At first glance, immobility seems a natural opposite to mobility. But, in a way, paralysis is also immanent to movement. In May 2018, a strike organized by truck drivers in Brazil ended up producing a series of disruptions causing an unprecedented strandedness. The event was not a humanitarian catastrophe, but a logistical disaster, exposing major failures in commodity supply systems, monumental economic losses, cancellations of basic services, disruption of public services and despair in the population. In face of this, it was possible to verify the strange, surprising and potently revealing nature of immobility in a world of mobile lives.
Dr. Benício’s work spans from Business Administration and Philosophy, having obtained a Ph.D. from City, University of London, United Kingdom (1997). He holds positions of Associate Professor at the Federal University of Pernambuco, Brazil and researcher of the National Council for Scientific and Technological Development, a research funding agency associated to the Ministry of Science and Technology. He has experience in the areas of teaching and research working mainly with the following subjects: Urban Studies; Mobility Policy; Technology and Modernity; Mobility and Citizenship; Discursive Policy and Practices; Post-Structuralism and Theory of Discourse.
Urbanization has been one of the most dramatic consequences of the industrial revolution. Indeed, the majority of the world population now lives in urban centers as compared to less than one (1) percent in 1800 and for most of human history. Today, Sub-Saharan Africa is often viewed as the world’s fastest urbanizing region. However, urbanization in the developing world has been associated with degradation of the natural environment and the worsening of the urban population’s health.
Remote sensing data and methods have been used to study urban land use change (LUC). High spatial resolution remote sensing images are used to identify detailed urban land use classes while only general land use types can be identified from moderate spatial resolution data. No studies have attempted to combine high spatial resolution and moderate spatial resolution images to conduct urban land use change analysis.
In this lecture, I will present a new image processing method that combine high and moderate spatial resolution imagery to conduct urban land use change analysis. I will also compare and contrast the growth rate, amount, and pattern of Accra and Kumasi, the two major urban centers of Ghana. Finally, I will be exploring the connections between changes in urban land cover and land use, and changes in under-five child mortality for the city of Accra.
Dr. Sory Toure obtained his doctorate in Geography from the joint doctoral program at UCSB and SDSU in the Fall of 2017. He is interested in the application of remote sensing methods and techniques to solve environmental problems stemming from the different aspects of the demographic transition. Throughout his graduate studies, he worked on the development of algorithms for the classification of remote sensing images. In other words, his work tries to link “the people to the pixel”. He is currently a lecturer at Cal State San Marcos where he teaches world regional geography and geographic information science classes.
Arthur Getis Distinguished Lecture
Many spatial scientists are focused on understanding processes by observing and comparing spatial patterns of objects or the space-time patterns of events. In their 1974 book, Models of Spatial Processes, Art Getis and Barry Boots presented a process-oriented approach for making inferences based on patterns. This approach has led to important findings, but widespread applicability of this approach has been limited by the equifinality problem. Particularly, the problem is that the same process might result in many different patterns, and very different processes might result in the same pattern.
Advances in computing have enabled increasingly complex simulation models, and spatially-explicit agent based models (ABMs) have been used to test hypotheses about spatial processes in a variety of disciplines. Pattern oriented modeling has been introduced to mitigate the equifinality problem by using multiple patterns in the creation and validation of models. This approach has been adapted by some researchers, but only on a limited basis. Despite the rapid increase in the amount, types, resolution, and volume of data being collected, most ABMs are still being calibrated using simple aggregate measures. There has been very little assessment of the relative value of patterns for discriminating between models.
This talk reports on experiments that assess the usefulness of space-time patterns for calibrating and validating individual-level models of vector-borne disease transmission. Micro-level space-time patterns appear to be more useful than macro-level patterns in eliminating poorly specified candidate models and efficiently estimating parameters. The initial results indicate that some patterns are more useful than others, but a more comprehensive evaluation is needed. The age of big data has given us an unprecedented number of patterns to use in our quest to understand process, but we know that this bounty comes with some difficulties. Volunteered data and data from sensor systems may not be representative samples of the study population. There is still much work to be done before we have a comprehensive understanding of how useful a pattern is for inferring process.
There is a long-standing debate over whether new roads unavoidably lead to environmental damage, especially forest loss, but causal identification has been elusive. Using multiple causal identification strategies, we study the construction of new rural roads to over 100,000 villages and the upgrading of 10,000 kilometers of national highways in India. The new rural roads had precise zero effects on local deforestation.
In contrast, the highway upgrades caused substantial forest loss, which appears to be driven by increased timber demand along the transportation corridors. In terms of forests, last mile connectivity had a negligible environmental cost, while expansion of major corridors had important environmental impacts.
Dr. Teevrat Garg is an assistant professor of economics at UC San Diego. He works on a variety of issues in economics, with a focus on applications to environmental problems in under-developed countries.
His current research projects include uncovering causal mechanisms that link ecosystem health to human health, with an emphasis on irrigation in rural communities in poor countries and the distributional consequences of adaptation to climate change. Prior to joining GPS, he completed a postdoctoral fellowship at the London School of Economics’ Grantham Institute for Climate Change and the Environment.