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

Chenglin Xie 1 , Bo Huang 1 , Christophe Claramunt 2 and Magesh Chandramouli 3 1 Department of Geomatics Engineering University of Calgary 2 The French Navy Academy Research Institute France 3 GIS center Feng 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

Spatial litera ls Tempora l litera ls

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;

1984

Tracking of complex land use changes

1992 1997 2002

Representing the complex change

} { 345600001 ’ s change: ([1984, 1991], struct(Land_use_type: “ ([1992, now], struct(Land_use_type: “ agriculture urban ”, ”, Gextent_ref: “G345600001|1984”)), Gextent_ref: “G345600001|1992”)) Temporal is used to represent the changes on different attributes

Spatio-temporal Query Language

Spatio-temporal DBMS

Query language Data model Spatio-temporal database Interact with the database

STOQL

Syntactical Constructs

OQL Type

[time 1 , time 2 ] Struct(start: time 1 , end: time 2 )

TimeInterval

e!

es.val

e.getHistory() es.val

List 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.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’

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)

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:

y

  1 1 

b x

2 2  

b x m m y P

  log (

e

1

y e

1 

e y P



P

) 

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 Dens_Pop Dist_Com Dist_Res Dist_Ind Dist_Road Per_Urb Per_Agr Description Population density of the cell Distance from the cell to the nearest commercial site Distance from the cell to the nearest residential area Distance from the cell to the nearest industrial site Distance from the cell to the nearest road Percentage of urban land use in the surrounding area within 200m radius 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

Variable Dens_Pop Dist_Com Dist_Res Dist_Ind Dist_Road Per_Urb Per_Agr Constant G.K. Gamma PCP Model 1984-1992 Coefficient S.E.

-0.0000358

-0.0001541

0.0002178

0.0000716

-0.0000596

0.0000589

-0.0044079

0.239770

-0.0967720

-0.125040

0.0001409

0.0000280

0.0010538

0.0115755

0.0090168

0.342002

0.94

92.8% Model 1992-1997 Coefficient S.E.

0.0001146

-0.0002411

0.0003900

0.0002320

0.0001611

0.0003375

-0.0017445

0.367502

-0.0931497

-2.09796

0.0005761

0.0002128

0.0013882

0.0273304

0.0165395

0.550595

0.97

97.9% Note: S.E.: standard error.

G.K. Gamma: Goodman-Kruskal Gamma PCP: percentage correctly predicted Model 1997-2002 Coefficient S.E.

-0.0001553

-0.0000207

0.0003338

0.0001753

0.0005248

0.0000389

-0.0039010

0.394208

-0.122817

-0.654405

0.0004804

0.0001639

0.0013603

0.0288114

0.0146438

0.411060

0.96

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 Urban Agriculture Overall Urban

45243

15775 61018 Predicted Agriculture 27425

150351

177776 Total 72668 166126 238794 % correct 62.3

90.5

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