Urban Change: Understanding how expansion and densification relate to demographic change and their implications for climate change.

Abstract:

Urbanization is the most demographic significant trend of the 21st century particularly in Asia. Characterizing it in a spatial context is difficult given the moderate resolution data provided by traditional sources of demographic data. Previous work on Saigon has shown by using these data together that much more about the correlates and potential consequences of change in the form and expansion of urban change can be learned than with a single data source alone. In this paper, we expand our analysis to two other much different urban and socioeconomic settings: Dhaka and Beijing. Particularly, where the demographic and socioeconomic indicators of change are too infrequent to capture annual change, use of satellites in combination with demographic data may be especially useful for capturing change in exurban and periurban areas, or in smaller cities within larger urban agglomerations.

Using spatial regression techniques, we estimate statistical relationships between remotely sensed data sets to assess the ability demographic changes to predict urban changes as detected by two different satellite measures of change 2000-2010 in Dhaka, Saigon, and Beijing. We then predict socioeconomic outcomes associated with emissions and vulnerability proxies. We use two much different types of satellite data -- the Dense Sample Method (DSM) analysis of the NASA scatterometer data and new built-up area data from the Global Human Settlement Layer of the JRC – which respectively proxy for increases in building heights (vertical expansion) and impervious surface-type changes (horizontal expansion). These different data products help us to better understand the evolution of the built environment and urban form, while the underlying demographic data provide information regarding composition of urban population change, at different levels of economic development, built-upness, and population density. Combining these types of data yields important, high resolution spatial information that provides a more accurate understanding of urban processes, particularly in the context of climate change (as shown in Figure 1). Together these will help understand the form of urban change as well as the relationship between urban change, vulnerability and population distribution within and on the periphery of growing cities.