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The Periodic Table for Spatial Analysis

Spatial Analysis Periodic Table

Introducing: The Periodic Table for Spatial Analysis

Each element in the Periodic Table for Spatial Analysis contains a set of spatial analysis tools. We’ve grouped common tools by color with vector analysis tools on the left and raster analysis on the right.

Vector Analysis/Conversion

Spatial Join

1. Vector Conversion [VC] – Converts file formats for points, lines, and polygons and alters data models from vector to raster or vice versa. (Feature to raster, rasterization vs vectorization)
2. Extract [EX] – Creates a subset of features by clipping, selecting, and splitting vector features. (Clip, select, and split)
3. Overlay [OV] – Overlays 2 or more vector layers and outputs layers based on overlapping features. (Intersect, union, erase tool)
4. Proximity [PX] – Generates output based on distances or proximity functions. (Buffer, Voronoi diagrams, and near functions)
5. Spatial Join [SJ] – Joins attributes from a separate layer based on distance or spatial relationship. (1-M Spatial join “Contains”, 1-1 Touches)
6. Plot Diagrams [PD] – Builds a graph or diagram based on a set of attributes and geographical locations. (Scatter plot, histograms, bar plots)
7. Geometry Shape [GS] – Computes the geometric shape of an object. (Compactness, perimeter/area ratio, rectangular fit).


Locations Map

8. Table Tools [TB] – Performs table functions for storing attribute data (Add field, create domains, reorder fields)
9. Add XY Coordinates [XY] – Converts a table of XY coordinates (latitude/longitude) into a layer with a defined coordinate system. (Add lat/long coordinates)
10. Calculate Geometry [CG] – Computes the length of the geometric measurements in the attribute table of vector features. (Calculate length/area)
11. Join Table [JT] – Appends the attribute columns from one table into another table based on matching record keys. (1:1, 1:M, M:N)
12. Relate [RL] – Generates a temporary table that displays matching records that associate with one or more matching records. (Relate vs Join)
13. Statistics [ST] – Calculates statistics based on a numerical field in a table. (RMSE, MAE, sum, mean, count, standard deviation).


Editing Generalize

14. Editing [ED] – Performs an editing function using the vertices and geometry in one or more layers. (Editing tools, densify, trim, snap, and extend)
15. Conflation [CF] – Resolves conflicts between two layers that display the same features with mismatching geometries. (Edge-matching, rubber sheeting, conflation)
16. Grid Index [GI] – Produces a set of consecutive rectangular map sheets that follow a linear feature for mapbook production. (Strip Map, fishnet, tessellation, QGIS Atlas, data driven pages)
17. Cartographic [CA] – Enhances or generalizes features in a dataset for cartographic display and aesthetic quality. (Smooth, simplify, aggregate).

3D Analysis

Intersect 3D

18. 3D Analysis [3D] – Performs an overlay or proximity analysis with 3D features. (3D analysis buffer, intersect, or union)
19. Line of Sight Visibility [LS] – Identifies obstruction and non-obstruction sections of a straight line from an observer. (Line of sight)
20. Volume [VS] – Calculates the amount of space above, below, within, or for the purpose of removing or adding material. (Cut/fill)
21. Viewshed [VW] – Determines locations visible to an observer in all directions with the output as a visibility raster.
22. Skyline [SL] – Displays visible and obstructed shadowed areas similar to a 3D fan pointing from an observer’s point of view.
23. Space-time Cubes [SC] – Builds temporal and 3D cubes representing slices of time in a geographic area. (Space-time cubes)

Network Analysis

24. Route [RT] – Finds the optimal route using a set of points and a network dataset. (Network analysis fastest route, find nearest, or shortest distance)
25. Directions [DR] – Lists the turns, streets, and directions from an origin to a destination point using a network dataset.
26. Optimal Site [OS] – Selects optimal sites from existing facilities, competing stores, and available demand. (Location-allocation)
27. Coverage [CV] – Computes the coverage or accessibility that a facility can be reached for a given distance, time, and network dataset. (Service area)
28. OD Cost Matrix [CM] – Measures the least cost path from multiple origin points to multiple destination points.
29. Huff Model [HM] – Predicts the probability that consumers will patron retail stores using store size, distance, and census tract population. (Huff gravity model)

Data Management

Merge Tool

30. Data Management [DM] – Manages layers with a set of tools to develop, alter, and maintain layers (Merge, append, data compare)
31. Projections [PJ] – Assigns a coordinate reference system for a layer. (Project, define projection)
32. Generalize Vector [GV] – Combines adjacent features or slivers based on common attribute values or shared borders. (Dissolve and eliminate)
33. Address Geocoding (AD) – Translates addresses into geographic locations with latitude and longitude coordinates. (Geocode, reverse geocode)
34. Topology [TP] – Fixes and catches editing errors such as overshoots, undershoots, slivers, overlaps, and gaps. (Topology rules)
35. Linear Referencing [LR] – Stores relative positions on a line feature represented by m-values for point/line events. (Linear referencing systems)
36. Spatial Adjustment [SA] – Aligns and transforms a vector layer that has been displaced, rotated, or distorted like georeferencing for vectors. (Vector bender, displacement links)
37. GeoEnrich [GE] – Ameliorates existing data with value-added information such as demographic, education, or income attributes. (GeoEnrichment)
38. Sampling [SP] – Creates a subset of data for sampling at set intervals or randomly. (Regular points, random points in extent)
39. Geotagging [GT] – Assigns geographic coordinates to digital photos through GPS without georeferencing. (Geotagging)
40. Parcel Fabric [PF] – Constructs cadastral specific to managing parcel fabric. (Cadastral divisions, split polygon)
41. Attachments [AT] – Builds attachments to store photos internally as a table relationship.
42. Full Motion Video [FMV] – Geo-enables video with the footprints coordinated on a map. (Full motion video)
43. COGO [CO] – Captures coordinates, bearings, and distances from transverse land survey measurements. (COGO – Coordinate Geometry)
44. Point Cloud [PC] – Manages LAS files with a set of tools to maintain, alter, and interpolate point clouds.
45. Web Service [WS] – Deploys or imports features from a layer as a web feature/mapping service. (Web feature service, GeoRSS)
46. TIN [TIN] – Creates a triangular irregular network for depicting three-dimensional terrain surfaces. (TIN mesh creation)
47. Indoor Mapping [IM] – Incorporates indoor floor plans with digital formats like BIM, Revit, and CAD. (Indoor mapping)
48. Temporal [TM] – Adds time properties to layers with the date and/or time (Convert time zone, update time field, temporal animation)
49. Real-time Tracking [TR] – Streams real-time movement of objects or change in status over time. (GeoEvent server, geofencing, make tracking layer)

Emerging Technology

Artificial Intelligence

50. Big Data [BD] – Analyzes and extracts data from datasets too large and complex with geographic locations. (GeoAnalytics Desktop Tools)
51. Machine Learning [ML] – Uses neural networks for classification, prediction, and segmentation through training and labeling. (Deep learning toolset, machine learning)
52. Data Engineering [DE] – Validates, cleans, and maintains spatial data into a usable form for analysis.
53. IoT [IOT] – Analyzes real-time data feeds and sensors from the Internet of Things (IoT) platform. (ArcGIS Velocity)
54. Agent-based Simulation and Modeling [AS] – Simulate scenarios and the emergence of phenomena through individual interactions in geographic space. (Multi-agent modeling environment)
55. Virtual Reality [VR] – Replaces the field of vision through headsets in a spatial environment.
56. Augmented Reality [AR] – Enhances 3D features on your phone’s display to interact spatially with the outside world. (Augmented reality)

Raster Data Management

Mosaic Raster Tool

57. Georeferencing [GR] – Stretches, scales, rotates, and skews raster images to better relate to geographic space. (Georeferencing)
58. Mosaic [MO] – Combines multiple raster images into a seamless, composite raster image. (Mosaic)
59. Raster Creation [RC] – Generates a raster for a given extent at a specific cell size. (Create a random raster, create a constant raster)
60. Spatial Autocorrelation [AL] – Measures how dispersed or clustered cells are located in a raster. (Moran’s I)
61. Generalization [RG] – Cleans raster data by generalizing, smoothing, and altering cells. (Nibble, shrink, expand)
62. Multidimensional [MD] – Provides an interface for array-oriented data for storing multidimensional variables. (NetCDF)
63. Resample [RS] – Updates the cell size for converting raster images. (Raster resample: Nearest neighbor, bilinear, and cubic convolution)
64. Raster Painting [PA] – Draws and erases raster cells with a set of brush, fill, and erase tools.

Raster Analysis

Least Cost Path Analysis

65. Raster Analysis [RA] – Performs raster analysis functions for a raster grid dataset. (Analyze patterns)
66. Map Algebra [MA] – Applies math-like operations in local, zonal, focal, and global configurations. (Map algebra)
67. Contours [CN] – Produces lines of constant elevation to represent the topography of the landscape. (Contours)
68. Zonal Statistics [ZS] – Generates statistics for defined zones of a raster surface. (Zonal statistics – mean, sum, and majority)
69. Cost Path [CP] – Finds the most cost-effective path, from a start point to a destination, which accumulates the least amount of cost. (Least cost path)
70. Raster Processing [RP] – Creates a subset of features by clipping, selecting, and splitting raster grids. (Raster clip, split raster)
71. Spatial Regression [RE] – Generates a prediction surface based on explanatory variables. (Ordinary least squares regression, spatial regression)
72. Terrain Analysis [TA] – Calculates the characteristics of the terrain from an input raster. (Slope, morphometry, TPI, and roughness)
73. Math Function [MF] – Executes a math function to update the numerical value on a cell-by-cell basis. (Arithmetic, power, exponential, and logarithmic)
74. Suitability [SU] – Overlays raster surfaces based on criteria to analyze suitability. (Fuzzy logic weighted sum)
75. Conditional [CON] – Performs a conditional statement on a raster that generates a binary output. (Greater than, equal to)

Remote Sensing

Photogrammetry Point Cloud

76. Band Index [BA] – Converts a set of imagery bands by leveraging the inherent wavelength properties. (NDVI, tasseled cap, wetness index)
77. Image Stretching [IS] – Arranges the display of an image by adjusting brightness, contrast, and gamma properties.
78. Image Classification [IC] – Assigns land cover classes to imagery pixels based on their spectral properties. (Supervised/unsupervised classification)
79. Composite Bands [CB] – Combines single-band rasters into a composite raster for true color or false color display. (Composite bands)
80. Pansharpening [PS] – Enhances spatial cell resolution by leveraging the panchromatic band.
81. Atmosphere Correction [AC] – Corrects remote sensing imagery through scattering inherent in the atmosphere. (Dark object subtraction, radiative transfer models, atmosphere correction)
82. Segmentation [SG] – Grouping similar pixels from an image into vector objects to recognize objects and features. (Segment mean shift, object-based image analysis)
83. Data Mining [DN] – Eliminates redundant data from variables that are highly correlated, aggregating essential information. (Principal component analysis)
84. Mensuration [ME] – Measures the geometry of two and three-dimensional features in an image. (Angles, height, perimeter, volume)
85. Photogrammetry [PH] – Performs stereographic parallax from two or more vantage points of the same object to measure relief displacement. (Photogrammetry)
86. Oblique [OB] – Collects images at an angle as opposed to a top-down orthographic perspective.
87. Radar [RD] – Measures the backscatter of a sent microwave pulses to Earth whether it’s specular, diffuse, or double-bounce reflection. (Synthetic aperture radar)


IDW Power 2

88. Interpolation [IP] – Estimates unknown values using sampled locations by creating a prediction surface. (IDW, spline, trend)
89. Kriging [KR] – Generates a probability and prediction surface by building a mathematical function through a semi-variogram. (Kriging and semi-variograms, and geostatistics)
90. Kernel Density [KD] – Calculates hot and cold spots by applying a density-per-unit function (Heat map)

Conclusion: The Periodic Table for Spatial Analysis

Spatial analysis may seem like alchemy to the inexperienced. But there is a science to it. By using spatial analysis, we can find patterns, quantify area, and predict outcomes with geography being the common link of it all.

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  1. I appreciate your effort which shows how deep you have been gone in spatial science.
    Thanks for sharing us!

  2. question <- "is spatial statistics element of spatial analysis?"
    if (question == TRUE){
    return("the table is missing the statistical component. Can it be added?"
    } else {
    return("Excellent table. Great Job")

  3. Thanks for creating this visual depiction of basic and traditional routines for spatial data processing and analysis. It’s a great tool to help illustrate and explain the various academic disciplines that come together to form a well-rounded professional in this field.

  4. Very clever – thank you all! I will tell others about this and use it in my own courses and in teaching.

    –Joseph Kerski

  5. This is a great composition of the ideals of my beloved field. I am a proud professional geographer whose tools are enshrined in computational geography – GIS, Remote Sensing, and Spatial Analysis. I will ensure that I commit this to memory to teach my students and mentees.

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