Chapter 11 Spatial Analysis Credit to Prof Michael Goodchild

n Methods for working with spatial data  to detect patterns, anomalies  to find answers to questions  to test or confirm theories (deductive reasoning)  to generate new theories and generalizations (inductive reasoning) n Methods for adding value to data  in doing scientific research  in trying to convince others What is spatial analysis?

n A collaboration between human and machine  the machine does things the human finds too tedious, difficult, complex to do by hand  the human directs, makes interpretations and inferences n Ranging from simple to complex  some methods are mathematically sophisticated e.g. statistical tests  other methods are visual, intuitive, simple e.g. making and examining maps

The Snow map n Cholera outbreak in Soho, 1854 n Dr John Snow and the pump n inference regarding the transmission mechanism for cholera n see n updating Snow Openshaw's map of childhood leukemia in N England

n Data types  Discrete objects (points, lines, areas)  Fields  spatially intensive, spatially extensive  nominal, ordinal, interval, ratio, cyclic variables n Application domains n Objectives Types of spatial analysis

n nominal e.g. vegetation class no implied order, no arithmetic operations no average "central" value is the commonest class (mode) n Ordinal e.g. ranking from best to worst implied order, but no arithmetic operations no average "central" value has half of cases above, half below (median) Data types

n Interval e.g. Fahrenheit temperature differences make sense arbitrary zero point "central" value is the mean n Ratio e.g. weight ratios make sense absolute zero point "central" value is the mean n Cyclic e.g. aspect be careful with arithmetic average of 1 and 359 is 180

n Queries and reasoning n Measurements n Transformations n Descriptive summaries n Optimization n Hypothesis testing Six distinct objectives

n In ArcMap n map view n table view n linked views n histogram view n scatterplot view QUERIES

n Exploratory spatial data analysis n interactive methods to explore spatial data n use of linked views n finding anomalies n mining large masses of data n SQL n structured or standard query language n e.g. SELECT FROM counties WHERE median value > 100,000

n We spend our lives in the vague world of human discourse  "is Santa Barbara north of LA?" a GIS needs to know exactly what is meant by "north of"  is Reno east or west of San Diego? we tend to think of the US as a square, with two N-S coasts  how to design a GIS to provide driving directions? to direct people through airports? REASONING WITH GIS

 a GIS would be easier to use if could "think" and "talk" more like humans or if there could be smooth transitions between our vague world and its precise world  in our vague world, terms like "north of" are context-specific geographically relevant terms like "across" or "in" have many meanings

n Measurements are often difficult to make by hand from maps MEASUREMENT WITH GIS

n Buffering n Point in polygon n Polygon overlay n Spatial interpolation n Density estimation TRANSFORMATIONS