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 EngineeringUniversity of Calgary

2The French Navy Academy Research InstituteFrance

3GIS centerFeng Chia University

Outline

• Introduction• Spatio-Temporal Data Model and Query

Language• Rural-Urban Land Conversion Modeling• Case Study• Summary

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

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

Spatialliterals

Temporalliterals

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.

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

Our spatio-temporal object model (cont’d)

• Shape can change in different forms:

creation

alteration

destruction

reincarnation

fusion fission reallocation

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; }

Tracking of complex land use changes

1984 1992

1997 2002

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

Spatio-temporal Query Language

Query language

Data model

Spatio-temporaldatabase

Spatio-temporal DBMSSpatio-temporal DBMS

Interact with thedatabase

Syntactical ConstructsSTOQL 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

Query Example 1

Query 1. Display all the parcels of land use ‘agricultural’ in 1980. Select p-geo.valFrom 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’

Query Example 2Query 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.ownerFrom parcels As parcel, parcel.geo! As parcelgeo1 parcelgeo2, protected-areas As p-area, p-area.geo! As p-areageo1 p-areageo2Where 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)

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:

1 1 2 2 m my a b x b x b x

log ( ) log ( )1e

Py it P

P

1

y

y

eP

e

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

Land use data

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

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

Binary logistic regression

Note: S.E.: standard error. G.K. Gamma: Goodman-Kruskal Gamma PCP: percentage correctly predicted

Variable Model 1984-1992 Model 1992-1997 Model 1997-2002

Coefficient S.E. Coefficient S.E. Coefficient S.E.

Dens_Pop -0.0000358 0.0002178 0.0001146 0.0003900 -0.0001553 0.0003338

Dist_Com -0.0001541 0.0000716 -0.0002411 0.0002320 -0.0000207 0.0001753

Dist_Res -0.0000596 0.0001409 0.0001611 0.0005761 0.0005248 0.0004804

Dist_Ind 0.0000589 0.0000280 0.0003375 0.0002128 0.0000389 0.0001639

Dist_Road -0.0044079 0.0010538 -0.0017445 0.0013882 -0.0039010 0.0013603

Per_Urb 0.239770 0.0115755 0.367502 0.0273304 0.394208 0.0288114

Per_Agr -0.0967720 0.0090168 -0.0931497 0.0165395 -0.122817 0.0146438

Constant -0.125040 0.342002 -2.09796 0.550595 -0.654405 0.411060

G.K. Gamma 0.94 0.97 0.96

PCP 92.8% 97.9% 95.7%

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

Observed Predicted Total % correct

Urban Agriculture

Urban 45243 27425 72668 62.3

Agriculture 15775 150351 166126 90.5

Overall 61018 177776 238794 81.9

Prognostic capacity evaluation (Cont’d)

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