ARC 3 - Pro Analyses

Basic GIS & Remote Sensing Terms Referenced in this Tutorial

• Geoprocessing:

  • Definition: The set of operations and tools used to manipulate and analyze spatial data in a GIS environment, including tasks such as buffering, clipping, and spatial joins.

• Spatial Analysis:

  • Definition: The process of analyzing spatial data to identify patterns, trends, relationships, and anomalies, often involving operations such as buffering, overlay, and proximity analysis.

• Geospatial Data:

  • Definition: Data that represents geographic features, phenomena, or attributes associated with specific locations on the Earth's surface, commonly used for mapping, analysis, and decision-making in GIS and remote sensing applications.

• Attribute Table:

  • Definition: A tabular data structure associated with a spatial dataset, containing descriptive information or attributes about the features represented in the dataset, often used for storing and analyzing non-spatial data.

• Raster Data:

  • Definition: Data that is represented as a grid of cells or pixels, with each cell containing a single value representing a specific attribute or phenomenon, commonly used for continuous spatial data such as elevation or satellite imagery.

• Vector Data:

  • Definition: Data that is represented by discrete geometric objects such as points, lines, and polygons, used to represent features with precise locations and shapes, commonly used for representing geographic features such as roads, rivers, and boundaries.

• Spatial Join:

  • Definition: A GIS operation that combines attribute data from two or more spatial datasets based on their spatial relationships, such as containment or proximity, resulting in a new dataset that merges the attribute information from the input datasets.

• Overlay Analysis:

  • Definition: A spatial analysis technique used to combine or overlay multiple spatial datasets to identify spatial relationships, intersections, or overlaps between features, often resulting in a new dataset that combines the attributes of the input datasets.

• Buffer Analysis:

  • Definition: A spatial analysis technique used to create a zone or buffer around spatial features based on a specified distance or criteria, commonly used for analyzing proximity and spatial relationships.

• Zonal Statistics:

  • Definition: A spatial analysis technique used to calculate summary statistics (e.g., mean, median, sum) for raster data within predefined zones or regions defined by vector polygons, commonly used for analyzing spatial patterns and trends.

• Interpolation:

  • Definition: A spatial analysis technique used to estimate values for locations where data is not available or sparse, based on known values from nearby locations, commonly used for creating continuous surfaces from point data such as elevation or temperature measurements.

• Classification:

  • Definition: The process of categorizing pixels or features within remote sensing imagery into discrete classes or categories based on their spectral characteristics, spatial patterns, or attributes, commonly used for land cover mapping and change detection.

• Data Visualization:

  • Definition: The graphical representation of spatial data using maps, charts, graphs, or other visual aids to communicate information, patterns, and insights, commonly used for data exploration, analysis, and presentation.

• Remote Sensing:

  • Definition: The science and technology of acquiring information about objects or areas on the Earth's surface from a distance, typically using sensors mounted on satellites, aircraft, or drones, commonly used for monitoring and mapping land cover, land use, and environmental changes.

• Spatial Autocorrelation:

  • Definition: A statistical technique used to measure the degree of spatial dependency or correlation between observations in a spatial dataset, commonly used for detecting spatial patterns and clustering in spatial data.

• Geospatial Analysis:

  • Definition: The process of analyzing and interpreting geographic data to derive insights, patterns, and relationships, commonly used for solving spatial problems, making decisions, and supporting planning and management activities.

General Tools

• Access ArcGIS Online Resources

• Access Living Atlas Resources

Use geoprocessing tools - Identify possible human contact zones for an invasive species that threatens New Zealand ecosystems.

• Buffer, Change Symbology, view Attribute Table, Select by Attributes, Select by Location (Intersect), Summarize Within, Create Bar Chart, View Geoprocessing History

Make a geoprocessing model - Build a visual diagram to connect and run geoprocessing tools.

• Use Model Builder to make a geoprocessing model to automate a process

Investigate pollution patterns with space-time analysis - Find regions of the world where pollution patterns are extreme or unusual.

• Multidimensional data (multiple times, depths, or heights), mosaic, attribute table contains list of mosaic images, applying a stretch to a raster (percent clip here), multidimensional raster layer, create a multidimensional raster from a multidimensional mosaic, space-time cube, temporal chart (we have done this in GEE), copy/paste layer to new map, Emerging Hot Spot Analysis tool (finds statistically significant trends in space-time data - uses space/time cube), Visualize Space Time Cube in 3D, clipping a 3D layer to a country boundary

Examine racial inequities in unsolved murder cases - Identify where unsolved homicides are most prevalent and which segments of the population are most impacted.

• create bar chart,  Mean Center tool (identifies the geographic center of a cluster of points and creates summary statistics for each cluster), filter by selecting bar in bar chart, filter by using an expression, Colocation Analysis tool, Enrich Data, Calculate Field, Summary Statistics 

Identify popular places with spatiotemporal data science - Detect clusters of spatial and temporal data to determine the best place to start a business.

• aggregate the check-ins, detect spatial and temporal clusters, create a space time cube, and analyze emerging hot spots, combine your results to determine the ideal location to open your business, tessalation, global Moran's I, Intersection,  Density-based Clustering tool (you used 3 diff methods), Convert Time Field tool, Create Space Time Cube By Aggregating Points, Time Series Clustering tool, Emerging Hot Spot Analysis (EHSA) tool