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SpatioTemporal Database Coupled with Spatial Statistics for Urban Land Use Change Analysis

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Title: SpatioTemporal 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 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 (Contd)
  • 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

5
(No Transcript)
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)gt Frank (1993) gt 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 (contd)
  • Shape can change in different forms

9
Our spatio-temporal object model (contd)
  • Designed a parametric type to represent the
    changes on different properties
  • Parametric type allows a function to work
    uniformly on a range of types.
  • TemporalltTgt (T is a type)
  • (val1, t1), (val2, t2), (val3, t3), , (valn,
    tn)
  • val T

Class parcel integer ID
temporalltstringgt owner temporalltstringgt
lutype //land use type temporalltpolygongt
shape
10
Tracking of complex land use changes
11
Representing the complex change
345600001s change (1984, 1991,
struct(Land_use_type agriculture,
Gextent_ref
G3456000011984)), (1992, now,
struct(Land_use_type urban,
Gextent_ref G3456000011992))

TemporalltTgt 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
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 50m50m cell size
  • Three classes of predictors were employed
  • Site specific characteristics
  • Proximity
  • Neighborhoods

20
Spatial sampling
  • Assumption of econometric modelerror 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 (Contd)

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
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