xNet+SC: Classifying Places Based on Images by Incorporating Spatial Contexts

With recent advancements in deep convolutional neural networks, researchers in geographic information science gained access to powerful models to address challenging problems such as extracting objects from satellite imagery. However, as the underlying techniques are essentially borrowed from other research fields, e.g., computer vision or machine translation, they are often not spatially explicit. In this paper, we demonstrate how utilizing the rich information embedded in spatial contexts (SC) can substantially improve the classification of place types from images of their facades and interiors. By experimenting with different types of spatial contexts, namely spatial relatedness, spatial co-location, and spatial sequence pattern, we improve the accuracy of state-of-the-art models such as ResNet - which are known to outperform humans on the ImageNet dataset - by over 40%. Our study raises awareness for leveraging spatial contexts and domain knowledge in general in advancing deep learning models, thereby also demonstrating that theory-driven and data-driven approaches are mutually beneficial

Visualization in spatial modeling

This chapter deals with issues arising from a central theme in contemporary computer modeling - visualization. We first tie visualization to varieties of modeling along the continuum from iconic to symbolic and then focus on the notion that our models are so intrinsically complex that there are many different types of visualization that might be developed in their understanding and implementation. This focuses the debate on the very way of 'doing science' in that patterns and processes of any complexity can be better understood through visualizing the data, the simulations, and the outcomes that such models generate. As we have grown more sensitive to the problem of complexity in all systems, we are more aware that the twin goals of parsimony and verifiability which have dominated scientific theory since the 'Enlightenment' are up for grabs: good theories and models must 'look right' despite what our statistics and causal logics tell us. Visualization is the cutting edge of this new way of thinking about science but its styles vary enormously with context. Here we define three varieties: visualization of complicated systems to make things simple or at least explicable, which is the role of pedagogy; visualization to explore unanticipated outcomes and to refine processes that interact in unanticipated ways; and visualization to enable end users with no prior understanding of the science but a deep understanding of the problem to engage in using models for prediction, prescription, and control. We illustrate these themes with a model of an agricultural market which is the basis of modern urban economics - the von Thünen model of land rent and density; a model of urban development based on interacting spatial and temporal processes of land development - the DUEM model; and a pedestrian model of human movement at the fine scale where control of such movements to meet standards of public safety is intrinsically part of the model about which the controllers know intimately. © Springer-Verlag Berlin Heidelberg 2006

Dynamics of urban sprawl

This paper introduces a framework for understanding the dynamics of urban growth,particularly the continuing problem of urban sprawl. The models we present are based on transitions from vacant land to established development. We propose that the essential mechanism of transition is analogous to the way an epidemic is generated within a susceptible population, with waves of development being generated from the conversion of available land to new development and redevelopment through the aging process. We first outline the standard aggregate model in differential equation form, showing how different variants (including logistic, exponential, predator-prey models) can be derived for various urban growth situations. We then generalize the model to a spatial system and show how sprawl can be conceived as a process of both interaction/reaction and diffusion. We operationalize the model as a cellular automata (CA) which implies that diffusion is entirely local, and we then illustrate how waves of development and redevelopment characterizing both sprawl and aging of the existing urban stock, can be simulated.Finally we show how the model can be adapted to a real urban situation - the AnnArbor area in Eastern Michigan - where we demonstrate how waves of development are absorbed and modified by particular historical contingencies associated with the re-existing urban structure

Modeling the Influence of Land Use Developments on Transportation System Performance

The growth in the urban population has influenced urban sprawl, congestion, and subsequently, delays on the existing road infrastructure. New land use developments occur in every part of the city due to rapid economic development and to meet the demand for better living standards. The induced traffic volume generated from such land use developments often results in increased congestion and vehicular delay on the existing roads. With recent advancements in the technology, it is possible to capture continuous, and comprehensive travel time data for every major corridor in a city. Therefore, the goal of this research is to model the influence of land use developments on travel time variations to improve the mobility of people and goods. Data for 259 road links were selected within the city of Charlotte, North Carolina (NC). Three years of travel time data, from the year 2013 to the year 2015, were collected from the private agency. Thirty-five different types of land use developments were considered in this research. The spatial dependency was incorporated by considering the land use developments within 0.5 miles, 1 mile, 2 miles, and 3 miles of the selected road link. Forty-eight statistical models were developed. The results obtained indicate that land use developments have a significant influence on travel times. Different land use categories contribute to the average travel time based on the buffer width, area type, and the link speed limit. Developing the models by classifying the links based on the speed limit (\u3c 45 mph, 45 to 50 mph, and \u3e 50 mph) was observed to be the best approach to examine the relationship between land use developments and the average travel time. Also, typically travel time on a selected road link is higher during the evening peak period compared to the morning peak and the afternoon off-peak period. Further, the results obtained indicate that the number of lanes and the posted speed limit are negatively associated with the travel time of the selected link

Tools for Assessing Climate Impacts on Fish and Wildlife

Climate change is already affecting many fish and wildlife populations. Managing these populations requires an understanding of the nature, magnitude, and distribution of current and future climate impacts. Scientists and managers have at their disposal a wide array of models for projecting climate impacts that can be used to build such an understanding. Here, we provide a broad overview of the types of models available for forecasting the effects of climate change on key processes that affect fish and wildlife habitat (hydrology, fire, and vegetation), as well as on individual species distributions and populations. We present a framework for how climate-impacts modeling can be used to address management concerns, providing examples of model-based assessments of climate impacts on salmon populations in the Pacific Northwest, fire regimes in the boreal region of Canada, prairies and savannas in the Willamette Valley-Puget Sound Trough-Georgia Basin ecoregion, and marten Martes americana populations in the northeastern United States and southeastern Canada. We also highlight some key limitations of these models and discuss how such limitations should be managed. We conclude with a general discussion of how these models can be integrated into fish and wildlife management