WORKING PAPERS"Measuring and Modeling Global Urban Expansion" by S. Sheppard, SILUS Working Paper 2006, Published in Birch and Wachter "Tracking Regional Growth and Development: The Nairobi Case" by Wilber K. Ottichilo, SILUS Working Paper 2007, Published in Birch and Wachter "Monitoring Urban Growth and Its Environmental Impacts Using Remote Sensing" by K. Seto, SILUS Working Paper 2006, Published in Birch and Wachter "The Urban Transition in Developing Countries: Demography Meets Geography" by M. Montgomery and D. Balk, SILUS Working Paper 2006, Published in Birch and Wachter "Natural Hazards Science: A Matter of Public Safety" by P. Leahy, SILUS Working Paper 2006, Published in Birch and Wachter "Green Investment Strategies: How they Matter for Neighborhoods" by S. Wachter, K. Gillen, and C. Brown, SILUS Working Paper 2007, Published in Birch and Wachter "What Is a Tree Worth? Green-City Strategies and Housing Prices" target="_blank" by S. Wachter and G. Wong, SILUS Working Paper 2007, Published in Real Estate Economics "Using Econometrics and Geographic Information Systems for Property Valuation: A Spatial Hedonic Pricing Model" by R. Bernknopf, K. Gillen, S. Wachter, and A. Wein, SILUS Working Paper 2008, Published in Linne and Thompson "A General Framework for Estimating the Benefits of Moderate Resolution Land Imagery in Environmental Application" by R. Bernknopf, W. Forney, R. Raunikar, and S. Mishra, SILUS Working Paper 2010, Published in Macauley and Laxminarayan "A Spatial Modeling Framework for Analyzing Potential Earthquake Damage: An Application to Memphis" by T. Smith, R. Bernknopf, and A. Wein, SILUS Working Paper 2008, Submitted to Risk Analysis "Technical Review of House Price Index Methodology" by C. Nagaraja, L. Brown, and S. Wachter, SILUS Working Paper 2010 "Remote Sensing Classification Procedure for Identifying Corn and Soybean Crops in Iowa with Landsat Imagery" by P. Amos, K. Steif, and S. Wachter, SILUS Working Paper 2010 "RDA Vacant Land Management in Philadelphia: The Costs of the Current System and the Benefits of Reform" by S. Wachter and K. Gillen, Press Release
PROJECTSResearch Ecosystem Portfolio Model for South Florida The South Florida Ecosystem Portfolio Model (EPM) prototype is a regional land-use planning Web tool that integrates ecological, economic, and social information and values of relevance to decision-makers and stakeholders. The EPM uses a multicriteria evaluation framework that builds on geographic information system-based (GIS) analysis and spatially-explicit models that characterize important ecological, economic, and societal endpoints and consequences that are sensitive to regional land-use/land-cover (LULC) change. The EPM uses both economics (monetized) and multiattribute utility (nonmonetized) approaches to valuing these endpoints and consequences. This hybrid approach represents a methodological middle ground between rigorous economic and ecological/ environmental scientific approaches. Land Use Portfolio Model: Earthquakes in Memphis Making decisions to reduce natural hazard losses from earthquakes relies on hazard assessments for a parcel, neighborhood, community, or region. Assessments are based on probabilistic estimates of damages; however, the projections of losses contain uncertainties that complicate mitigation policy decisions. The framework developed here extends the use of scientific information for damage estimation by incorporating spatial uncertainties about the earthquake source and local geologic conditions. Although a methodology exists for evaluating site specific probabilistic damages and loss, what is less understood is how to aggregate site information into a risk analysis for a portfolio of sites. Furthermore, empirical evidence indicates that shaking and liquefaction at sites close to one another tend to exhibit some degree of positive correlation. While such correlations do not affect the expected loss in a given study area, they do increase the variance of these losses. We demonstrate these effects in 1,200 land p arcels in Memphis, TN, by comparing simulated spatially independent and dependent losses resulting from a 7.7 magnitude earthquake. Repeated simulations on a regional scale yield a sampling distribution of total realized losses providing maximum-likelihood estimates for exceedance probability (EP) functions of loss. A value at risk (VaR) assessment based on these exceedance probabilities illustrates the effect of spatial uncertainty on a hypothetical mitigation plan decision in a Memphis neighborhood. A comparison of these assessment results for both spatially dependent and independent scenarios shows how a failure to account for unobserved spatial dependencies can lead to an underestimate of potential extreme damage and loss conditions. Landsat Benefit Analysis: Case Study in Nonpoint Source Pollution of Ground Water Resources Moderate resolution land imagery (MRLI) may be useful for a more complete assessment of the cumulative landscape-level effect of agricultural land use and land cover on environmental quality. If this improved assessment yields a net social benefit, then that benefit reflects the value of information (VOI) from MRLI. Environmental quality and the capacity to provide environmental services evolve because of human actions, changing natural conditions, and their interaction with natural physical processes. The human actions, in turn, are constrained and redirected by many institutions and regulations such as agriculture, energy, and environmental policies. A general framework for bringing together relevant processes (i.e. sociologic, biologic, physical, hydrologic, and geologic) at meaningful scales to interpret environmental implications of MRLI applications is presented. We set out a specific application using MRLI observations to identify crop planting and thus estimate surface management activities over a re gional landscape that influence groundwater resources. We tailor the application to the characteristics of nonpoint source groundwater pollution hazards in Iowa to illustrate the general framework in a land use-hydrologic-economic system. In the example, MRLI VOI derives from both reducing losses to agricultural production and reducing mitigation and treatment costs necessary to avoid human health and other consequences of contaminated groundwater. Measurement of Real Estate Value over Time and over Space: House Price Index Methodology This project examines house price index methodology and explores what makes an index both practical and representative. Two characteristics are investigated: predictive ability (quantitative) and index structure (qualitative). Five indices are analyzed. Four of these are repeat sales indices in the traditional sense. The fifth is an autoregressive index that makes use of the repeat sales idea but includes single sales as well. Each of these methods is applied to data from U.S. home sales in twenty metropolitan areas to assess predictive ability. The five indices tend to track each other over time. However, the differences are substantial enough to be of interest, and we find that the autoregressive index has the best predictive performance. Green Infrastructure: Measuring Value Although the importance of place-based investments is recognized, there is little empirical evidence directly quantifying their impact. The purpose of this study is to describe a methodology for quantifying the economic benefits of green investment and to use the methodology to measure gains from recently implemented green investment initiatives in the City of Philadelphia. The methodology, which deploys precise, time-based spatial data to identify when and where investment occurs, permits the identification and measurement of the neighborhood-level effects of public investment.
Mapping The National Atlas: The Addition of Housing Statistics SILUS is selecting and preparing appropriate housing statistics and will assist with the technical work required to include these housing variables in the National Atlas. The data that we are bringing to the National Atlas, in compliance with the Content Standard for Digital Geospatial Metadata, are compiled by and available from existing federal agencies (e.g. Census, FHFB/OFHEO, BLS). Specifically, data on housing stock, rents, prices, vacancy, and tenure (rental v. owner-occupied) are five housing factors that are critical to linking socioeconomic/demographic characteristics to housing outcomes. The inclusion of these variables, it is hoped, will provide citizens, social science researchers, planners, and other practitioners with information useful for conducting analyses relating to housing status and the interaction of housing with variables in other National Atlas categories. Spatial Projections for Population Growth in the U.S. The National Urban Growth Model began as a "Plan for America: 2050" studio in the School of Design at the University of Pennsylvania with the thought that the US Census Bureau had a valid projection for the American population in 2050. We realized that this number did not actually exist and we created our own methodology to forecast our population growth. We used data available from a private vendor - Woods and Poole - for population by county until 2025. We then made our own projections for 2050 based on the Woods & Poole information. Eventually, the Census did release a national population projection for 2050 and we were quite pleased to see that our model was within 3% of the Census' projection. The model was able to create a growth model that would show how metropolitan areas would grow until 2050 if no major policy changes were made. The model looked at where growth would occur in the US and found that the trend will continue to see population growth in coastal areas and decline in the central states. Land Use / Land Cover Census Model SILUS has developed a cross-domain research product, Land Use Land Cover by Census Tract (LULC-CT) to link physical and biologic descriptions of the earth with census socio-economic demographic data. The product enables the linkage of USGS land use land cover data to census data. LULC-CT, a database with software code, does this by computing the amount of land use per census tract for all land use types (using land size measured in square meters) based on land use land covered data by census tract developed by USGS over 1992 to 1996.