eeos 381 -spatial databases and gis...

Lecture 7

Spatial Analysis

EEOS 381 - Spatial Databases and

GIS Applications

EEOS 381 - Spring 2015: Lecture 7 2

What is Spatial Analysis?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


Some Common TasksSome 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


Some Common StepsSome 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


Things to NoteThings 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


Uses of Spatial AnalysisUses 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)


Inductive Spatial AnalysisInductive 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


Types of Spatial AnalysisTypes 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


Queries & ReasoningQueries & Reasoning

Most basic of analysis operations

To answer simple questions

No changes occur in database

No new data created



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



Reasoning:–Taking the results of queries and

applying meaning to them - e.g., navigational directions


MeasurementsMeasurements

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 387-388 in textbook



Slope



Alternative definitions of slope

The angle between the surface and the horizontal, range 0 to 90

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



Aspect



http://www.small-farm-permaculture-and-sustainable-living.com/permaculture_slope_and_aspect.html

Also see http://prykea.files.wordpress.com/2010/05/tanzania_ dem.jpg

uhttp://www.rockware.com


TransformationsTransformations

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 13-16 textbook

The basisfor many

applications


Analysis tools for vector datain 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 3rd party extensions are available

with some of these tools).


Topological Overlay TechniquesTopological 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


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



After UNION operation

• Display and analysis based on the “sum” field

• Weights can be changed to try different “what-if”scenarios




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



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 ErdasImagine, IDRISI, GRASS, among other software



Raster analysis tools in Spatial Analyst– Turn on in Customize > Extensions



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 isolinesbetween the data points

– Methods of spatial interpolation are designed to solve this problem• Thiessen polygons, Inverse-distance weighting

(IDW), Kriging (see pages 333-337 in textbook)



Comparison of spatial interpolation methods:



Comparison of spatial interpolation methods:

ThiessenPolygons

Inverse Distance Weighting

Kriging


Descriptive SummariesDescriptive 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


Descriptive SummariesDescriptive Summaries

The centroid or mean center replicates the balance-

point 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


Data MiningData 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:• http://www.innovativegis.com/basis/mapanalysis/topic28/topic28.htm


Data MiningData MiningFlow chart outlining GIS-based and Data Mining meth odology used to develop Slide Category Landslide Susceptibility Zoning Maps for t he Wollongong City Council Area.

http://eis.uow.edu.au/landslide/scope/UOW049837.html


OptimizationOptimization

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


Optimization - ExampleOptimization - 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


Optimization -ExampleOptimization -Example

Using ModelBuilder to 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.Overlay of all

resources

Final resultFinal result

Constraints to development

Study Area

Net Developable Area


Hypothesis TestingHypothesis 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


Retrieval and ReclassificationRetrieval 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. 96-97 in

textbook)


Retrieval and ReclassificationRetrieval 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 0-2-4. In this classification, land-use areas conducive to use as an airport are assigned a value of 4.

http://www.esri.com/news/arcuser/0408/suitability.html


Connectivity FunctionsConnectivity 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”



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


Cell-based (Raster) Analysis

– Example of overlay analysis with 20+ datasets using Spatial Analyst ArcGIS extension




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



Closest Facility composite network analysis



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

3-D shape3-D shapeOblique ImageryOblique ImageryDraped Shaded ReliefDraped Shaded Relief



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)


Connectivity Functions – Line of SightConnectivity Functions – Line of Sight

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

Intervisibility can 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.

Intervisibility can be applied to: • human vision - aesthetics and property values • electromagnetic radiation - FM radios, cellular

telephones and personal communication networks


Connectivity Functions – ViewshedConnectivity 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.


This viewshed image, draped over a georectified1874 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, 1863.

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)

Connectivity Functions – ViewshedConnectivity Functions – Viewshed


More 3-D ExamplesMore 3-D Examples

A dense network of Wi-Fi infrastructure viewed above the built environment of Salt Lake City, Utah



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.



This pollutant, often nicknamed ‘urban smog’ is largely derived from vehicle emissions: red identifies higher levels, while blue represents lower levels

Visualizing Visualizing nitrogen oxide nitrogen oxide ((NOxNOx) ) pollution in pollution in the Virtual the Virtual London modelLondon model


GeneralizationGeneralization

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 92-95 in textbook



Why generalize?

–Faster processing

–Faster printing/exporting

–Reduced storage

–Simpler cartographic display



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



Other types:

– Simplification

– Smoothing

– Merging/Dissolving

– Aggregation

– Amalgamation

– Collapse

– Refinement

– Exaggeration

– Enhancement

– Displacement

– Selection/Omission

Generalization may be database or cartographic

ArcToolbox tools

eeos 381 -spatial databases and gis...

Documents