Urbanization process is one of great issues in our time. Cities are now home to more than half of the world's population. Especially, the towns and cities of developing countries are places where almost all the world population growth is occurring and thus preparation for the growth is required to meet a range of development needs. In this increasing urban era, it is of importance to monitor urban populations and environments, and understand how urban populations are changing in both the spatial and time dimensions for development and urban planning policies.
This dissertation considers econometric modeling of city population growth, aiming at estimating and forecasting rates of city population growth and size for almost all of the developing countries. Both classical and Bayesian spatial econometric models of city growth are used, which can provide us information on uncertainty and take into account of economic, demographic, geographic, and environmental factors promoting and hindering city population growth.
The main contributions of this dissertation are two-fold: (1) it develops Bayesian MCMC estimation and forecasting methods of a panel data model with spatially correlated errors and (2) it lays the foundation of a spatially-explicit cities database by linking two existing cities datasets, that of the United Nations Population Division (a panel dataset of city populations) and CIESIN's GRUMP dataset, housed at the Columbia University's Earth Institute, which is in geospatial format. This geospatial cities database provides us with a better understanding of patterns of urbanization.
By incorporating into the database additional city-level indicators using GIS and geospatial programming, this study analyzes how current urban populations are distributed by ecological environments and how the patterns evolve over time. This analysis documents urban settlements and their population sizes in the dryland ecozone and low-elevation coastal zone, and quantifies vulnerable populations to related climate-related hazards (e.g. storm surges, droughts). Also, the model estimation implies that growth of a city is affected not only by the city's characteristics but also by those of its neighboring cities.
As a baseline model, this study first develops fertility-based econometric models of city growth for developing countries. It finds that urban fertility rate has a significant positive impact on developing-country city growth rate, so re-confirms the important role of fertility in city growth of developing countries. The median future city growth rate is projected to decline as fertility rates continue their historical trend downward.
In summary, this dissertation deals with the population dimension of urbanization process, aiming at developing international-level estimation and forecasting methods of city growth, especially in developing countries. There are unresolved issues which should be addressed in the future. Also, simultaneous approach considering multi-dimensions of urbanization process should be studied for systematic analysis of complex urbanization process.
|Advisor:||Montgomery, Mark R.|
|Commitee:||Anagostopoulos, Alexis, Mendell, Nancy, Sanderson, Warren|
|School:||State University of New York at Stony Brook|
|School Location:||United States -- New York|
|Source:||DAI-A 72/09, Dissertation Abstracts International|
|Subjects:||Statistics, Economics, Demography, Urban planning|
|Keywords:||Cities, Climate change, Developing countries, Fertility, Markov Chain Monte Carlo, Panel data, Population, Population projection, Spatial econometrics, Urbanization|
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