Urban growth modeling of Kathmandu metropolitan region, Nepal

Abstract

The complexity of urban system requires integrated tools and techniques to understand the spatial process of urban development and project the future scenarios. This research aims to simulate urban growth patterns in Kathmandu metropolitan region in Nepal. The region, surrounded by complex mountainous terrain, has very limited land resources for new developments. As similar to many cities of the developing world, it has been facing rapid population growth and daunting environmental problems. Three time series land use maps in a fine-scale (30 m resolution), derived from satellite remote sensing, for the last three decades of the 20th century were used to clarify the spatial process of urbanization. Based on the historical experiences of the land use transitions, we adopted weight of evidence method integrated in cellular automata framework for predicting the future spatial patterns of urban growth. We extrapolated urban development patterns to 2010 and 2020 under the current scenario across the metropolitan region. Depending on local characteristics and land cover transition rates, this model produced noticeable spatial pattern of changes in the region. Based on the extrapolated spatial patterns, the urban development in the Kathmandu valley will continue through both in-filling in existing urban areas and outward rapid expansion toward the east and south directions. Overall development will be greatly affected by the existing urban space, transportation network, and topographic complexity.

Introduction

Urban growth is recognized as physical and functional changes due to the transition of rural landscape to urban forms. The time–space relationship plays an important role in order to understand the dynamic process of urban growth. The dynamic process consists of a complex nonlinear interaction between several components, i.e., topography, river, land use, transportation, culture, population, economy, and growth policies. Many efforts have been made to improve such dynamic process representation with the utility of cellular automata (CA) coupling with fuzzy logic (Liu, 2009), artificial neural network (Almeida et al., 2008, Li and Yeh, 2002), Markov chain with modified genetic algorithm (Tang, Wang, & Yao, 2007), weight of evidence (Soares-Filho et al., 2004), non-ordinal and multi-nominal logit estimators (Landis, 2001), SLEUTH (Clarke et al., 1997, Jantz et al., 2010) and others (Batty et al., 1997, White and Engelen, 1997).

Models based on the principles of CA are developing rapidly. CA approach provides a dynamic modeling environment which is well suited to modeling complex environment composed of large number of individual elements. The land use change and urban growth process can be compared with the behavior of a cellular automaton in many aspects, for instance, the space of an urban area can be regarded as a combination of a number of cells, each cell taking a finite set of possible states representing the extent of its urban development with the state of each cell evolving in discrete time steps according to local transition rules. Therefore, CA based urban models usually pay more attention to simulating the dynamic process of urban development and defining the factors or rules driving the development (Batty et al., 1997). Different CA models have been developed to simulate urban growth and urban land use/cover change over time. The differences among various models exist in modifying the five basic elements of CA, i.e., the spatial tessellation of cells, states of cells, neighborhood, transition rules, and time (Liu, 2009). CA models have demonstrated to be effective platforms for simulating dynamic spatial interactions among biophysical and socio-economic factors associated with land use and land cover change (Jantz et al., 2010).

While new urban models have provided insights into urban dynamics, a deeper understanding of the physical and socioeconomic patterns and processes associated with urbanization is still limited in developing countries in South Asia. Although, emerging geospatial techniques are bridging the spatial data gap recently, empirical case studies are still very few (Thapa & Murayama, 2009). This research aims to simulate urban growth in Kathmandu metropolitan region in Nepal using weight of evidence technique incorporating with CA. As the result of population growth and migration from rural to urban areas, urbanization has been recognized as a critical process in metropolitan areas of Nepal (Bhattarai and Conway, 2010, Haack, 2009, Portnov et al., 2007). The Kathmandu metropolitan region, capital and major tourist gateway, has been facing rapid urbanization over the last three decades. Recently, it has an estimated population of 2.18 million with an annual growth rate of 5.2% (Thapa & Murayama, 2010). Such urbanization pressure results rapid changes in the urban landscape pattern of the region adding more constructions and the loss of natural lands.

Kathmandu, the capital of Nepal, has long history of development and exhibits a typical city surrounded by complex mountain terrains in the Himalayan region. History has witnessed its development as a strategic center of power, politics, culture and commerce (Thapa, Murayama, & Ale, 2008). However, along with the establishment of modern transportation infrastructures bringing easy access to the city, the agglomeration of rural settlements of Kathmandu valley into the city began in the early 1960s. The predominantly agricultural landscape gradually changed to an urban landscape with increasing human settlement in the 1960s and 1970s. The land changing process has escalated since the 1980s. Spatial diffusion of urban/built-up areas has spread outward from the city core and along the major roadways. Agricultural encroachment in rural hills and mountain peripheries and urbanization in the valley floor area are identified as the most common phenomenon in the valley (Bhattarai and Conway, 2010, Haack, 2009, Thapa and Murayama, 2009).

Several urban land use development planning and policy initiatives for the valley have been made by the government in the past decades (Thapa et al., 2008). A latest planning document ‘Long Term Development Concept for Kathmandu Valley’ (Kathmandu Uptyakako Dirghakalin Bikas Avadharana) was released in 2002 (KVUDC, 2002). This document as planning reference conceptualizes scenarios to develop the Kathmandu metropolitan region by 2020. This long-term plan recommends the promotion of the tourism led service sector; guided urban development seeking compact urban form and the conservation of agricultural land; infrastructure development coordinated with land use; a new outer ring road to connect the traditional settlements in the metropolitan region; and rigorous regulation of areas defined as environmentally sensitive. All these policy recommendations eventually affect the future spatial pattern of urbanization.