Danlin Yu

A system dynamic modeling approach for evaluating municipal solid waste generation, landfill capacity and related cost management issues.

As planning for sustainable municipal solid waste management has to address several inter-connected issues such as landfill capacity, environmental impacts and financial expenditure, it becomes increasingly necessary to understand the dynamic nature of their interactions. A system dynamics approach designed here attempts to address some of these issues by fitting a model framework for Newark urban region in the US, and running a forecast simulation. The dynamic system developed in this study incorporates the complexity of the waste generation and management process to some extent which is achieved through a combination of simpler sub-processes that are linked together to form a whole. The impact of decision options on the generation of waste in the city, on the remaining landfill capacity of the state, and on the economic cost or benefit actualized by different waste processing options are explored through this approach, providing valuable insights into the urban waste-management process.

Tobacco outlet density and demographics: analysing the relationships with a spatial regression approach.

OBJECTIVE Studies of relationships between tobacco sales and socio-economic/sociodemographic characteristics are well documented. However, when analysing the data that are collected on geographic areas, the spatial effects are seldom considered, which could lead to potential misleading analytical results. This study addresses this concern by applying the spatial analysis method in studying how socio-economic factors and tobacco outlet density are related in New Jersey, USA. STUDY DESIGN A spatial regression method applied to tobacco outlet and socio-economic data obtained in 2004 in New Jersey, USA. METHOD This study assessed the association between tobacco outlet density and three demographic correlates - income, race and ethnicity - at the tract level of analysis for one state in the north-eastern USA. Data for 1938 residential census tracts in the state of New Jersey were derived from 2004 licences for 13,984 tobacco-selling retail outlets. Demographic variables were based on 2000 census data. When applying a regression model, the residuals of an ordinary least squared (OLS) estimation were found to exhibit strong spatial autocorrelation, which indicates that the estimates from the OLS model are biased and inferences based on the estimates might be misleading. A spatial lag model was employed to incorporate the potential spatial effects explicitly. RESULTS Agreeing with the OLS residual autocorrelation test, the spatial lag model yields a significant coefficient of the added spatial effect, and fits the data better than the OLS model. In addition, the residuals of the spatial regression model are no longer autocorrelated, which indicates that the analysis produces more reliable results. More importantly, the spatial regression results indicate that tobacco companies attempt to promote physical availability of tobacco products to geographic areas with disadvantageous socio-economic status. In New Jersey, the percentage of Hispanics seems to be the dominant demographic factor associated with tobacco outlet distribution, followed by median household income and percentage of African Americans. CONCLUSION This research applied a spatial analytical approach to assess the association between tobacco outlet density and sociodemographic characteristics in New Jersey at the census tract level. The findings support the common wisdom in the public health research domain that tobacco outlets are more densely distributed in socio-economically disadvantaged areas. However, incorporating the spatial effects explicitly in the analysis provides less biased and more reliable results than traditional methods.

Modeling Spatial Dimensions of Housing Prices in Milwaukee, WI

In this study we investigate spatial dimensions of housing-market dynamics in the City of Milwaukee by modeling the determinants of housing prices. From the 2003 Master Property data file of the city, two sets of owner-occupied single-family houses were randomly selected (one to construct the models, and the other to rest the models). Besides conventional housing attributes, remote-sensing information, in particular the fractions of soil and impervious surface representing degraded neighborhood environment conditions, is added to improve the model. Spatial regression and geographically weighted regression approaches are employed to examine spatial dependence and heterogeneity. Results reveal that these spatial models tend to perform better, especially in terms of model performance and predictive accuracy, than the ordinary least squares estimates.

Analyzing Regional Inequality in Post-Mao China in a GIS Environment

Regional inequality in China has attracted considerable scholarly attention, but the use of geographic information system (GIS) techniques for rigorous analysis remains limited. This paper utilizes recent data and GIS and spatial statistical techniques to analyze changing patterns of regional inequality in China from 1978 to 2000. It also identifies the changing clusters of regional development in China. We illustrate that regional inequality in China is sensitive to development trajectories of the provinces, and that conventional measures of regional inequality mask geographical clustering. Patterns of change are explained by both contextual and regression analyses. Journal of Economic Literature, Classification Numbers: F21, G32, P31. 16 figures, 3 tables, 30 references.

Understanding Population Segregation from Landsat ETM+ Imagery: A Geographically Weighted Regression Approach

This study attempts to understand population segregation issues in Milwaukee County, Wisconsin utilizing remote sensing and regression technologies. Population segregation was measured with a local segregation index Di based on the theory of the index of dissimilarity. Remote sensing information was extracted from a Landsat ETM+ image through spectral mixture analysis, unsupervised classification, and texture analysis. Global ordinary least squares (OLS) regression and geographically weighted regression (GWR) analyses were applied to explore the relationships between population segregation and remote sensing variables. Results indicate that remote sensing information has the potential to increase our understanding of socio-cultural issues such as population segregation.

Modeling Owner-Occupied Single-Family House Values in the City of Milwaukee: A Geographically Weighted Regression Approach

This study investigates the spatial non-stationarity of the relationship between house values and various attributes in the City of Milwaukee. From the 2003 Master Property (MPROP) data file of the City of Milwaukee, a set of owner-occupied single family houses were randomly selected (representing 99% of confidence within a ±2% range of accuracy of the total population) to model how house values are related to various house attributes. Remote sensing information (the fraction of soil and impervious surface that represent degraded neighborhood environmental conditions) is added to fine-tune the relationship. A geographically weighted regression (GWR) approach is used to investigate spatial non-stationarity. The modeling revealed that significant spatial non-stationarity existed between house values and the predictors. Specifically, the study found that those house attributes—including floor size, number of bathrooms, air conditioners, and fire-places—add more value to houses in the more affluent areas (especially on the east side near Lake Michigan and in suburban areas) than in the relatively poor areas. In addition, older houses in the historical area are more expensive, which differs from other areas. Environmental conditions, though expected to have a negative impact on house values in most areas, did not affect house values in the historical area.

Incorporating Remote Sensing Information in Modeling House Values: A Regression Tree Approach

This paper explores the possibility of incorporating remote sensing information in modeling house values in the City of Milwaukee, Wisconsin, U.S.A. In particular, a Landsat ETMimage was utilized to derive environmental character- istics, including the fractions of vegetation, impervious surface, and soil, with a linear spectral mixture analysis approach. These environmental characteristics, together with house structural attributes, were integrated to house value models. Two modeling techniques, a global OLS regression and a regression tree approach, were employed to build the relationship between house values and house structural and environmental characteristics. Analysis of results indicates that environmental characteristics gener- ated from remote sensing technologies have strong influ- ences on house values, and the addition of them improves house value modeling performance significantly. Moreover, the regression tree model proves as a better alternative to the OLS regression models in terms of predicting accuracy. In particular, based on the testing dataset, the mean average error (MAE) and relative error (RE) dropped from 0.202 and 0.434 for the OLS model to 0.134 and 0.280 for the regression tree model, while the correlation coefficient between the predicted and observed values increased from 0.903 to 0.960. Further, as a nonparametric and local model, the regression tree method alleviates the problems with the OLS techniques and provides a means in delineating urban housing submarkets.

Tobacco outlet density and demographics at the tract level of analysis in New Jersey: A statewide analysis

Aim: Geographic relationships between tobacco outlet density and demographics were examined at the tract level in New Jersey, a Northeastern US state. Method: Data for 1938 residential census tracts were analyzed. The 2000 TIGER/Line files were used to geocode addresses of licensed tobacco-selling retail outlets. Median income, percent African-American residents, and percent Hispanic residents were based on year 2000 census data. Address matching with ArcGIS® resulted in successful geocoding of 13,984 (93.1%) outlets. Findings: Results showed that outlet density was significantly related with demographics. Tracts with greater density of tobacco outlets tended to have lower median household income and higher percentages of African-American or Hispanic residents. Cluster analysis of tracts resulted in a three-cluster solution, identifying high, medium and low areas of disparity. The high disparity area was characterized by tracts with the highest tobacco outlet density, the highest percentages of African-American and Hispanic residents, the lowest percentage of white residents, and the lowest median income. Further analysis showed that while there were significant associations between tobacco outlet density and all three demographic variables across the state, such associations varied in each of the three clusters. Conclusions: Results may be used to inform strategic planning and policy decisions on a statewide basis.

Modeling Urban Growth Using GIS and Remote Sensing

Based on remote sensing and GIS, this study models the spatial variations of urban growth patterns with a logistic geographically weighted regression (GWR) technique. Through a case study of Springfield, Missouri, the research employs both global and local logistic regression to model the probability of urban land expansion against a set of spatial and socioeconomic variables. The logistic GWR model significantly improves the global logistic regression model in three ways: (1) the local model has higher PCP (percentage correctly predicted) than the global model; (2) the local model has a smaller residual than the global model; and (3) residuals of the local model have less spatial dependence. More importantly, the local estimates of parameters enable us to investigate spatial variations in the influences of driving factors on urban growth. Based on parameter estimates of logistic GWR and using the inverse distance weighted (IDW) interpolation method, we generate a set of parameter surfaces to reveal the spatial variations of urban land expansion. The geographically weighted local analysis correctly reveals that urban growth in Springfield, Missouri is more a result of infrastructure construction, and an urban sprawl trend is observed from 1992 to 2005.

Sustainable Urban Redevelopment: Assessing the Impact of Third-Party Rating Systems

AbstractPrioritization of urban redevelopment to achieve sustainable neighborhood revitalization has received considerable attention. This study investigates whether a prescriptive approach to urban development, the third-party rating system, coupled with a business intelligence dashboard, as a data visualization tool to display the status of redevelopment can provide feasible and intuitive integration of data in which to prioritize redevelopment. This study presents a new framework and key sustainability indicators based on existing third-party rating systems to prioritize redevelopment. These assessments are introduced into a spatial decision support system using a dashboard as an interactive tool to gather and consolidate data and to present an evaluative means for decision makers. The tool allows identification of the highest-priority sites for long-term and short-term redevelopment of properties in Paterson, New Jersey. The study attempts to advance knowledge in sustainable urban redevelopment throug...