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GIS i d S ti T lGIScience and Spatio-Temporal Analysis: An Overview of Recent
Advances
Michael F. GoodchildUniversity of California
Santa Barbara
What do we know?• …that we didn’t a few years ago• What do we need to know?
– and what are the chances of finding out?• Space
i– static• because of the economies of scale of map production
• Dynamics• Dynamics– remote sensing’s time slices– a flood of new data typesa flood of new data types
Time-critical data• Near real-time• Sensors embedded in the environment and
lon people– tracking movement
H ti i t lli t• Humans acting as intelligent sensors– densely distributed on the ground
observing uploading– observing, uploading• crowdsourcing or volunteered geographic information
Interoperability of geolocation34 d 24 i 42 7 N th 119 d 52 i 14 4• 34 deg 24 min 42.7 sec North, 119 deg 52 min 14.4 sec West (3m)
• 909 West Campus Lane Goleta CA 93117 USA909 West Campus Lane, Goleta, CA 93117, USA (20m)
• 3811560N, 236150E, Zone 11, Northern Hemisphere (10m)
• NE 1/4, Section 12, Township 23 Range 5 of the Second Principal Meridian (300m)Second Principal Meridian (300m)
• National Grid reference 11SKU36151156 (10m)• Visual identification on a georegistered base map g g p
(20m)• “Mike Goodchild's house” (20m)
What does time add?• Insights that are not available from space
aloneTi i d d i t• Time is ordered, space is not– potential for getting closer to causality
resolving among competing hypotheses– resolving among competing hypotheses• The temporal extent of new data sources are
necessarily shortnecessarily short– compared to the time-scale of health impacts– can space be substituted for time?p
Advances in analysis• Mining time as well as space
– what happens at x at time t is a function of what happens nearby at time t what happened at x athappens nearby at time t, what happened at x at time t-1, etc.
– finding anomalies in space-time• Analyzing and interpreting tracks
– tracking exposure– inferring interactions
• spotting swarms
inferring acti ities– inferring activities• Real-time analysis
Comparative spatial analysis of the development of the Chinese and US economies through time, 1978-1998
Xinyue Ye, Bowling Green State University
The issues• Big Data
– variable qualityt ll d li– no controlled sampling
– synthesis before analysis– tracking uncertainty at the datum level– tracking uncertainty at the datum level– semantics
• variable meaning
• CyberGIS– computing in the cloud– unlimited computing resources– beyond the old shortcuts
More issues• Space or place?
– linked by location and timeli k d b l d ti ?– or linked by placename and time?
– a platial (or platiotemporal) analysis?• Analogs• Analogs
– what places and times are like this one?– learning from similar situationslearning from similar situations
A very different world• Spatiotemporal dependence
– observations are always autocorrelatedS ti t l h t it• Spatiotemporal heterogeneity– all places, times are different
little prospect of generalization– little prospect of generalization• no random samples
• Requires us to think differentlyRequires us to think differently– based in spatial (and temporal) concepts– orthogonal to traditional training in quantitative g g q
analysis
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