicator)

    • hub.arcgis.com
    • secas-fws.hub.arcgis.com
    Updated Sep 25, 2023
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    U.S. Fish & Wildlife Service (2023). Caribbean Low-Urban Historic Landscapes (Southeast Blueprint Indicator) [Dataset]. https://hub.arcgis.com/maps/bb6f52c0a3f34311a0eb2b2bbcd211d3
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    Dataset updated
    Sep 25, 2023
    Dataset authored and provided by
    U.S. Fish & Wildlife Service
    Area covered
    Description

    Reason for Selection Low-urban historic landscapes indicate significant cultural landscapes whose cultural context has been less impacted by urban development. Cultural landscapes are “properties [that] represent the combined works of nature and of man” (UNESCO 2012). Loss of natural habitat within these cultural landscapes reduces their overall historic and cultural value. Input Data

    Southeast Blueprint 2023 subregions: Caribbean
    Southeast Blueprint 2023 extent
    2020 LANDFIRE Existing Vegetation Type (EVT) (v2.2.0) for Puerto Rico and the U.S. Virgin Islands; access the data for U.S. Insular Areas
    The following The National Register of Historic Places data for Puerto Rico provided by Eduardo Cancio, Information Systems Specialist with the Puerto Rico State Historic Preservation Office (SHPO) on 2-21-2023 (contact ecancio@prshpo.pr.gov for more information):NRHP_PR_individual_properties.shp
        NRHP_PR_lineal_districts.shp
        NRHP_PR_polygonal_districts.shp 
    

    The National Register of Historic Places reflects what Americans value in their historic built environment. It is the collection of our human imprint on the landscape that records through time our changing relationship with the landscape, bridging between modern life and our history by providing, as closely as possible, experiences that evoke our empathy and understanding of previous eras.

      OpenStreetMap data “multipolygons” layer, accessed 3-14-2023 
    

    A polygon from this dataset is considered a historic site if the “historic” tag is not null. In OpenStreetMap, a historic feature refers to “features that still exist or of which traces are observable, and that are of historic interest, or where the feature class is generally of historical interest”. We only used historic polygons if the name tag is also not null. OpenStreetMap® is open data, licensed under the Open Data Commons Open Database License (ODbL) by the OpenStreetMap Foundation (OSMF). Additional credit to OSM contributors. Read more on the OSM copyright page.

      Select USVI historic districts: Polygon boundaries for the Christiansted National Historic District on St. Thomas and Charlotte Amalie Historic and Architectural Historic District on St. Croix, provided by Nikita Beck with the University of the Virgin Islands on 3-6-2023 (contact nikita.beck@uvi.edu for more information)
    

    Mapping Steps

    Identify urban areas using the following classes from 2020 LANDFIRE EVT: Developed-High Intensity, Developed-Low Intensity, Developed-Medium Intensity, Developed-Open Space, Developed-Roads. Classify all urban pixels as 1 and all other pixels as 0.
    Calculate the percent urban in a 270 m radius circle for each pixel using the Focal Statistics tool in ArcGIS. Since the LANDFIRE data resolution is 30 m, 270 m (9 pixels) approximates a 250 m radius. Retain all pixels that are <50% urban within a 270 m radius. 
    Create a historic places layer by combining the following vector datasets as follows:Buffer National Register point data from the Puerto Rico SHPO by 100 m.
        Combine National Register polygons from the Puerto Rico SHPO, select USVI historic districts, and OpenStreetMap polygons. Only use OpenStreetMap polygons if both the historic and name columns are null. Buffer the polygons by 30 m.
        Buffer line data from the Puerto Rico SHPO by 30 m.
        Merge all buffered point, polygon, and line data into one layer and convert to a 30 m raster representing historic places.
    
    Use the historic places raster to remove areas that fall outside of the historic places.
    Reclassify the above raster into 3 classes, seen in the final indicator values below.
    Clip to the Caribbean Blueprint 2023 subregion.
    As a final step, clip to the spatial extent of Southeast Blueprint 2023. 
    

    Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. Final indicator values Indicator values are assigned as follows: 2 = Historic place with nearby low-urban buffer 1 = Historic place with nearby high-urban buffer 0 = Not identified as a historic place Known Issues

    There are likely spatial mapping errors for some of the historic areas. 
    Some historic areas with cultural importance are not captured in the National Register of Historic Places.
    The approach to measuring urban development doesn’t capture degradation to historic places that were historically in larger cities (e.g., courthouses and other downtown buildings). It also doesn’t distinguish between historic places that have always been urban and historic places that used to be low-urban.
    This layer likely underrepresents some historic areas in the U.S. Virgin Islands compared to Puerto Rico because we were unable to incorporate historic places data from the USVI SHPO during the timeline of this Blueprint update. As a result, some sites on the National Register of Historic Places are not depicted in this indicator.
    OpenStreetMap is a crowdsourced dataset. While members of the OpenStreetMap community often verify map features to check for accuracy and completeness, there is the potential for spatial errors (e.g., misrepresenting the boundary of a historic site) or incorrect tags (e.g., labelling an area as a historic site that does not have historic value). However, using a crowdsourced dataset gives on-the-ground experts, Blueprint users, and community members the power to fix errors and add new historic sites to improve the accuracy and coverage of this indicator in the future.
    Because open water is considered a non-urban landcover for the purposes of this analysis, this indicator is likely overprioritizing some urbanized historic areas that are close to water, such as marinas and bridges.
    

    Disclaimer: Comparing with Older Indicator Versions There are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email hilary_morris@fws.gov). Literature Cited OpenStreetMap. Historic. Data extracted through Geofabrik downloads. Accessed March 14, 2023. [https://wiki.openstreetmap.org/wiki/Key:historic].

    LANDFIRE, Earth Resources Observation and Science Center (EROS), U.S. Geological Survey. Published August 1, 2022. LANDFIRE 2020 Existing Vegetation Type (EVT) Puerto Rico US Virgin Islands. LF 2020, raster digital data. Sioux Falls, SD. [https://www.landfire.gov].UNESCO (2012) Operational Guidelines for the Implementation of the World Heritage Convention [1]. UNESCO World Heritage Centre. Paris. Page 14. [https://whc.unesco.org/archive/opguide12-en.pdf].

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    Island Habitat (Southeast Blueprint Indicator)

    • hub.arcgis.com
    • secas-fws.hub.arcgis.com
    • +1more
    Updated Sep 25, 2023
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    U.S. Fish & Wildlife Service (2023). Island Habitat (Southeast Blueprint Indicator) [Dataset]. https://hub.arcgis.com/datasets/1d076b5ffc164689afba7a09b054faea
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    Dataset updated
    Sep 25, 2023
    Dataset authored and provided by
    U.S. Fish & Wildlife Service
    Area covered
    Description

    Reason for SelectionIslands provide important habitat for many species, including birds, sea turtles, mammals, insects, and plants. Their relative isolation from disturbance and mainland predators can make them important breeding habitat for coastal birds and sea turtles (as represented by piping plover and loggerhead sea turtle). Their unique ecology and isolation can also make them important habitat for some mammals, plants and insects that are only found on islands (as represented by Cape Sable thoroughwort, Florida semaphore cactus, silver rice rat, and Bartram’s hairstreak butterfly). As a barrier that can protect the mainland from major storms, they also help protect ecosystems and human communities from extreme weather events. The critical habitat included in this indicator refers to areas with specific physical or biological features that are essential to conserving a federally threatened or endangered species and may require special management or protection.Input DataBase Blueprint 2022 extentSoutheast Blueprint 2023 extentIsland boundaries from the Global Island Explorer provided by the U.S. Geological Survey and Esri, accessed 5-13-2022. (Global Islands data has been updated since this release. Click here to download the latest version.)Critical habitat provided by the U.S. Fish and Wildlife Survey, accessed 6-23-2022Mapping StepsClip the Global Island Explorer data to the Base Blueprint 2022 extent and merge small and large islands.From the critical habitat data (CRITHAB_POLY.shp), select the following species based on ‘comname’: piping plover, loggerhead sea turtle, Cape Sable thoroughwort, Florida semaphore cactus, silver rice rat, Bartram’s hairstreak butterfly.Clip the critical habitat for the selected species to the merged islands layer.Convert the islands and selected species data to raster and clip to the spatial extent of Base Blueprint 2022.Add zero values to help users better understand the extent of this indicator and to make it perform better in online tools. Buffer the island shapefile by 40 km to make a continuous buffer along the coast, with no gaps. Use this to create a raster of zeros for that buffer.Combine rasters so parts of islands with selected species critical habitat get a value of 2, other island pixels get a value of 1, and all other areas in the buffer described above get a value of 0.As a final step, clip to the spatial extent of Southeast Blueprint 2023.Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. Final indicator valuesIndicator values are assigned as follows:2 = Island critical habitat for any of six threatened and endangered species (piping plover, loggerhead sea turtle, Cape Sable thoroughwort, Florida semaphore cactus, silver rice rat, or Bartram’s hairstreak butterfly)1 = Other island area0 = Not a coastal islandKnown IssuesThis indicator underestimates piping plover critical habitat in parts of the Chandeleur Islands off the coast of Louisiana. Locations of barrier islands can be highly dynamic, and the island boundaries and critical habitat data did not agree on the locations of some parts of the islands.Disclaimer: Comparing with Older Indicator Versions There are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email hilary_morris@fws.gov).Literature Cited Sayre, R., S. Noble, S. Hamann, R. Smith, D. Wright, S. Breyer, K. Butler, K. Van Graafeiland, C. Frye, D. Karagulle, D. Hopkins, D. Stephens, K. Kelly, Z, basher, D. Burton, J. Cress, K. Atkins, D. van Sistine, B. Friesen, B. Allee, T. Allen, P. Aniello, I Asaad, M. Costello, K. Goodin, P. Harris, M. Kavanaugh, H. Lillis, E. Manca, F. Muller-Karger, B. Nyberg, R. Parsons, J. Saarinen, J. Steiner, and A. Reed. 2018. A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized global ecological coastal units. Journal of Operational Oceanography–A Special Blue Planet Edition. [https://doi.org/10.1080/1755876X.2018.1529714]. U.S. Geological Survey and Esri. Global Island Explorer. Accessed May 13, 2022. [https://rmgsc.cr.usgs.gov/gie/]. U.S. Fish and Wildlife Service. Critical Habitat. Accessed June 23, 2022. [https://ecos.fws.gov/ecp/report/table/critical-habitat.html].

  8. a

    Utah Water Related Land Use

    • sgid-utah.opendata.arcgis.com
    • opendata.gis.utah.gov
    • +2more
    Updated Nov 22, 2019
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    Utah Automated Geographic Reference Center (AGRC) (2019). Utah Water Related Land Use [Dataset]. https://sgid-utah.opendata.arcgis.com/datasets/e3e7fc9316bb4ad09474401ff46e734f
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    Dataset updated
    Nov 22, 2019
    Dataset authored and provided by
    Utah Automated Geographic Reference Center (AGRC)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    Feature Classes are loaded onto tablet PCs and Field crews are sent to label the crop or land cover type and irrigation method for a subset of select fields or polygons. Each tablet PC is attached to a GPS unit for real-time tracking to continuously update the field crew’s location during the field labeling process.Digitizing is done as Geodatabase feature classes using ArcPro 3.1.0 with Sentinel imagery as a background with other layers added for reference. Updates to existing field boundaries of individual agricultural fields, urban areas and more are precisely digitized. Changes in irrigation type and land use are noted during this process.Cropland Data Layer (CDL) rasters from the United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) are downloaded for the appropriate year. https://nassgeodata.gmu.edu/CropScape/Zonal Statistics geoprocessing tools are used to attribute the polygons with updated crop types from the CDL. The data is then run through several stages of comparison to historical inventories and quality checking in order to determine and produce the final attributes.LUID - Unique ID number for each polygon in the final dataset, not consistent between yearly datasets.Landuse - A general land cover classification differentiating how the land is used.Agriculture: Land managed for crop or livestock purposes.Other: A broad classification of wildland.Riparian/Wetland: Wildland influenced by a high water table, often close to surface water.Urban: Developed areas, includes urban greenspace such as parks.Water: Surface water such as wet flats, streams, and lakes.CropGroup - Groupings of broader crop categories to allow easy access to or query of all orchard or grain types etc.Description - Attribute that describes/indicates the various crop types and land use types determined by the GIS process.IRR_Method - Crop Irrigation Method carried over from statewide field surveys ending in 2015 and updated based on imagery and yearly field checks.Drip: Water is applied through lines that slowly release water onto the surface or subsurface of the crop.Dry Crop: No irrigation method is applied to this agricultural land, the crop is irrigated via natural processes.Flood: Water is diverted from ditches or pipes upland from the crop in sufficient quantities to flood the irrigated plot.None: Associated with non-agricultural landSprinkler: Water is applied above the crop via sprinklers that generally move across the field.Sub-irrigated: This land does not have irrigation water applied, but due to a high water table receives more water, and is generally closely associated with a riparian area.Acres - Calculated acreage of the polygon.State - State where the polygons are found.Basin - The hydrologic basin where the polygons are found, closely related to HUC 6. These basin boundaries were created by DWRe to include portions of other basins that have inter-basin flows for management purposes.SubArea - The subarea where the polygons are found, closely related to the HUC 8. Subareas are subdivisions of the larger hydrologic basins created by DWRe.Label_Class - Combination of Label and Class_Name fields created during processing that indicates the specific crop, irrigation, and whether the CDL classified the land as a similar crop or an “Other” crop.LABEL - A shorthand descriptive label for each crop description and irrigation type.Class_Name - The majority pixel value from the USDA CDL Cropscape raster layer within the polygon, may differ from final crop determination (Description).OldLanduse - Similar to Landuse, but splits the agricultural land further depending on irrigation. Pre-2017 datasets defined this as Landuse.LU_Group - These codes represent some in-house groupings that are useful for symbology and other summarizing.Field_Check - Indicates the year the polygon was last field checked. *New for 2019SURV_YEAR - Indicates which year/growing season the data represents.

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    Learn how you can add new datasets to our index.

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Iowa Department of Transportation (2017). 13.3 Distance Analysis Using ArcGIS [Dataset]. https://training-iowadot.opendata.arcgis.com/documents/IowaDOT::13-3-distance-analysis-using-arcgis/about
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13.3 Distance Analysis Using ArcGIS

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Dataset updated
Mar 4, 2017
Dataset authored and provided by
Iowa Department of Transportationhttps://iowadot.gov/
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

One important reason for performing GIS analysis is to determine proximity. Often, this type of analysis is done using vector data and possibly the Buffer or Near tools. In this course, you will learn how to calculate distance using raster datasets as inputs in order to assign cells a value based on distance to the nearest source (e.g., city, campground). You will also learn how to allocate cells to a particular source and to determine the compass direction from a cell in a raster to a source.What if you don't want to just measure the straight line from one place to another? What if you need to determine the best route to a destination, taking speed limits, slope, terrain, and road conditions into consideration? In cases like this, you could use the cost distance tools in order to assign a cost (such as time) to each raster cell based on factors like slope and speed limit. From these calculations, you could create a least-cost path from one place to another. Because these tools account for variables that could affect travel, they can help you determine that the shortest path may not always be the best path.After completing this course, you will be able to:Create straight-line distance, direction, and allocation surfaces.Determine when to use Euclidean and weighted distance tools.Perform a least-cost path analysis.

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