GIS-based Spatial Modeling Research Articles

Mapping and Modelling Malaria Risk Areas Using Climate, Socio-Demographic and Clinical Variables in Chimoio, Mozambique.

Background: Malaria continues to be a major public health concern in Africa. Approximately 3.2 billion people worldwide are still at risk of contracting malaria, and 80% of deaths caused by malaria are concentrated in only 15 countries, most of which are in Africa. These high-burden countries have achieved a lower than average reduction of malaria incidence and mortality, and Mozambique is among these countries. Malaria eradication is therefore one of Mozambique’s main priorities. Few studies on malaria have been carried out in Chimoio, and there is no malaria map risk of the area. This map is important to identify areas at risk for application of Public Precision Health approaches. By using GIS-based spatial modelling techniques, the research goal of this article was to map and model malaria risk areas using climate, socio-demographic and clinical variables in Chimoio, Mozambique. Methods: A 30 m × 30 m Landsat image, ArcGIS 10.2 and BioclimData were used. A conceptual model for spatial problems was used to create the final risk map. The risks factors used were: the mean temperature, precipitation, altitude, slope, distance to water bodies, distance to roads, NDVI, land use and land cover, malaria prevalence and population density. Layers were created in a raster dataset. For class value comparisons between layers, numeric values were assigned to classes within each map layer, giving them the same importance. The input dataset were ranked, with different weights according to their suitability. The reclassified outputs of the data were combined. Results: Chimoio presented 96% moderate risk and 4% high-risk areas. The map showed that the central and south-west “Residential areas”, namely, Centro Hipico, Trangapsso, Bairro 5 and 1° de Maio, had a high risk of malaria, while the rest of the residential areas had a moderate risk. Conclusions: The entire Chimoio population is at risk of contracting malaria, and the precise estimation of malaria risk, therefore, has important precision public health implications and for the planning of effective control measures, such as the proper time and place to spray to combat vectors, distribution of bed nets and other control measures.

Pet- and special needs-friendly shelter planning in south florida: A spatial capacitated p-median-based approach

Evacuating individuals and allocating space in shelters are complex tasks that depend on disaster characteristics, roadway network characteristics, the demographics and socioeconomic status of people in the affected region, as well as the effectiveness of the emergency plans. This problem becomes even more challenging when special needs (access and functional needs) populations and those with pets are considered since they may require more time in the event of an evacuation along with more space in shelters. As such, this paper focuses on the evacuation and sheltering needs of such a population group, those 85 and older with special needs (access and functional needs) or pets, in Southeast Florida. Data from Miami-Dade and Broward counties are analyzed using a GIS-based spatial capacitated p-median optimization model. First, an extensive analysis is conducted to assess the accessibility of the American Red Cross-approved shelters with respect to different population groups through which static congested travel times are obtained between each shelter location and U.S. census units. Additionally, a variety of scenarios are constructed to simulate evacuating 85+ populations living in the evacuation zone, providing dynamic congested travel times. Finally, a capacitated p-median optimization model is implemented to maximize the accessibility and capacity of the existing shelters for 85+ populations with special needs (access and functional needs) or pets. Results indicate that the location and allocations of people to shelters are sensitive to demographics and roadway conditions, and the accessibility to shelters can be improved for this group of people based on optimization model results.

The use of urban spatial scenario design model as a strategic planning tool for Addis Ababa

Urban population growth and expansion of settlement areas are among the major challenges that African cities are facing. Addis Ababa's settlement area has been expanding into the city's peripheral area at the expense of losing green infrastructure (GI) of farmland and vegetated areas. The protection of GI is impeded by the lack of foresight information and tools to support urban planning. Therefore, a GIS-based urban spatial scenario design model (USSDM) was applied for modelling the settlement expansion in Addis Ababa. A business as usual scenario (BAU) and a densification scenario (DENS) were modelled to evaluate the impact of population density on the green infrastructure and the implications of excluding settlement development from flood prone areas. Training and workshops were conducted on the use of USSDM in the Addis Ababa master plan review. The results of our study indicated that increasing population density from 166 to 350 inhabitants per hectare would almost halve the losses of the green infrastructure. Moreover, the settlement development in the densification scenario would be located closer to the vicinity of the built-up area rather than spreading along the eastern part of the city, which is currently occupied by farmland in the case of BAU. Densification would also slow down expansion of settlements in river corridors but might expose more inhabitants to flood hazards. USSDM is one of few models in Africa that is designed towards application by urban planners as a tool of assessing the impact of urban development strategies on the surrounding environment.

Identifying ecosystem service hotspots for targeting land degradation neutrality investments in south-eastern Africa

Land degradation response actions need motivated stakeholders and investments to improve land management. In this study we present methods to prioritise locations for degradation mitigation investments based on stakeholder preferences for ecosystem services. We combine participatory and spatial modelling approaches and apply these for Zambia, South Africa, and Tanzania to: i) prioritise ecosystem services in each country; ii) to map the supply of these ecosystem services in each country, and; iii) prioritise areas important for investment for the continuous delivery of these ecosystem services based on their vulnerability to land degradation. We interviewed 31 stakeholders from governmental and non-governmental organizations to select the most important ecosystem services per county. Stakeholders were also asked to indicate on national maps the hotspots of these ecosystem services and locations with a high degradation risk. We then assessed the supply of the stakeholder-selected ecosystem services and land degradation risk using GIS-based spatial models. We found that for each country the spatial extent and magnitude of ecosystem services supply and land degradation based on GIS data coincides with stakeholder knowledge in some locations. In the context of supporting national level policy to achieve land degradation neutrality as proposed by the United Nations Convention to Combat Desertification we argue that the correct representation, the level of acceptance, and use of modelled outputs to support decisions will be greater when model outputs are corroborated by stakeholder knowledge. Ecosystem services that are identified as “important” by diverse stakeholder groups have a broader level of awareness and could therefore drive motivations, commitments, and actions towards improved land management, contributing to land degradation neutrality.

GIS-based landslide spatial modeling in Ganzhou City, China

Landslide susceptibility mapping is among the first works for disaster management and land use planning activities in a mountain area like Ganzhou City. The aims of the current study are to assess GIS-based landslide spatial modeling using four models, namely data-driven evidential belief function (EBF), frequency ratio (FR), maximum entropy (Maxent), and logistic regression (LR), and to compare their performances. At first, a landslide inventory map was prepared according to aerial photographs, satellite images, and extensive field surveys. In total, 3971 landslide events were recognized in the study area that used 2979 landslides (75 %) for modeling and 992 landslide events (25 %) for validation. In the next step, the landslide-conditioning factors, namely slope angle, slope aspect, altitude, plan curvature, profile curvature, topographic wetness index (TWI), slope-length (LS), lithology, normalized difference vegetation index (NDVI), distance from rivers, distance from faults, distance from roads, and rainfall, were derived from the spatial database. Finally, landslide susceptibility maps of Ganzhou City were mapped in ArcGIS based on EBF, FR, Maxent, and LR approaches and were validated using the receiver operating characteristic (ROC) curve. The ROC plot assessment results showed that in the landslide susceptibility maps using the EBF, FR, Maxent, and LR models, the area under the curve (AUC) values were 0.7367, 0.7789, 0.7903, and 0.8237, respectively. Therefore, it can be concluded that all four models have AUC values of more than 0.70 and can be used in landslide susceptibility mapping in the study area. Also, the LR model had the best performance in the current study. Meanwhile, the mentioned models (EBF, FR, Maxent, and LR) showed almost similar results. The resultant susceptibility maps produced in the current study can be useful for land use planning and hazard mitigation purposes in the study area.

GIS-based Association Between PM10 and Allergic Diseases in Seoul: Implications for Health and Environmental Policy.

PurposeThe role of PM10 in the development of allergic diseases remains controversial among epidemiological studies, partly due to the inability to control for spatial variations in large-scale risk factors. This study aims to investigate spatial correspondence between the level of PM10 and allergic diseases at the sub-district level in Seoul, Korea, in order to evaluate whether the impact of PM10 is observable and spatially varies across the subdistricts.MethodsPM10 measurements at 25 monitoring stations in the city were interpolated to 424 sub-districts where annual inpatient and outpatient count data for 3 types of allergic diseases (atopic dermatitis, asthma, and allergic rhinitis) were collected. We estimated multiple ordinary least square regression models to examine the association of the PM10 level with each of the allergic diseases, controlling for various sub-district level covariates. Geographically weighted regression (GWR) models were conducted to evaluate how the impact of PM10 varies across the sub-districts.ResultsPM10 was found to be a significant predictor of atopic dermatitis patient count (P<0.01), with greater association when spatially interpolated at the sub-district level. No significant effect of PM10 was observed on allergic rhinitis and asthma when socioeconomic factors were controlled for. GWR models revealed spatial variation of PM10 effects on atopic dermatitis across the sub-districts in Seoul. The relationship of PM10 levels to atopic dermatitis patient counts is found to be significant only in the Gangbuk region (P<0.01), along with other covariates including average land value, poverty rate, level of education and apartment rate (P<0.01).ConclusionsOur findings imply that PM10 effects on allergic diseases might not be consistent throughout Seoul. GIS-based spatial modeling techniques could play a role in evaluating spatial variation of air pollution impacts on allergic diseases at the sub-district level, which could provide valuable guidelines for environmental and public health policymakers.

Creating GIS-based spatial interaction models for retail centres in Jeddah City

Spatial interaction models are used today in facilities planning research for predicting and for allocating flows of demand between origin and destination areas based on the attractiveness of each facility and based on the distance between facilities and demand areas. These models have been adapted to a wide range of application areas including predicting flows of people to shops, offices, schools, and hospitals. The aim of this paper is to use GIS for producing spatial interaction models for two retail centres Jeddah City, Saudi Arabia. These models are created using ArcGIS software and using the interaction function which is available within the network analysis module. To produce these models, detailed geo-database was created that covers location of retail centres, the capacity of each centre, the size of centres demand at the study area, and road network coverage for Jeddah City. The created models can be used by city planners for identifying areas of the city that are poorly served by existing retail centres. In addition, these models can be used to define the impacts of expanding retail supply and or retail demand at the study area.

Predicting the Spatial Distribution of King Rails in an Agricultural Landscape

Species distribution modeling is valuable for assessing rare or elusive species and in landscapes where accessibility is limited, such as agricultural lands. While birds' local habitat associations are often well known, knowledge of broad-scale relationships and the spatial patterns of environmental factors is generally lacking. In this study, we develop and validate a predictive habitat model for the King Rail (Rallus elegans) in the rice-growing region of southwestern Louisiana and identify important areas for this elusive bird. In a previous localized study we found it to be associated with canopy cover and ditches/streams around the perimeter of rice fields. Here, we used a geographic information system (GIS) to extrapolate these habitat associations to the region (>940 000 ha). We determined the proportion of rice fields within a 5-km radius and quantified canopy cover and ditches within a 1-km radius. In repeated call-back surveys from 2007 to 2009 we recorded King Rails at 44 of 155 locations. On this basis, accounting for imperfect detection, we modeled King Rail occurrence. The validation results showed the model had good predictive ability, and in 17% of the area the predicted probability of occurrence was high. Accounting for imperfect detection resulted in an increased probability of occurrence. Most importantly, the spatial pattern revealed the threat of encroaching woody vegetation and the vulnerability of rail habitat to hurricanes and further land-use changes. Our study illustrates that field research can be extrapolated to GIS-based spatial models, which can identify key spatial patterns in a species' distribution.

Predicting the Distribution and Ecological Niche of Unexploited Snow Crab (Chionoecetes opilio) Populations in Alaskan Waters: A First Open-Access Ensemble Model

Populations of the snow crab (Chionoecetes opilio) are widely distributed on high-latitude continental shelves of the North Pacific and North Atlantic, and represent a valuable resource in both the United States and Canada. In US waters, snow crabs are found throughout the Arctic and sub-Arctic seas surrounding Alaska, north of the Aleutian Islands, yet commercial harvest currently focuses on the more southerly population in the Bering Sea. Population dynamics are well-monitored in exploited areas, but few data exist for populations further north where climate trends in the Arctic appear to be affecting species' distributions and community structure on multiple trophic levels. Moreover, increased shipping traffic, as well as fisheries and petroleum resource development, may add additional pressures in northern portions of the range as seasonal ice cover continues to decline. In the face of these pressures, we examined the ecological niche and population distribution of snow crabs in Alaskan waters using a GIS-based spatial modeling approach. We present the first quantitative open-access model predictions of snow-crab distribution, abundance, and biomass in the Chukchi and Beaufort Seas. Multi-variate analysis of environmental drivers of species' distribution and community structure commonly rely on multiple linear regression methods. The spatial modeling approach employed here improves upon linear regression methods in allowing for exploration of nonlinear relationships and interactions between variables. Three machine-learning algorithms were used to evaluate relationships between snow-crab distribution and environmental parameters, including TreeNet, Random Forests, and MARS. An ensemble model was then generated by combining output from these three models to generate consensus predictions for presence-absence, abundance, and biomass of snow crabs. Each algorithm identified a suite of variables most important in predicting snow-crab distribution, including nutrient and chlorophyll-a concentrations in overlying waters, temperature, salinity, and annual sea-ice cover; this information may be used to develop and test hypotheses regarding the ecology of this species. This is the first such quantitative model for snow crabs, and all GIS-data layers compiled for this project are freely available from the authors, upon request, for public use and improvement.

Spatial modelling of sprinkler irrigation suitability in a Central Brazilian Cerrado region

Sprinkler irrigation, an agricultural production system that is causing increasing conflict among water resource users, is expanding quickly in the Central Western Cerrado regions of Brazil. To subsidize watershed management and concession of water rights, GIS-based spatial modelling was applied to spatially predict relative likelihood of the installation of centre sprinkler irrigation systems. Interpretation of multitemporal Landsat Thematic Mapper and Enhanced Thematic Mapper imagery was conducted to map spatial distribution of centre-pivot sprinkler systems. Multi-source data layers on environmental conditions and infrastructure were elaborated to test their predictive power in an Ecological Niche Factor Analysis, a spatial modelling technique for presence-only data. Underpinned by an exploratory analysis of spatial autocorrelation of irrigation systems, suitability predictions were found to be accurate on landscape scale and improved when the model includes underlying ecogeographical factors (EGV) such as farming suitability, soil groupings and distance to the hydrographic network and a density layer of existing irrigations.

TMDL Implementation in Agricultural Landscapes: A Communicative and Systemic Approach

Increasingly, total maximum daily load (TMDL) limits are being defined for agricultural watersheds. Reductions in non-point source pollution are often needed to meet TMDL limits, and improvements in management of annual crops appear insufficient to achieve the necessary reductions. Increased adoption of perennial crops and other changes in agricultural land use also appear necessary, but face major barriers. We outline a novel strategy that aims to create new economic opportunities for land-owners and other stakeholders and thereby to attract their voluntary participation in land-use change needed to meet TMDLs. Our strategy has two key elements. First, focused efforts are needed to create new economic enterprises that capitalize on the productive potential of multifunctional agriculture (MFA). MFA seeks to produce a wide range of goods and ecosystem services by well-designed deployment of annual and perennial crops across agricultural landscapes and watersheds; new revenue from MFA may substantially finance land-use change needed to meet TMDLs. Second, efforts to capitalize on MFA should use a novel methodology, the Communicative/Systemic Approach (C/SA). C/SA uses an integrative GIS-based spatial modeling framework for systematically assessing tradeoffs and synergies in design and evaluation of multifunctional agricultural landscapes, closely linked to deliberation and design processes by which multiple stakeholders can collaboratively create appropriate and acceptable MFA landscape designs. We anticipate that application of C/SA will strongly accelerate TMDL implementation, by aligning the interests of multiple stakeholders whose active support is needed to change agricultural land use and thereby meet TMDL goals.

Climatic regionalization mapping of the Murrumbidgee Irrigation Area, Australia

In this study, spatial analysis techniques were used to generate climatic zoning maps of the East Murrumbidgee Irrigation Area (MIA), Australia. Long term climate data were used to analyze and assess regional climatic variation by cluster analysis in a GIS environment. Based on thermal and moisture factors, four variables were chosen to develop climate zones. These variables include annual average rainfalls, annual average ETc, annual average Growing Day Degree (GDD) and daily average temperature generated from daily climate data of 36 years (1971–2006) on 12 local weather stations as the fundamental elements of regional climate characteristics. GIS-based spatial analysis models are used to map climate sub-regions from sets of climatic parameters derived from regional climate data sources. Two climate zoning schemes are presented by integration of spatial interpolation, spatial cluster analysis and climate indices methods. One scheme comes with the two climate zones of the West and the East based on spatial cluster analysis according to selected climatic variables, and the other comes with the three comprehensive hydro-thermal zones and the six hydro-thermal balance zones.

The Relationship of Incident Diabetes with Chronic Particulate Matter Exposures

ISEE-0471 Background and Objective: Information is lacking as to whether Diabetes Mellitus (DM) merely modifies the impact of air pollution exposures on cardiovascular outcomes or whether it is also an air pollution associated outcome. Using two prospective cohorts, the Nurses' Health Study (NHS) and the Health Professionals Follow-Up Study (HPFS), we investigated the relationship of incident DM with chronic exposures to PM10 (particulate matter less than 10 microns in diameter), PM2.5 (particulate matter less than 2.5 microns in diameter), and PM10-2.5 (particulate matter between 2.5 and 10 microns in diameter). Methods: Cases were initially reported through biennial questionnaires and confirmed by supplemental questionnaire. During the follow-up from 1992 to 2002, questionnaires also provided information on time-varying covariates and updated addresses which were geocoded and used to assign air pollution exposures that were estimated with GIS-based spatial statistical models. Results: Among participants living in metropolitan areas of the northeastern and midwestern US, there were 4,073 incident cases of DM in the NHS, and 646 cases in the HPFS. Among women, DM was significantly associated with increases in each size fraction of PM averaged over the 12 months prior to diagnosis in single pollutant time and age adjusted models. Risk of DM was also elevated for men. The greatest risk was related to PM2.5 exposure (HR for women: 1.32; 95%CI: 1.14-1.51; HR for men: 1.36; 95%CI: 0.98, 1.87). After adjustment for known risk factors for DM, risks remained elevated but were considerably attenuated. Conclusion: Using time-varying measures of covariates and long-term exposures, we found particulates were associated with increased risks of DM among women and men living in the northeastern and midwestern US. This relationship was confounded by known risk factors for DM.

`Optimal' Accessibility Landscapes? Development of a New Methodology for Simulating and Assessing Jobs—Housing Relationships in Urban Regions

Research into land use—transport relationships through the lens of the jobs—housing balance and the closely related excess commuting framework continues to draw substantial interdisciplinary attention. There have been several recent research efforts aimed at extending the excess commuting framework and its GIS-based spatial models to more prescriptive, policy-relevant situations. This paper puts forward the idea of a theoretical `optimal' urban jobs—housing balance and proposes a new spatial model for finding it. The developed model treats the region's theoretical minimum commute as a baseline indicator of the jobs—housing balance. Alternative patterns of workers and jobs are simulated in order to improve this indicator. The model is demonstrated in several scenarios using data from the decennial US census (2000). Results demonstrate the model's capability for finding `optimal' spatial distributions of jobs and housing, as well as pointing out inefficiencies in existing urban structure. Summary remarks and suggestions for future research are provided.

GIS-based spatial analysis and modeling for landslide hazard assessment: A case study in upper Minjiang River basin

By analyzing the topographic features of past landslides since 1980s and the main land-cover types (including change information) in landslide-prone area, modeled spatial distribution of landslide hazard in upper Minjiang River Basin was studied based on spatial analysis of GIS in this paper. Results of GIS analysis showed that landslide occurrence in this region closely related to topographic feature. Most areas with high hazard probability were deep-sheared gorge. Most of them in investigation occurred assembly in areas with elevation lower than 3 000 m, due to fragile topographic conditions and intensive human disturbances. Land-cover type, including its change information, was likely an important environmental factor to trigger landslide. Destroy of vegetation driven by increase of population and its demands augmented the probability of landslide in steep slope.

An IT and GIS exploration of web-based nature tourism enterprises in the rural agricultural sector in Texas

This paper addresses the role of IT in facilitating economic diversification for rural agricultural enterprises in Texas. The development of Texas INFRONT, an innovative database that acts as a clearinghouse of information for enterprises, is described. GIS mapping and analysis was applied as an innovative investigative tool to examine the relationship between a changing economic environment and the rise of online nature tourism businesses by rural producers. Spatial analysis for clusters and dispersions of nature tourism businesses using the internet was conducted. This revealed a variety of phenomena such as the concentration of nature tourism enterprises around urban centres. The results indicate that GIS-based spatial modelling informed by theoretical frameworks such as cultural ecology, while not common in the field of tourism research, can reveal important information about IT innovation and use by rural producers diversifying into nature tourism enterprises supported by the web, under conditions of economic hardship.

TRAFFIC-RELATED AIR POLLUTION AND CHILDREN’S RESPIRATORY HEALTH: DEVELOPING AND REFINING ESTIMATES OF RESIDENTIAL EXPOSURE

ISEE-521 Introduction: Several epidemiological studies have found associations between residential proximity to busy roads and adverse health effects. We recently conducted a school-based, cross-sectional study to examine respiratory health among children (8 to 11 years) living and attending schools at varying distances from high-traffic roads. Air pollutants, including particulate matter (PM10, PM2.5), black carbon (BC), and nitrogen oxides (NOX, NO,), were measured at each of ten schools. We found associations between traffic pollutants (especially BC, NOX, and NO {NOX- NO2}) and asthma and bronchitis episodes in the past 12 months, using a two-stage hierarchical model. In our analysis to date, we used pollutant levels measured at the school sites as estimates of the children’s exposure to traffic emissions. To refine exposure estimates, we obtained additional within-neighborhood measurements of NOX and NO2. We report here on the development of new measures of residential traffic exposure based on geographic information systems (GIS) technology. These metrics will then be compared with observed traffic pollutant concentrations. Methods: We utilize information on both school or residential address and traffic data using geographic information systems (GIS) technology to calculate various traffic metrics, including closest traffic volume within a given buffer radius, maximum traffic volume, and distance-weighted traffic density. Second, we develop a model incorporating prevailing wind direction and distance. Third, we present results of a spatial regression model similar to that developed by Brauer et al. (Epidemiology 2003; 14:228-39). This model uses information on several factors, such as local traffic density, distance to the highway and traffic counts, to explain the variation in school and neighborhood NOx/NO2 concentrations that were collected. Our last exposure measure is based on self-reported assessment of traffic from our intake questionnaires. Results: The GIS-based traffic metrics and spatial regression models will be presented and compared with previous results obtained using school-based or residential exposure measurements. Ultimately, these measures will be used to further explore associations between both bronchitis and asthma symptoms. Discussion: Different metrics of exposure to traffic pollutants can be used to explore exposure misclassification. By reducing measurement error, we will be able to elucidate more clearly the relationships of traffic to respiratory health outcomes. The results will help determine the relative importance of different approaches to refining exposure estimates and, in so doing, will provide methodological guidance for future traffic studies. Information generated from this study will be useful for both ambient air quality standard-setting and pollution control strategies.

GIS-based spatial analysis and modeling for landslide hazard assessment in steeplands, southern Honduras

Landslides are a serious concern in steepland areas where intensive storms are common. The level of landslide hazard is not only dependent on storm and land physical attributes, human activities, especially agriculture practices, are also important considerations. Hurricane Mitch was one of the strongest hurricanes of the 20th century; it generated intense rain that triggered widespread landslides and flooding throughout Honduras and Nicaragua. The objective of this study was to develop and test a heuristic approach for predicting spatial distribution of landslide hazard. Four variables, slope, aspect, stream proximity, and land cover type, generated from available topography and remote sensing data, were used in the model. This model was developed and verified in southern Honduras based on data from the Namasigue watershed and validated in the adjacent El Triunfo watershed. Results of GIS analyses showed that the likelihood of landslide was significantly influenced by slope and land cover. As slope increased, the percentage of land affected by landslides, when the soils were saturated, increased sharply on land used for crop production, indicating that agricultural activity and the associated removal of deep-rooted permanent vegetation increased the landslide hazard on steep sites. Consequently, sites covered by shrub fallow and forests had relatively low incidence of landslides regardless of the topographic features. The model was effective in predicting landslide hazard and generating landslide hazard maps; this model has been found to be a useful aid for soil and water conservation organizations in the region to target conservation investment.

Land allocation based on integrated GIS-optimization modeling at a watershed level

Results of a watershed optimization model that specifies the future amount of land for each land use type at a sub-area level are incorporated into a GIS-based spatial allocation model to provide specific location recommendations based on existing land use, slope, distance to surface water and conversion preference. Thus, more detailed interpretation and implementation of optimal development scenarios can be obtained. This integrated approach was applied in Lake Erhai basin, China. This prototype land allocation system combines the strength of optimization modeling and GIS. A decision maker knows where and what type of land use changes should be made to achieve an environmental and economic sustainable future. That is, the modeling results provide support for answering questions of “what should I do?” and “how do I do it?” in land use planning and decision-making.

Exploring Spatial Effects on Urban Housing Duration

The decision to buy or sell a dwelling is complex. As such, researchers have considered models of housing price, location choice, and other dimensions of the problem. However, one potentially overlooked aspect of the decision to buy or sell a home concerns the spatial characteristics associated with the property itself. To this end, the authors utilize the Cox proportional hazards approach to model housing duration as a function of spatial and property-specific variables. They incorporate exogenous spatial variables into their model by using GIS-based spatial modeling. They find that spatial factors, such as commuting time zones, as well as aspatial characteristics, such as equity constraints, are important for explaining housing turnover. Moreover, the applicability of the Cox proportional hazard model to the spatial analysis of housing duration is demonstrated.