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Spatial Analytics

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What Is Spatial Analysis?

Spatial analysis allows companies to analyze the locations, relationships, attributes, and proximities in geospatial data to extract insights using geographical modeling. In the past, spatial analysis was conducted solely with Geographic Information Systems (GIS) framework, which is a software framework specializing in connecting location data with descriptive information and displaying that data on a map. Today, the process leverages the capabilities of data science and machine learning, too.

Why Is Spatial Analysis Important?

Spatial analysis enables companies to combine geographic and descriptive data from a variety of sources and use that data to drive geographic models and visualizations. Through data visualization, companies can see spatial data points clearly to understand where and why events happened to determine the suitability of a location for business purposes, interpret and understand change, detect patterns, and predict outcomes.

Spatial Analysis Process

Spatial analysis follows a series of well-defined steps including:


Exploration: This is done by working with an existing spatial data file or through geocoding, where values such as postcodes and latitude/longitude points are translated into specific geographic coordinates and projections.
Exploration


Modeling: Employing descriptive analytics to calculate both values and shapes, such as an isochrone mapping, which is used to visualize distances and travel times between points. It’s also possible to encode geographical entities such as lines and polygons to build 2D and 3D models of real-world objects.
Modeling


Comparison: Processing various spatial shapes together makes it easier to calculate areas of overlap or boundary and therefore generate new spatial information in the form of calculated points, lines, or polygons.
Comparison


Prediction: By reviewing how spatial analytics changes over time, analysts can detect patterns and present interactive maps with forecasted data.
Prediction

Types of Spatial Analysis

Spatial Data Analysis: Data is collected, processed, and augmented to generate value according to location-based attributes, properties, or relationships. This allows access to details, such as location, position, and distance, that would otherwise be difficult to obtain.
Spatial Data Analysis


Spatial Autocorrelation: Testing determines whether data points that are closely co-located are also similar when it comes to other attributes. For example, spatial autocorrelation can investigate whether a disease is isolated or present in clusters around an area.
Spatial Autocorrelation


Spatial Stratified Heterogeneity: The uneven distribution of features in a spatial region is measured to determine how patchy/heterogeneous a series of layers/strata is within the defined boundaries. Commonly used to determine coverage within a series of geospatial polygon zones as part of a larger descriptive analysis.
Spatial Heterogeneity


Spatial Interpolation: Location-based data points with known attributes are used to estimate the values at other unknown points. This type of interpolation is commonly used to estimate temperatures between weather station locations to create an interpolated statistical “surface” across the region of interest.
Spatial Interpolation


Spatial Regression: Models are built that consider spatial characteristics alongside traditional numeric features to infer numeric results such as salaries and birth rates.
Spatial Regression


Spatial Interaction: Insights are drawn from the interaction of different entities including points, lines, and polygons. For example, boundaries may touch, areas may overlap, or a spatial object can be completely contained by another.
Spatial Interaction


Simulation and Modeling: An analysis and understanding of geospatial objects and their properties offers a measurement of their changes over periods of time following the experimental conditions.
Simulation and Modeling


Multiple-Point Geostatistics (MPS): A collection of algorithms that simulates spatial structures and patterns based on a statistical model. MPS often focuses on describing geospatial structures through probability distributions and is used for subsurface reservoir models.
Muliple Point Geostatistics

Getting Started With Spatial Analysis

Traditional spatial analysis required extensive knowledge in GIS platforms such as Esri or QGIS, but Analytic Process Automation (APA) makes it accessible to analysts and data scientists alike. The Alteryx APA Platform™ offers a unified process to transform raw data into powerful geospatial insights with:
  • A wide range of geospatial data formats such as GeoJSON
  • Visual, no-code tools to transform, geocode, process, and analyze spatial and hundreds of other data sources
  • Tight integration of spatial reference data, including regular updates to geospatial datasets such as geocoding and drivetime analysis thanks to partnerships with TomTom, MapBox, and other vendors

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