EEOS Spatial Databases and GIS Applications

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1 EEOS Spatial Databases and GIS Applications Lecture 7 Spatial Analysis

2 What is Spatial Analysis? The crux of GIS, Real GIS Methods of turning spatial data into usable information Answers questions Reveals patterns and anomalies in data and relationships between features EEOS Spring 2015: Lecture 7 2

3 Some Common Tasks Mapping where things are Mapping the most and least Mapping density Finding what s inside Finding what s nearby Mapping change Answer what if? scenarios EEOS Spring 2015: Lecture 7 3

4 Some Common Steps Frame the question - what do you need to know? Understand your data - what data do you have, what do you need? Choose method(s) - often more than one Process the data - use the tools (ArcToolbox) Review results - map, table, chart - and determine if your question is answered EEOS Spring 2015: Lecture 7 4

5 Things to Note Effective spatial analysis requires an intelligent user, not just a powerful computer Knowledge of the data and the science behind the application A method of analysis is spatial if the results depend on the locations of the objects being analyzed The methods may stay the same, but move the objects (change their location) and the results may change EEOS Spring 2015: Lecture 7 5

6 Uses of Spatial Analysis Inductive Examine empirical evidence in search of patterns that might support new theories or general principles (e.g., cause and effect ); based on observation Deductive Testing known theories or principles against data Normative Develop or prescribe new or better designs (e.g. siting) EEOS Spring 2015: Lecture 7 6

7 Inductive Spatial Analysis A redrafting of the map made by Dr. John Snow in 1854, showing the deaths that occurred in an outbreak of cholera in the Soho district of London EEOS Spring 2015: Lecture 7 7

8 Types of Spatial Analysis Queries and reasoning Measurements Transformations Descriptive summaries Data Mining Optimization Hypothesis testing Retrieval and Reclassification Topological Overlay Techniques Neighborhood Operations Connectivity Functions EEOS Spring 2015: Lecture 7 8

9 Queries & Reasoning Most basic of analysis operations To answer simple questions No changes occur in database No new data created EEOS Spring 2015: Lecture 7 9

10 Queries & Reasoning Examples: How many people live within 1 mile of the nuclear reactor? (proximity analysis) How many acres of land are zoned for residential use? Use GIS software query tools ( Select by attributes, Select by Location ) or other programs (e.g. SQL*Plus, Access) Often requires JOIN to other table(s) Example Census data EEOS Spring 2015: Lecture 7 10

11 Queries & Reasoning Reasoning: Taking the results of queries and applying meaning to them - e.g., navigational directions EEOS Spring 2015: Lecture 7 11

12 Queries & Reasoning EEOS Spring 2015: Lecture 7 12

13 Queries & Reasoning EEOS Spring 2015: Lecture 7 13

14 Measurements Numerical values that describe aspects of geographic data Many tasks require measurement from maps, which can be tedious and inaccurate if made by hand Examples: Length and Area (often underestimated in a GIS) Shape (e.g. to find sliver polygons ) Distance, Direction Slope (angle - rise over run ) and Aspect (direction of steepest tilt) See pages in textbook EEOS Spring 2015: Lecture 7 14

15 Measurements Slope EEOS Spring 2015: Lecture 7 15

16 Measurements Slope EEOS Spring 2015: Lecture 7 16

17 Measurements Slope EEOS Spring 2015: Lecture 7 17

18 Measurements Alternative definitions of slope The ratio of the change in elevation to the actual distance traveled, range 0 to 1 The ratio of the change in elevation to the horizontal distance traveled, range 0 to infinity The angle between the surface and the horizontal, range 0 to 90 EEOS Spring 2015: Lecture 7 18

19 Measurements Aspect EEOS Spring 2015: Lecture 7 19

20 Measurements EEOS Spring 2015: Lecture 7 20

com/permaculture_slope_and_aspect.html uhttp://www.rockware.

21 Measurements uhttp:// Also see EEOS Spring 2015: Lecture 7 21

22 Transformations Change datasets or combine them to create new data, in order to derive new insights and analyze relationships (for both raster and vector) Examples: Overlay analyses ( spatial joins ) Identity, union, intersect Clip, Erase, Update Neighborhood operations (e.g. Buffer) Connectivity Analysis proximity, network, 3D Conversion (e.g. raster to vector) Spatial Interpolation May create new fields in attribute tables See chapters textbook The basis for many applications EEOS Spring 2015: Lecture 7 22

23 Analysis tools for vector data in ArcToolbox An analysis often combines ( strings together ) more than one of these functions, creating new or temporary datasets that are used in subsequent steps. Model Builder and Python Command Window in ArcGIS can facilitate steps. ** Some tools available only with an Arc/Info license (free 3 rd party extensions are available with some of these tools). EEOS Spring 2015: Lecture 7 23

24 Topological Overlay Techniques Overlay multiple data layers in a vertical fashion The most required and common technique in geographic data processing Usually polygon-on-polygon, but also point in polygon, line in polygon Uses Boolean logic - AND, OR, XOR, NOT Useful in assigning weight scores for certain criteria Raster data uses arithmetic overlay operations - add, subtract, divide, multiply EEOS Spring 2015: Lecture 7 24

of all the layers are added and stored in a new field Display and

25 Topological Overlay Techniques Layer weighting and assigning scores Each layer is given a weight score (value) After overlay, the scores of all the layers are added and stored in a new field Display and analysis can then be based on the sum field EEOS Spring 2015: Lecture 7 25

26 Topological Overlay Techniques After UNION operation Display and analysis based on the sum field Weights can be changed to try different what-if scenarios EEOS Spring 2015: Lecture 7 26

27 Topological Overlay Techniques Overlay analyses may result in spurious or sliver polygons In any two such layers there will almost certainly be boundaries that are common to both layers e.g. following rivers But the two versions of such boundaries will not be coincident As a result large numbers of small sliver polygons will be created these must somehow be removed ( ELIMINATE ) this is normally done using a user-defined tolerance EEOS Spring 2015: Lecture 7 27

28 Topological Overlay Techniques Overlays with rasters ( map algebra ) A B The two input data sets are maps of (A) travel time from the urban area shown in black, and (B) county (red indicates County X, white indicates County Y). The output map identifies travel time to areas in County Y only, and might be used to compute average travel time to points in that county in a subsequent step. Use ArcGIS Spatial Analyst, or Erdas Imagine, IDRISI, GRASS, among other software EEOS Spring 2015: Lecture 7 28

29 Topological Overlay Techniques Raster analysis tools in Spatial Analyst Turn on in Customize > Extensions EEOS Spring 2015: Lecture 7 29

30 Transformations Spatial interpolation Values of a field have been measured at a number of sample points There is a need to estimate the complete field (i.e. fill in the blanks ) to estimate values at points where the field was not measured to create a contour map by drawing isolines between the data points Methods of spatial interpolation are designed to solve this problem Thiessen polygons, Inverse-distance weighting (IDW), Kriging (see pages in textbook) EEOS Spring 2015: Lecture 7 30

31 Transformations Comparison of spatial interpolation methods: EEOS Spring 2015: Lecture 7 31

32 Transformations Comparison of spatial interpolation methods: Thiessen Polygons Inverse Distance Weighting Kriging EEOS Spring 2015: Lecture 7 32

Descriptive Summaries Capture essence of a dataset in one or two numbers Spatial equivalent of descriptive statistics in statistical analysis

33 Descriptive Summaries Capture essence of a dataset in one or two numbers Spatial equivalent of descriptive statistics in statistical analysis Examples: centers (centroid), mean, standard deviation use histograms and charts Spatial Statistics Tools toolbox EEOS Spring 2015: Lecture 7 33

34 Descriptive Summaries The centroid or mean center replicates the balancepoint property in two dimensions the point about which the two-dimensional pattern would balance if it were transferred to a weightless, rigid plane and suspended EEOS Spring 2015: Lecture 7 34

35 Data Mining Analysis of massive data sets in search for patterns, anomalies, and trends spatial analysis applied on a large scale must be semi-automated because of data volumes widely used in practice, e.g. to detect unusual patterns in credit card use Example: EEOS Spring 2015: Lecture 7 35

36 Data Mining Flow chart outlining GIS-based and Data Mining methodology used to develop Slide Category Landslide Susceptibility Zoning Maps for the Wollongong City Council Area. EEOS Spring 2015: Lecture 7 36

37 Optimization Normative in nature - Designed to select ideal locations for objects or paths given certain criteria Example: find the best location for a new school, based on open land proximity to large number of children certain distance away from existing schools certain distance away from waste sites May involve other types of analysis EEOS Spring 2015: Lecture 7 37

Optimization - Example Suitability model to find the best location to construct a new school (using rasters)* Certain land uses are more conducive than others for building a new school for example,

38 Optimization - Example Suitability model to find the best location to construct a new school (using rasters)* Certain land uses are more conducive than others for building a new school for example, forest and agriculture were more favorable than residential housing in this model. It was desired to locate the school on flat slopes, near recreation sites, and far from existing schools. The input rasters were first identified and the derived rasters were created for example, the slope raster was created from elevation. The model inputs were reclassified, weighted, and combined using the Weighted Overlay tool. * From ArcGIS Help: Spatial Analyst > Overlay analysis sample applications EEOS Spring 2015: Lecture 7 38

Study Area Net Developable Area Constraints to development Final result Overlay of all resources Optimization - Example Using ModelBuilderto identify priority land for conservation This model uses

39 Study Area Net Developable Area Constraints to development Final result Overlay of all resources Optimization - Example Using ModelBuilderto identify priority land for conservation This model uses raster datasets and Spatial Analysis tools to reclassify, apply a weight score, and overlay natural resource layers with constraints to development to yield a final output layer. EEOS Spring 2015: Lecture 7 39

40 Hypothesis Testing Reasoning from the results of a limited sample to make generalizations about an entire population Must determine if sample is acceptable as a random and independent sample of the population EEOS Spring 2015: Lecture 7 40

41 Retrieval and Reclassification Often the initial analysis step Retrieval - selective search (using SQL), manipulation and output of data, without need to modify data Reclassification - involves looking at an attribute, or a series of attributes, for a single data layer and classifying the data layer based on the range of values of the attribute; for both vector and raster. data may be dissolved (internal boundaries removed) based on classes (groups) Choropleth mapping for display (pgs in textbook) EEOS Spring 2015: Lecture 7 41

Retrieval and Reclassification Land-use data is reclassified from a nominal classification, such as High Density Residential, Industrial, Cemetery, or Park, to the index classification of

42 Retrieval and Reclassification Land-use data is reclassified from a nominal classification, such as High Density Residential, Industrial, Cemetery, or Park, to the index classification of In this classification, land-use areas conducive to use as an airport are assigned a value of 4. EEOS Spring 2015: Lecture 7 42

43 Connectivity Functions Use functions that accumulate values over an area being traversed Proximity Analysis identify any feature that is near any other feature based on location, attribute value, or a specific distance. Examples: identifying all the forest stands that are within 100 meters of a road, but not necessarily adjacent to it Adjacency - within a distance of zero EEOS Spring 2015: Lecture 7 43

44 Connectivity Functions Cell-based (Raster) Analysis Use imagery Examples: Cost distance, Euclidian distance Hydrologic tools (flow direction, basin delineation, etc.) Overlay analyses Use Spatial Analyst ArcGIS extension EEOS Spring 2015: Lecture 7 44

45 Connectivity Functions Cell-based (Raster) Analysis Example of overlay analysis with 20+ datasets using Spatial Analyst ArcGIS extension EEOS Spring 2015: Lecture 7 45

Connectivity Functions Network Analysis Use linear features, perhaps with point events Examples: find shortest path ( route optimization ) for transportation networks

46 Connectivity Functions Network Analysis Use linear features, perhaps with point events Examples: find shortest path ( route optimization ) for transportation networks model flow along hydrographic hierarchy - define rate, consider impedance and cost Use Network Analyst or Spatial Analyst ArcGIS extensions EEOS Spring 2015: Lecture 7 46

47 Connectivity Functions Closest Facility composite network analysis EEOS Spring 2015: Lecture 7 47

48 Connectivity Functions 3D Analysis Use of: cross-sections (profiles); slope draped images and surfaces oblique imagery; TINs; 3D shapes (multi-patch) 3D Analyst extension in ArcGIS; ArcGlobe; ArcScene Draped Shaded Relief Oblique Imagery 3-D shape EEOS Spring 2015: Lecture 7 48

49 Connectivity Functions 3D Analysis Example: Generation of perspective surfaces to model: line-of-sight (calculates intervisibility between pairs of points given their position in 3D space and a surface. It also determines what is visible along the lines between these points since they are profiled on the surface.) and viewshed (identifies the cells in an input raster that can be seen from one or more observation points or line, i.e. area see from a given vantage point) EEOS Spring 2015: Lecture 7 49

Connectivity Functions Line of Sight Useful for planning locations of unsightly facilities such as smokestacks, or surveillance facilities such as fire towers.

50 Connectivity Functions Line of Sight Useful for planning locations of unsightly facilities such as smokestacks, or surveillance facilities such as fire towers. Intervisibilitycan be computed based on a set of rays radiating outwards from a vantage point; a surface can only obstruct a view by rising above the line of sight. Intervisibilitycan be applied to: human vision -aesthetics and property values electromagnetic radiation -FM radios, cellular telephones and personal communication networks EEOS Spring 2015: Lecture 7 50

51 Connectivity Functions Viewshed Viewshed indicates the entire area an individual can see from a given point, based on topography and obstructions. For example, being able to determine the viewshed and how it could be altered is of particular use to park planners and landscape architects. EEOS Spring 2015: Lecture 7 51

Connectivity Functions Viewshed This viewshed image, draped over a georectified 1874 map, estimates which parts of the Gettysburg battlefield

It suggests he saw much more than historians previously thought (tinted white), though little of the hotly contested ground around Little Round

52 Connectivity Functions Viewshed This viewshed image, draped over a georectified 1874 map, estimates which parts of the Gettysburg battlefield were probably visible from Lee s viewpoint high in the cupola of the Lutheran Seminary on July 2, It suggests he saw much more than historians previously thought (tinted white), though little of the hotly contested ground around Little Round Top (Image created by Caitrin Abshere, Charlie Wirene, and Anne Kelly Knowles; 1874 map courtesy the National Archives and Records Administration) EEOS Spring 2015: Lecture 7 52

53 More 3-D Examples A dense network of Wi-Fi infrastructure viewed above the built environment of Salt Lake City, Utah EEOS Spring 2015: Lecture 7 53

The height of the bars identifies rateable value a UK indicator of the

54 More 3-D Examples A 3-D representation of office and retail land use in London. The height of the bars identifies rateable value a UK indicator of the value of the property/real estate in the grid squares that is dedicated to office and retail functions. EEOS Spring 2015: Lecture 7 54

largely derived from vehicle emissions: red identifies higher

55 More 3-D Examples Visualizing nitrogen oxide (NOx) pollution in the Virtual London model This pollutant, often nicknamed urban smog is largely derived from vehicle emissions: red identifies higher levels, while blue represents lower levels EEOS Spring 2015: Lecture 7 55

56 Generalization Often part of an application, either before or after analysis Definition:Reducing the level of detail in geographic data By simplifying, weeding, abstracting To reduce the volume of data without adversely affecting its use For both vector and raster See pages in textbook EEOS Spring 2015: Lecture 7 56

57 Generalization EEOS Spring 2015: Lecture 7 57

58 Generalization EEOS Spring 2015: Lecture 7 58

59 Generalization Why generalize? Faster processing Faster printing/exporting Reduced storage Simpler cartographic display EEOS Spring 2015: Lecture 7 59

60 Generalization Example - Weeding The process of removing points in a polygon or polyline while preserving important aspects of shape (aka coordinate thinning ) The Douglas-Poiker algorithm A rigorous process that can be applied to any polygon or polyline Requires the specification of a tolerance parameter that defines the allowed deviations between the original feature and its generalized version EEOS Spring 2015: Lecture 7 60

Exaggeration Enhancement Displacement Selection/Omission ArcToolbox

61 Generalization Other types: Simplification Smoothing Merging/Dissolving Aggregation Amalgamation Collapse Refinement Exaggeration Enhancement Displacement Selection/Omission ArcToolbox tools Generalization may be database or cartographic EEOS Spring 2015: Lecture 7 61

GEOGRAPHY 350/550 Final Exam Fall 2005 NAME:

GEOGRAPHY 350/550 Final Exam Fall 2005 NAME: 1) A GIS data model using an array of cells to store spatial data is termed: a) Topology b) Vector c) Object d) Raster 2) Metadata a) Usually includes map projection, scale, data types and origin, resolution