Spatial Analysis and Modeling in Geographical Transformation Process

Humans' representation about geographic phenomena in natural language is usually qualitative rather than quantitative. Qualitative spatial reasoning provides an approach which is considered to be closer to the representation. Commercial GIS software is being confronted with a challenge that the software should be equipped with artificial intelligent functions like qualitative spatial reasoning for users, especially for spatial decision-makers. This research proposes a framework of field-based fuzzy spatial reasoning through which qualitative description usually encountered in spatial reasoning process can be handled quantitatively. As preconditioning, field-based fuzzy representation structure for qualitative description is put forward, then the methods of constructing membership function are discussed. Standard operations of field-based fuzzy spatial reasoning model in the case of constraint satisfaction problem (CSP) are illustrated. An example explains the implication of the model in spatial decision-making process.

The analysis of local spatial autocorrelation for spatial attributes has been an important concern in geographical inquiry. In this chapter, we propose a concept and algorithm of k-order neighbours based on Delaunay’s triangulated irregular networks (TIN) and redefine Getis and Ord’s (Geographical Analysis, 24, 189–206, 1992) local spatial autocorrelation statistic as G _i (k) with weight coefficient w _ij (k) based on k-order neighbours for the study of local patterns in spatial attributes. To test the validity of these statistics, an experiment is performed using spatial data of the elderly population in Ichikawa City, Chiba Prefecture, Japan. The difference between the weight coefficients of the k-order neighbours and distance parameter to measure the spatial proximity of districts located in the city center and near the city limits is found by Monte-Carlo simulation.

Urban land-use pattern occurs not only at certain spatial scale but also at certain classification system. Study of spatial scale effect on urban land-use pattern analysis for the construction of spatial model of urban land-use changes entails the consideration of urban land-use classification system selected. Purpose of this research is to investigate the effect characteristics of spatial scale on the result of urban land-use pattern analysis for two different classification systems in terms of spatial autocorrelation and fuzzy mathematics by an empirical study in the CBD (Central Business District) of Tokyo so as to provide some useful information for the construction of multi-scale or hierarchical spatial model of urban dynamics. The Results show that while spatial autocorrelation of all the categories of urban land-use at different classification systems in this study is scale-dependent, characteristics of the spatial scale effect on pattern of urban land-use categories are different from classification systems. However, the general change trends of spatial autocorrelation of urban land-use for different classification systems show similar across a range of scale. Reducing number of urban land-use categories for one classification system may diminish the loss of information of land-use area across the range of scale and the effect of spatial scale on urban land-use pattern analysis in the certain extent.

Local spatial interaction (i.e., neighborhood interaction) between land-use types is an important component in Cellular Automata (CA)-based urban geosimulation models. Herein a new method based on the integration of Tobler's First Law of Geography with Reilly's gravity model and coupled with logistical regression approach is proposed to model and calibrate the neighborhood interaction. This method is embedded into a constrained CA model to simulate the spatial process of urban growth in the Tokyo metropolitan area. The results indicate that this method captures the main characteristics of neighborhood interactions in the spatial process of urban growth. Further, this method provides an alternative and extensive approach to present local spatial interactions for “bottom-up” urban models.

Advantages of dasymetric map over traditional choropleth map have been well documented in many cartographic journals. Dasymetric uses ancillary dataset to create smaller geographical unit of population. In fact, the smaller geographical unit of population data is required for effective disaster management, emergency preparedness, retail market competition, health and disease studies, crime analysis and other population data analysis at micro-scale level. In this chapter, we discuss new dasymetric mapping technique based on GIS estimated building population which was computed from building footprints, census tract and LIDAR derived Digital Volume Model DVM.

Population data used in GIS analyses is generally assumed to be homogeneous and planar (i.e. census tracts, townships or prefectures) due to the public unavailability of building population data. However, information on building population is required for micro-spatial analysis for improved disaster management and emergency preparedness, public facility management for urban planning, consumer and retail market analysis, environment and public health programs and other demographic studies. This chapter discusses a GIS approach using the Areametric and Volumetric methods for estimating building population based on census tracts and building footprint datasets. The estimated results were evaluated using actual building population data by visual, statistical and spatial means, and validated for use in micro-spatial analysis. We have also implemented a standalone GIS tool (known as PopShape GIS) for generating new building footprint with population attribute information based on user-defined criteria.

Though enrolment by proximity is an official educational policy in Iran, the lack of clearly defined school attendance areas has resulted in an informal open enrolment system where parents may choose public schools outside their residential areas. Two major consequences of parental choice are longer commutes to schools and increased use of motor vehicle transport. To delineate school attendance areas for public female junior high schools in the city of Rasht in Northern Iran, this research used the multiplicatively weighted Voronoi diagram (MWVD) technique to construct school attendance areas. The approach was shown to be useful in developing countries like Iran where accident fatalities are not only disproportionately high but the public school system lacks sufficient educational diversity to offset the societal costs of opting out of neighborhood schools. A GIS-based specialist package was used for constructing the proposed school attendance areas. Given the MWVD utility in demarcating spaces so that all journeys within them are closest to a chosen point, the research concluded that proposed school attendance areas can result in shorter and more convenient commutes on foot for students. This can eliminate the need to travel by vehicle for the overwhelming majority of students and thus make their journeys more convenient and safer.

In this chapter, we examined four land use and land cover mapping approaches, i.e., unsupervised, supervised, fuzzy supervised and GIS post-processing. Advanced Land Observing Satellite image and fieldwork data were used to predict urban land use and land cover of Tsukuba city in Japan. Geographic reference data were created applying random stratified sampling method for assessing accuracy of each map produced from the approaches. The accuracies of the maps measured using error matrices and Kappa indices. Among the approaches tested, the GIS post-processing approach improved the mapping results, showing the highest overall accuracy of 89.33%. The fuzzy supervised approach yielded a better accuracy (87.67%) than the supervised and unsupervised approaches. This chapter presents the strengths of the mapping approaches and the potentials of the sensor for mapping of urban areas, which may help urban planners to monitor and interpret complex and dynamic urban system.

To simulate urban land-use pattern for past and future through constructing spatial model, dynamics analysis of urban land-use pattern to abstract spatial process of urbanization is an important step. Spatial metrics provide good links between urban land-use pattern and process. This research analyzes urban dynamics of Yokohama city at multi-category system and high spatial resolution scale in terms of spatial metrics under the support of the data set “Detailed Digital Information (10 m Grid Land Use) of Metropolitan Area” of Tokyo. The results show that the dynamics are presented well using the spatial metrics at the micro-scale. Comparison of the analysis results between multi-category system and binary-category system is carried out to investigate the difference in presenting urban dynamics in terms of spatial metrics at different spatial scales. The results indicate that the difference in depicting the process of urban dynamics exists at different scales, and analyzing urban dynamics at multi-scale using spatial metrics contributes to comprehensive interpretation of urban dynamics. The analyses also offer useful information for research on selecting metrics in interpretation of urban dynamics.

Tam Dao National Park (TDNP) region is a last remaining primary forest near Hanoi, the capital of Vietnam. It is endowed with some of the highest levels of biodiversity in Vietnam. Forest conversion due to illegal logging and agricultural expansion due to growing population in its vicinity is a major problem that is hampering biodiversity conservation efforts in the TDNP region. Yet, areas vulnerable to forest conversion are unknown. In this chapter, we predicted areas vulnerable to forest changes in the TDNP region using multi-temporal remote sensing data and a multi-layer perceptron neural network (MLPNN) with a Markov chain model (MLPNN-M). The MLPNN-M model predicted increasing pressure in the remaining primary forest within the park as well as on the secondary forest in the surrounding areas. The primary forest is predicted to decrease from 18.03% in 2007 to 15.10% in 2014 and 12.66% in 2021. Our results can be used to prioritize locations for future biodiversity conservation and forest management efforts. The combined use of remote sensing and spatial modeling techniques provides an effective tool for monitoring the remaining forests in the TDNP region.

This chapter presents an integrated technique of AHP and GIS to evaluate the land for peri-urban agriculture. Hanoi province, Vietnam was selected for the case study. Transformation of conventional agriculture to modern cash crops is the current trend in peri-urban Hanoi. A field survey with focused group discussions was conducted. Based on field survey data analysis, soil, land use, water resources, road network and market were chosen as major factors affecting the peri-urban agriculture. A map of each factor with different logical criteria was prepared. The Analytical Hierarchical Process (AHP) method was applied to identify the priority weight of each factor. Five spatial layers with their corresponding weights were linearly combined to prepare the suitability map. The map was further scaled as High suitable, Medium suitable, Low suitable and Unsuitable land for the peri-urban agriculture. This empirical scenario provides a cost effective and rapid land evaluation framework which may help policy makers, urban and regional planners and researchers working in developing countries.

The idea that motivated this research is based on the principle of strong foundation, one of the key elements of sustainable livelihood. In this study, site suitability analysis, a scientific method that could help build a good foundation of a livelihood project like beekeeping, was carried out. Geographic Information System (GIS) and Multi-Criteria Evaluation (MCE) techniques, which have been proven in many studies and experiences to aid in the decision-making process, were used to evaluate the suitability of the province of La Union, Philippines for beekeeping. An empirical conceptual model, which involves the participation of stakeholders and experts in the decision-making process, was developed and implemented. The model is organized into three main components such as database creation and management, spatial multi-criteria analysis, and validation process. The final suitability map was validated through a correlation analysis of the honey yield of the existing beekeeping projects and the calculated suitability values. Results showed a relatively high correlation between these two parameters. This empirical scenario provides a framework, which may help policy makers, planners, and other concerned and interested individuals strengthen the foundation of beekeeping and other related livelihood projects, a foundation that could make these economic ventures sustainable.

Abstract This chapter explores situation of sea navigation in south East Asia focusing on the Strait of Malacca. The strait links the Indian and Pacific oceans, which is considered one of the busiest in several narrow water channels around the world. The paper highlights the significance of the strait to global maritime trade, volume of traffic, and rising environmental and social consequences. A feasibility study of constructing a new shipping canal in the South Thai Kra Isthmus as an alternative option of Malacca route had been studied since 19th century. The paper examines suitable sites for a potential shipping canal in the Kra Isthmus using physiographic spatial data i.e., elevation, sea charts, geology, soils, and river systems. Each spatial data was considered as a separate decision variable for site evaluation. Separate evaluation criterions were prepared for each variable based on shipping canal requirements. Overlaying the maps in ArcGIS environment, the variables were carefully evaluated, and five suitable sites were suggested. The length of the shipping canal over sea and land was computed for each site. Site B located in south of Ranong and Chumphon provinces, was found shortest one, whereas site C in Surat Thani, Pangnna and Krabi provinces, was the longest. However, each site consisted of benefits and constrains.

This chapter examines the spatiotemporal pattern of urbanization in Kathmandu Valley using remote sensing and spatial metrics techniques. The study is based on time series data compiled from satellite images acquired in the last four decades. A five-step hybrid technique is presented to create land use and land cover maps from remote sensing imagery. Urban built-up areas had a slow trend of growth in the 1960s and 1970s but have grown rapidly since the 1980s. The metrics of the urbanization process has confirmed that the landscape in the valley consists of fragmented and heterogeneous land use combinations. However, the refill type of development process in the city core and immediate fringe areas has shown a decreasing trend in the neighborhood distances between land use patches, and an increasing trend towards physical connectedness, which indicates a higher probability of homogenous landscape development in the upcoming decades.

Poverty has a geographic dimension. Geography, particularly the physical environment, plays a significant bearing on the state of poverty particularly in developing countries. However, this geographic dimension has not been given much attention in many poverty studies, especially in the Philippines. In an attempt to underscore its importance, this study explores the possible underlying determinants to poverty using two adjacent provinces of Albay and Camarines Sur as pilot site. Taking advantage of the analytical capability of GIS, the study incorporated spatial variables in the multiple regression analysis, namely: agro-climatic conditions, access to road infrastructure, and proximity to major markets; together with the influence of land distribution program, fiscal decentralization policy, and population growth, in order to analyze their likely influence on the incidence of poverty. Regression results revealed that access to road infrastructure, proximity to major markets, rate of land distribution, bias in fiscal decentralization policy, and aspects of agro-climatic condition, i.e. elevation, slope and rainfall, all have significant effects on poverty incidence within the study site. Geography and facets of public policy have a strong impact on the state of poverty, indeed.