Research in Spatial Science for
Business
James B. Pick, Univ. of Redlands
[email protected]
August 6, 2011
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.
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)
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.
Components of BI
SDSS - Structure
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).
Conceptual Model of SDSS from Jaripathirun
and Zahedi
(Source: Jarupathirun and Zahedi, 2005)
Spatial Analysis – Map Overlay
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.
Spatial Analysis
Modeling of
Industrial
Locations for
Los Angeles
Using Location
Quotient
Source: Greene and Pick, 2006
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.
Spatial auto-correlation can measure the regression residuals in
predicting internet users in China by Moran’s I
Moran’s I = -0.111
not signif at 0.05 level.
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
GIS
• Challenge – can IS theories, concepts, and
methods be enlarged to apply to GIS??
Examples
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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
GIS industry trends – fast moving
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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?
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
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…………………..