Spatio-Temporal Database Coupled with Spatial Statistics for Urban Land Use Change Analysis

1. Spatio-Temporal Database Coupled with Spatial Statistics for Urban Land Use Change Analysis Chenglin Xie1, Bo Huang1, Christophe Claramunt2and 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 • 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., 2002. 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., 2005. 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: 1990 1996 2002 • 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. 1992 1984 1997 2002 Tracking of complex land use changes

11. Representing the complex change 345600001’s change: { ([1984, 1991], struct(Land_use_type: “agriculture”, Gextent_ref: “G345600001|1984”)), ([1992, now], struct(Land_use_type: “urban”, Gextent_ref: “G345600001|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. STOQL OQL Type [time1, time2] Struct(start: time1, end: time2) TimeInterval e! e.getHistory() List es.val es.val T (any ODMG type and basic spatial types) es.vt es.vt TimeInterval es.index e.getStateIndex(ev) (es in e) Unsigned Long Syntactical Constructs

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

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 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 1984-2002 • 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

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