15,355 research outputs found

    ASSESSING THE JOINT INFLUENCE OF ECOLOGICAL AND SOCIOECONOMIC DETERMINANTS OF INCREASES IN THE BUILT-ENVIRONMENT: A STUDY OF TRENDS IN CENTRAL NORTH CAROLINA

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    This paper advances an empirical model assessing how, over both time and space, changes in land-use respond to changing economic and ecological conditions. Focusing on Central North Carolina, a region that has undergone extensive changes in forest cover and agricultural lands over the past two decades, landscape dynamics are modeled by exploiting a spatial database that links several satellite images spanning the years 1975-1999 to a suite of socioeconomic, institutional and GIS-created explanatory variables.Environmental Economics and Policy, Land Economics/Use,

    Cities and Satellites: Spatial Effects and Unobserved Heterogeneity in the Modeling of Urban Growth

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    The confluence of factors driving urban growth is highly complex, resulting from a combination of ecological and social determinants that co-evolve over time and space. Identifying these factors and quantifying their impact necessitates models that capture both why urbanization happens as well as where and when it happens. Using a database that links five satellite images spanning 1976–2001 to a suite of socioeconomic, ecological and GIS created explanatory variables, this study develops a spatial-temporal model of the determinants of built-up area across a 25,900 square kilometer swath across central North Carolina. Extensive conversion of forest and agricultural land over the last decades is modeled using the complementary log-log derivation of the proportional hazards model, thereby affording a means for modeling continuous- time landscape change using discrete-time satellite data. To control for unobserved heterogeneity, the model specification includes an error component that is Gamma distributed. Results confirm the hypothesis that the landscape pattern surrounding a pixel has a major influence on the likelihood of its conversion and, moreover, that the omission of external spatial effects can lead to biased inferences regarding the influence of other covariates, such as proximity to road. Cartographic and nonparametric validation exercises illustrate the utility of the model for policy simulation.Urban growth, landscape pattern, satellite imagery, hazard model,North Carolina

    Incorporating Spatial Complexity into Economic Models of Land Markets and Land Use Change

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    Recent work in regional science, geography, and urban economics has advanced spatial modeling of land markets and land use by incorporating greater spatial complexity, including multiple sources of spatial heterogeneity, multiple spatial scales, and spatial dynamics. Doing so has required a move away from relying solely on analytical models to partial or full reliance on computational methods that can account for these added features of spatial complexity. In the first part of the paper, we review economic models of urban land development that have incorporated greater spatial complexity, focusing on spatial simulation models with spatial endogenous feedbacks and multiple sources of spatial heterogeneity. The second part of the paper presents a spatial simulation model of exurban land development using an auction model to represent household bidding that extends the traditional Capozza and Helsley (1990) model of urban growth to account for spatial dynamics in the form of local land use spillovers and spatially heterogeneous land characteristics.urban growth, urbanization, land development, spatial dynamics, heterogeneity, agent-based models, spatial interactions, Land Economics/Use, Research Methods/ Statistical Methods,

    Local Industrial Structures and Female Entrepreneurship in India

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    We analyze the spatial determinants of female entrepreneurship in India in the manufacturing and services sectors. We focus on the presence of incumbent female-owned businesses and their role in promoting higher subsequent female entrepreneurship relative to male entrepreneurship. We find evidence of agglomeration economies in both sectors, where higher female ownership among incumbent businesses within a district-industry predicts a greater share of subsequent entrepreneurs will be female. Moreover, higher female ownership of local businesses in related industries (e.g., those sharing similar labor needs, industries related via input-output markets) predict greater relative female entry rates even after controlling for the focal district-industry’s conditions. The core patterns hold when using local industrial conditions in 1994 to instrument for incumbent conditions in 2000-2005. The results highlight that the traits of business owners in incumbent industrial structures influence the types of entrepreneurs supported.

    Mining large-scale human mobility data for long-term crime prediction

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    Traditional crime prediction models based on census data are limited, as they fail to capture the complexity and dynamics of human activity. With the rise of ubiquitous computing, there is the opportunity to improve such models with data that make for better proxies of human presence in cities. In this paper, we leverage large human mobility data to craft an extensive set of features for crime prediction, as informed by theories in criminology and urban studies. We employ averaging and boosting ensemble techniques from machine learning, to investigate their power in predicting yearly counts for different types of crimes occurring in New York City at census tract level. Our study shows that spatial and spatio-temporal features derived from Foursquare venues and checkins, subway rides, and taxi rides, improve the baseline models relying on census and POI data. The proposed models achieve absolute R^2 metrics of up to 65% (on a geographical out-of-sample test set) and up to 89% (on a temporal out-of-sample test set). This proves that, next to the residential population of an area, the ambient population there is strongly predictive of the area's crime levels. We deep-dive into the main crime categories, and find that the predictive gain of the human dynamics features varies across crime types: such features bring the biggest boost in case of grand larcenies, whereas assaults are already well predicted by the census features. Furthermore, we identify and discuss top predictive features for the main crime categories. These results offer valuable insights for those responsible for urban policy or law enforcement

    Valuing Natural Space and Landscape Fragmentation in Richmond, VA

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    Hedonic pricing methods and GIS (Geographic Information Systems) were used to evaluate relationships between sale price of single family homes and landscape fragmentation and natural land cover. Spatial regression analyses found that sale prices increase as landscapes become less fragmented and the amount of natural land cover around a home increases. The projected growth in population and employment in the Richmond, Virginia region and subsequent increases in land development and landscape fragmentation presents a challenge to sustaining intact healthy ecosystems in the Richmond region. Spatial regression analyses helped illuminate how land cover patterns influence sale prices and landscape patterns that are economically and ecologically advantageous

    How user throughput depends on the traffic demand in large cellular networks

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    Little's law allows to express the mean user throughput in any region of the network as the ratio of the mean traffic demand to the steady-state mean number of users in this region. Corresponding statistics are usually collected in operational networks for each cell. Using ergodic arguments and Palm theoretic formalism, we show that the global mean user throughput in the network is equal to the ratio of these two means in the steady state of the "typical cell". Here, both means account for double averaging: over time and network geometry, and can be related to the per-surface traffic demand, base-station density and the spatial distribution of the SINR. This latter accounts for network irregularities, shadowing and idling cells via cell-load equations. We validate our approach comparing analytical and simulation results for Poisson network model to real-network cell-measurements
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