1. Spatio-Temporal Database Coupled with Spatial Statistics for Urban Land Use Change Analysis
Chenglin Xie1, Bo Huang1, Christophe Claramunt2 and
Magesh Chandramouli3
1Department of Geomatics Engineering
University of Calgary
2The French Navy Academy Research Institute
France
3GIS center
Feng Chia University

2. Outline Introduction Spatio-Temporal Data Model and Query Language
Rural-Urban Land Conversion Modeling
Case Study
Summary

3. Introduction
Understanding the driving forces for urbanization is critical for proper planning and management of resources
Comprehensive and consistent geographical record of land use and relative information: a prerequisite to understanding land use change
Modeling the rural-urban land conversion pattern: critical to predicting urban growth

4. Introduction (Cont’d)
It is necessary to bridge the gap between spatio-temporal database modeling and land use prognostic modeling
Automate the process of change-tracking and predictive analysis
Makes it possible to look back exploring why the change happened

6. Spatio-temporal data models
Snapshot model
Space-time composite model
Event-based spatio-temporal data model
Spatio-temporal object model in line with the Object Database Management Group (ODMG) standard
Huang, B. and Claramunt, C., STOQL: An ODMG-based spatio-temporal object model and query language. In D. Richardson and P. Oosterom (eds.), Advances in Spatial Data Handling, Sringer-Verlag.
Huang, B. and Claramunt, C., Spatiotemporal data model and query language for tracking land use change. Accepted for publication in Transportation Research Record, Journal of Transportation Research Board, US.

7. Our spatio-temporal object model
Different properties (e.g. owner and shape) may change asynchronously
owner: John (1990)–> Frank (1993) –> Martin (2000-now)
shape:  
Different properties may be of different types (string, integer, struct etc.)
owner: string
shape: polygon

8. Our spatio-temporal object model (cont’d)
Shape can change in different forms:

9. Our spatio-temporal object model (cont’d)
Designed a parametric type to represent the changes on different properties
Parametric type allows a function to work uniformly on a range of types.
Temporal<T> (T is a type)
{(val1, t1), (val2, t2), (val3, t3), …, (valn, tn)}
val: T
Class parcel {
integer ID;
temporal<string> owner;
temporal<string> lutype; //land use type
temporal<polygon> shape; }

10. Tracking of complex land use changes
1984
1992
1997
2002

11. Representing the complex change
’s change: {
([1984, 1991], struct(Land_use_type: “agriculture”,
Gextent_ref: “G |1984”)),
([1992, now], struct(Land_use_type: “urban”,
Gextent_ref: “G |1992”)) }
Temporal<T> is used to represent the changes on
different attributes

12. Spatio-temporal Query Language
Spatio-temporal DBMS
Query language
Data model
Interact with the
database
Spatio-temporal
database

13. Syntactical Constructs
STOQL
OQL
Type
[time1, time2]
Struct(start: time1, end: time2)
TimeInterval e!
e.getHistory()
List
es.val
T (any ODMG type and basic spatial types)
es.vt
es.index
e.getStateIndex(ev) (es in e)
Unsigned Long

14. Query Example 1
Query 1. Display all the parcels of land use ‘agricultural’ in 1980.
Select p-geo.val
From parcels As parcel, parcel.geo! As p-geo, parcel.landuse! As p-landuse
Where p-landuse.vt.contains([1980]) and
p-geo.vt.contains([1980]) and
p-landuse.val = ‘agricultural’

15. Query Example 2
Query 2. What were the owners of the parcels which intersected the protected area of the river ‘River1’ over the year 1990, while they were away from that protected area over the year 1980.
Select parcel.owner
From parcels As parcel, parcel.geo! As parcelgeo1 parcelgeo2,
protected-areas As p-area, p-area.geo! As p-areageo1 p-areageo2
Where p-area.name = ‘River1’ and
p-areageo1.vt.contains([1980]) and
parcelgeo1.vt.contains([1980])
p-areageo1.val.disjoint(parcelgeo1.val) and
p-areageo2.vt.contains([1990]) and
parcelgeo2.vt.contains([1990])
p-areageo2.val.intersects(parcelgeo2.val)

16. Rural-Urban Land Conversion Modeling
Several techniques
Cellular automata (CA)
Exploratory spatial data analysis
Regression analysis
Artificial neural networks (ANNs)
The general form of logistic regression model:

17. Case Study New Castle County, Delaware, USA is selected as study area
Snapshots of land use and land cover in 1984, 1992, 1997 and 2002 are used
Land use classifications
Urban areas
Residential
Commercial
Industrial
Agricultural
Others (not suitable for development)
Forest
Water
Barren

18. Land use data

19. GIS-based predictor variables
Seven predictor variables were compiled in ArcInfo 9.0 based on 50m×50m cell size
Three classes of predictors were employed
Site specific characteristics
Proximity
Neighborhoods
Variable name
Description
Dens_Pop
Population density of the cell
Dist_Com
Distance from the cell to the nearest commercial site
Dist_Res
Distance from the cell to the nearest residential area
Dist_Ind
Distance from the cell to the nearest industrial site
Dist_Road
Distance from the cell to the nearest road
Per_Urb
Percentage of urban land use in the surrounding area within 200m radius
Per_Agr
Percentage of rural land use in the surrounding area within 200m radius

20. Spatial sampling
Assumption of econometric model—error terms for each individual observation are uncorrelated
Integration of systematic sampling and random sampling methods
Land use type
Owner
shape

21. Binary logistic regression
Variable
Model
Model
Model
Coefficient
S.E.
Dens_Pop
Dist_Com
Dist_Res
Dist_Ind
Dist_Road
Per_Urb
Per_Agr
Constant
G.K. Gamma
0.94
0.97
0.96
PCP
92.8%
97.9%
95.7%
Note: S.E.: standard error.
G.K. Gamma: Goodman-Kruskal Gamma
PCP: percentage correctly predicted

22. Prognostic capacity evaluation
The validation process of the model is performed for the span of
The overall 81.9% correct prediction is relative high and the accuracy of correct prediction for urbanized area (62.3%) is relative satisfactory compared to the results of other researches in this field
Observed
Predicted
Total
% correct
Urban
Agriculture
45243
27425
72668
62.3
15775
150351
166126
90.5
Overall
61018
177776
238794
81.9

23. Prognostic capacity evaluation (Cont’d)

24. Summary
Bridges the gap between spatio-temporal database modeling and land use change analysis
Spatial-temporal data model represents complex land parcel changes dynamics over time and parcel
Employs spatial land use, population and road network data to derive a predictive model of rural-urban land conversions in New Castle County, Delaware
Succeeds largely in revealing the land use change