chenglin xie1, bo huang1, christophe claramunt2 and
DESCRIPTION
Outline Introduction Spatio-Temporal Data Model and Query Language Rural-Urban Land Conversion Modeling Case Study SummaryTRANSCRIPT
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
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; }
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 1Query 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 ( )1ePy it PP
1
y
y
ePe
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