Abstract
The recent rapid urbanization in Chiang Mai-Lamphun area may have many socio-economic impacts on its rural surroundings, including the redistributing of the rural workforce. This paper presents the use of spatial statistical techniques to simultaneously model physical and socio-economic factors affecting the out-flow of rural workforce in order to understand the region-wide spatial impact of the development. This application example also demonstrates that GIS can be efficient technical bed for spatial data analysis in this kind of regional study.

1. Introduction
As the major growth center for Northern Thailand- "metropolitan area" of Chiang Mai-Lamphun is most distinguishable in the region, embracing districts centers located within about 40 kilometers from the city of Chiang Mai and/or Lamphun municipality. Geographically, the study area is located approximately between Latitude 18°08' N and 19°06'N and Longitude 98°30'E and 99°25', with total area of 5806 km2, Administratively, the study area composes of 10 districts of Chiang Mai province and 6 districts of Lamphun province, resulting in total of 146 sub-districts or tambols (Fig. 1). During the last decade, the area had experienced rapid urban expansion and rapid industrial establishments (Suwan, 1992, Tran, 1997). In the past, as Suwan (1992) showed, Chiang Mai is characterized by scattered small-scale and cottage industry. Most have grown from settlements around administrative headquarters, market centers or important transport mode. From 1986, the Northern Industrial Estate with 87 projects implemented (as of December 1994) was built in Muang Lamphun district. The rapid and unbalanced urbanization could severely distort traditional rural-urban relations, widen urban-rural disparity, aggravated landlessness and rural poverty, which in total present on alarming perspective for future development of the study area. As a result of "push" and "pull" factors in the urban-rural linkages, the rural population tend to look for an employment outside the place of their residence. Thus, the rural labor outflow indicating the job attraction of urban centers as well as the excess of free labor released from the agricultural sector could represent the intensity of urban-rural linkages as a result of regional development.


Figure 1. Map of Chiang Mai-Lamphun area with tambol boundaries (lighter lines) and district boundaries (darker lines).
Recent technological advances in geographic information system (GIS) have made it possible to manipulate large amounts of geographic data and construct the topological structure underlying complicated spatial phenomena. The integration of spatial analysis and GIS is an important next step in the development of spatial analysis technologies. Illustrating the importance of integration the technologies, this study is an attempt to model the intensity of urban-rural linkage in Chiang Mai-Lamphun area in terms of intra-regional job distribution in 1994 and to explore the significant socio-economic factors affecting the decision of people to choose place of work. The analyses presented in this paper were conducted on the 'loose coupling' (i.e., using data transfer) between GIS package (Arc/Info 7.0) and spatial statistical software (SpaceStat 1.80).

2. Spatial GIS Database Building and Management
Since the study was emphasizing on macro level of analysis, the data were collected from the secondary sources. The most important source for spatial data is the land-use maps, topographic maps, transportation network map. The major source for socio-economic data were the National Rural Development Database, Ministry of Industry, Chiang Mai and Lamphun provincial offices, Municipality offices, Department of Town and Country Planning, etc.

The spatial data available in forms of paper maps (e.g., land-use, road network maps, etc.) were digitized and introduced into vectors GIS. The digitized maps were corrected and topography was built to make them usable in later analysis. The aspatial (attributes on socio-economic) data were input directly into GIS database and/or converted from database files. The "join" operation was applied to make interrelated information (spatial attributes) combined through a case item. The GIS database for the Chiang Mai-Lamphun project was maintained in Arc/Info 7.0.

3. The Model Specification and Definition of Explanatory Variables
Based on data available at different level and scale, the tambol (sub-district) was chosen as basic areal unit for the study. The spatial overlay and logical-statistical analysis in GIS (Arc/Info) were adopted to summarize the selected information over each areal unit (tambol) for creating the desired spatial variables for the spatial statistical analysis (Tran, 1997).

The percentage of working-out population of each tambol could be served well as indicator for urban-rural linkage and was assumed to be a function of 'push' and 'pull' factors related to demographic, economic and social aspects of development in Chiang Mai-Lamphun area. The spatial data integration in GIS finally produced a set of spatial and spatialized variables for the analysis, which therefore was categorized under three general headings: (1) demographic structure, (2) economic structure, and (3) education attainment. The demographic aspect was represented by population density and the population pressure could be one of the important factors pushing rural people from their village to look for an employment in other places. The social aspect was represented by different level of education attainment (illiteracy, primary education, secondary education) as education was the primary condition for rural people in finding an employment in urban areas (Sriboonruang, 1992).

A large set of economic variables re presenting different economic sectors was submitted to factor analysis in order to identify the underlying dimensions, or factors of economic structure. As result, the economic structure of the study area was represented by 3 major composite economic factors having respective groups of high-factors-loading original variables summarized in Table 1. The detailed procedure to derive 3 major economic factors and their interpretation are beyond the scope of this paper and were discussed in T ran (1997).

Table 1 Factor characteristics and respective groups of high-factor-loading economic variables.

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• Factor 1(Index of Urban-biased Economy) high-positively correlates with percentage of urbanized and residential areas, road density, property taxes and percentage of occupied in trading population.
• Factor 2(Index of Industrial Economy)highly-positively correlates with total number of industrial employees, number of employees of the large scale factories, number of factories, the total capital investments, the industrial land-use.
• Factor 3(Index of Lacking Opportunity) highly-positively correlates with travel time to nearest town and market centers, median distance to industrial centers and nearest roads, farmer population, and negatively correlates with percentage of agriculture land.

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4. Classical Linear Regression Model
In order to avoid the possibility of non-formal errors, the response variable was transformed with a natural logarithm function to create new variable. The transformed variable exhibit a distribution non significantly different from normal at p=0.01, based on Wald statistic. The urban-rural dichotomy played important impacts on most of explanatory variables as shown in Table 2 suggesting that it is necessary to include this important variable (Rural-Urban Indicator) into the starting model. The regression analysis model running under SpaceStat 1.980 software and the insignificant explanatory variables were excluded from models based on t-value (t=0.1). The eventual linear regression model with diagnostic tests was summarized in Table 3.

Table 2 A brief classical ANOVA table of regressions on urban-rural dichotomy indicator.

Explanatory Variables (units)	F-value	Probability F	Adjusted R-Square
Factor 1	67.5488	*0.0000	*.31458
Factor 2	0.0251	0.8743	0.00677
Factor 3	2.40459	0.1231	0.00960
Illiterate population (%)	0.8754	0.3510	0.00086
Primary Educated population (%)	6.8803	*0.0097	0.03897
Population Density (person/ha)	58.8338	0.0000	*0.28513
Secondary Educated population (%)	52.5278	*0.0000	*0.26219
Working-out population (%)	4.2259	*0.0480	0.04117

Note * indicates the significance at p<0.05
Table 3 Results of traditional regression analysis with non-normal errors, heteroskedastic errors, multicollinear predictors and spatially autocorrelated errors diagnostics

Response Variable :In (Working-out pop. +1)
R2=0.4567 R2-adj=0.4187 Log-likelihood=-101.54 AIC=171.027
Variable	Coefficients	Std. Err.	t-value	Prob
Constant	1.94702	0.197472	9.859745	0.000000
Factor 1	0.1777	0.100939	1.760476	0.080511
Factor 3	-0.452775	0.0591491	-7.60476	0.000000
Pop density	-0.000138259	5.54217E-05	-2.494679	0.013770
Rural-Urban Indicator	0.630054	0.205611	3.064298	0.002618
Regression Diagnostics Multicollinearity condition number = 0.165699 Kiefer-Salmon (error normality) = 11.435 (p = 0.003) Koenker-Bassett test (heteroskedasticity) = 33.138 (p = 0.000) Moran's I (error) = 0.276 (p = 0.000)

The regression diagnostics showed insignificant non-normal errors and insignificant multicollinearity. However, the spatial autocorrelation was clearly present in the model residual (at significant level of 0.00%), showing significant violation o the basic assumption for linear regression analysis on spatial independence of sampling.

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