Research in Spatial Science for Business

1. Research in Spatial Science for Business James B. Pick, Univ. of Redlands james_pick@redlands.edu August 6, 2011

2. The spatial edge • DSS and BI supports better decision making. • Having spatial information marginally increases the accuracy of BI. • It does this by taking spatial relationships into account beyond ordinary BI modeling.

3. Baseball example of non-spatial versus spatial query. If we associate the X-Y coordinate location of the stadium that the player is associated with, we are adding a spatial attribute. • Example of query without the spatial attribute. “Find all players with a batting average over .300.” • Example of query with the spatial attribute. “Find all players with a batting average over .250 and whose location is within 300 miles of Detroit Stadium.” (Source: Keith Clarke, 2008)

4. How is spatial BI accomplished • Ordinary BI uses modeling and data management components to respond to unstructured problems. • Spatial BI adds to these components spatial components that yield greater accuracy and the capability to perform spatial analysis.

5. Components of BI

6. SDSS - Structure

7. Spatial Analysis • GIS analysis techniques consist of methods that are used in the spatial analysis, modeling, and statistical analysis of a GIS. • Spatial analysis consists of analytical techniques that emphasize the digital boundary layers. • It relates and compares the features of the physical locations of objects in space (Getis, 1999; Longley and Batty, 2003). Since it draws principally from geography, it is not familiar to most IS researchers. • Modeling and statistical analysis methods include many methods and techniques well known in business disciplines, but often modified to take into account spatial relationships. These methods are based both on attribute and spatial data, • Statistical methods that include space are referred to as spatial statistics or geostatistics(Getis, 1999, 2011).

8. Conceptual Model of SDSS from Jaripathirun and Zahedi (Source: Jarupathirun and Zahedi, 2005)

9. Spatial Analysis – Map Overlay

10. Spatial Analysis – Location Quotient If a location quotient for an area is more than 100, the area is considered specialized in that activity. In the GIS exercise, the subareas are ZIP codes, whereas the larger area is the city of Detroit, and we compute the location quotient (LQ) in the following manner: where Eij is establishments in subarea j in sector i; Ej is total establishments in subarea j; Ei is city establishments in sector i; and Et is total city establishments.

11. Spatial Analysis Modeling of Industrial Locations for Los Angeles Using Location Quotient Source: Greene and Pick, 2006

12. Spatial Statistics • Spatial autocorrelation. • Measures how much like values cluster together geographically. Tobler’s first law – like values tend to group together. • Can be measured by Moran’s I statistic (Longley, Goodchild, Maguire, and Rhind, 2011; ESRI, 2011). • Moran’s I is an inferential test, with the null hypothesis being that the values of a variable are randomly distributed spatially. • It is interpreted by both its p value for statistical significance (in this case p = 0.05 or less is the cutoff), as well as by the z-score. • If the z-score is positive, the values of the variables are more clustered (high value tends to be located near high values and low value near low values) than expected randomly; • if the z-score is negative, the spatial pattern is more dispersed, i.e. high values are more separated than randomly distributed from neighboring high values and low values are more separated from neighboring low values than in a random pattern (Longley, Goodchild, Maguire, and Rhind, 2011; ESRI 2011). • A low absolute value for Moran’s I indicates that spatial autocorrelation is not present in a dependent variable.

13. Spatial auto-correlation can measure the regression residuals in predicting internet users in China by Moran’s IMoran’s I = -0.111not signif at 0.05 level.

14. Methods of spatial statistics • Spatial autocorrelation • Spatial regression • Geographically weighted regression (GWR) • Spatial econometrics • Nonparametric spatial point patterns • Spatial interactions • Gravity models • Dynamic spatial models including time series • Spatial simulation and modeling • Cellular automata modeling. • Fixed grid cells. Cells change states based on neighbors, over time. • Agent-based spatial modeling. Agents have purposeful behavior and are objective-seeking • Spatial interpolation • Kriging

15. GIS • Challenge – can IS theories, concepts, and methods be enlarged to apply to GIS?? Examples • Systems analysis and design theory, methodologies, and processes • Business process management • Outsourcing theories and findings • Virtual communities • Value of IT investments • Data-base theory and processes (e.g. how does an Esri geo-database fit into standard relational or object-oriented IS theory?) • Business intelligence/decision support systems • MIS organizational theory • Workforce • IS ethics

16. GIS industry trends – fast moving • Web-, mobile-, server-based spatial information systems • Collaborative GIS • Cloud-based spatial paradigms • Massive spatial data management • Vastly enlarged public spatial resources • GeoDesign • Space-time • Crowdsourcing • Widespread sensor input • Movement to enhanced spatial analytics Challenge – how can these cutting-edge developments be (1) understood, and (2) utilized by IS researchers?

17. Collaborative suggestions for research • Greater communication and collaboration of ongoing research projects within the MIS community (SIGGIS as a change factor) • Collaboration of IS and geographical researchers. • Geographers know more about location, space, and GIScience, but less (much less) about IS/IT and business/management. • Collaboration of IS researchers with industry • Although much is proprietary about business GIS, some firms are motivated to share, especially if trust can be established. More likely if the firm has been successful in GIS/spatial applications • International collaboration in research

18. The Challenge – GIS/spatial research needs to be more a part of MIS scholarship. It goes two ways, not only will GIS researchers benefit, but MIS field will have a stronger base…………………..