How to use Spatial Data with ArcGIS Desktop /Real Projects

Learn density analysis in ArcGIS Desktop, applying simple density and kernel density methods to compute point and line concentrations in a circular neighborhood for presentation.

Explore Euclidean distance analysis in ArcGIS to identify hotels within 200 meters of roads, using the Euclidean distance tool to generate output distance and direction rasters.

Use cost path analysis to identify the least-cost route from Chester Valley to city center in ArcGIS Desktop, then create a cost path polyline for the most suitable road design.

Apply density analysis and spatial data evaluation in ArcGIS Desktop to assess health centers across eight countries and reveal outbreak spread through average nearest neighbor and directional distribution.

Create a line density map of fault lines per square kilometer using the line density tool in ArcGIS Desktop. Use the results to interpret fault line density and earthquake intensity.

Apply kernel density analysis in ArcGIS desktop to map virus outbreak density using health centers data across eight countries, with insights on cell size and search radius.

Learn how to create a continuous surface from point data using interpolation, comparing deterministic and geostatistical approaches, and applying inverse distance weighted method and kriging to predict spatial values.

Apply the idw method to estimate soil clay amounts from well points, creating a continuous surface to predict clay distribution across the basin.

Explore creating continuous surfaces with IDV and IDV barrier in ArcGIS to assess how a stream affects soil clay (salt rate) across a landscape, comparing methods.

Apply ordinary kriging to map lake pollution using ArcGIS desktop, converting samples into a continuous pollution surface and revealing higher pollution near factories.

Assess the accuracy of a kriging-generated continuous surface by comparing interpolated raster values with sample point values, using extract values to points and field differences to evaluate accuracy.

Learn how ArcGIS spatial analysis tools perform cell-based and zonal statistics on raster data, deriving per-cell and per-zone measures such as mean, min, max, and standard deviation.

Use ArcGIS Desktop's tabulate area tool to calculate farmland, forest, and urban areas by slope classes from raster data, converting cell sizes to square kilometers for land-use analysis.

Use cell statistics to create Turkey's temperature map from five years of raster data, then compute mean and maximum temperatures across the rasters.

Explore spatial data analysis with ArcGIS Desktop by computing crime rate and statistics for districts using zonal statistics as table, and apply the same method to Turkey's temperature data.

Reclassify raster data to integer values using the reclassification tool, enabling overlaps and registration across slope and digital elevation layers for analysis.

Convert vector data to raster in ArcGIS Desktop to enable overlap and raster-based analyses, using the feature to raster tool at a 20-meter resolution.

Use the raster calculator to overlay disaster layers—distance to fault, fire, flood, and landslide zones—to identify the most dangerous areas and calculate their square-meter extents.

Create analysis maps in ArcGIS desktop by preparing data, applying Euclidean distance and reclassifying rasters for roads, parks, schools, and slope layers, then overlay with a DEM to locate sites.

Reclassify the Euclidean distance rasters for parks, schools, and roads. Convert float values to five integer classes for the slope, using natural breaks, and prepare for a weighted overlay.

Discover weighted overlay analysis in ArcGIS by reclassifying distance, slope, and land-use rasters, then overlapping them with weights to locate optimal new urban sites near parks and schools.

Learn to build and run geoprocessing workflows in arcgis through model builder, using tools, variables, and connectors to create, edit, and share models.

Use model builder in ArcGIS to create a digital elevation model from vertex, contour, river, lake, and boundary layers. Run topo to raster and adjust display to visualize the dem.

Explore supervised and unsupervised image classification in ArcGIS Desktop, using training areas, spectral signatures, and imagery to produce land cover maps and assess accuracy.

Explore unsupervised classification using a cluster-based algorithm in ArcGIS spatial analysis, applying four-class and twelve-class results to satellite imagery with different band combinations and raster properties.

Define three classes: forest, beach, and sea, and apply interactive supervised classification using training samples and the maximum likelihood method to preview and refine results before final classification.

Build and merge training samples for forest, vegetation, urban, and barren lands; use maximum likelihood classification with a signature file to produce classified and confidence rasters.