Jacqueline Geoghegan

Spatial landscape indices in a hedonic framework: an ecological economics analysis using GIS

Abstract This paper develops a spatial hedonic model to explain residential values in a region within a 30-mile radius of Washington DC. Hedonic models of housing or land values are commonplace, but are rarely estimated for non-urban problems and never using the type of spatial data (geographical information system or GIS) available to us. Our approach offers the potential for a richer model, one that allows for spatial heterogeneity in estimation, and one that ties residential land values to features of the landscape. Beyond the traditional variables to explain residential values, such as man-made and ecological features of the parcel and distance to cities and natural amenities, we also hypothesize that the value of a parcel in residential land use is affected by the pattern of surrounding land uses, not just specific features of point locations. We have also created and added these variables to the hedonic model by choosing an appropriate area around an observation, and calculating measures of percent open space, diversity, and fragmentation of land uses, measured at different scales around that observation. These indices have, for the most part, been significant in the models. By including two of the landscape indices developed by landscape ecologists, we have developed a model that explains land and housing values more completely, by capturing how individuals value the diversity and fragmentation of land uses around their homes.

Theory, data, methods: developing spatially explicit economic models of land use change

Questions of land use/land cover change have attracted interest among a wide variety of researchers concerned with modeling the spatial and temporal patterns of land conversion and understanding the causes and consequences of these changes. Among these, geographers and natural scientists have taken the lead in developing spatially explicit models of land use change at highly disaggregate scales (i.e. individual land parcels or cells of the landscape). However, less attention has been given in the development of these models to understanding the economic process — namely, the human behavioral component — that underlies land use change. To the extent that researchers are interested in explaining the causal relationships between individual choices and land use change outcomes, more fully articulated economic models of land use change are necessary. This paper reviews some of the advances that have been made by geographers and natural scientists in developing these models of spatial land use change, focusing on their modeling of the economic process associated with land use change. From this vantage point, it is argued that these models are primarily “ad hoc,” developed without an economic theoretical framework, and therefore are susceptible to certain conceptual and estimation problems. Next, a brief review of traditional economic models of land use determination is given. Although these models are developed within a rigorous economic framework, they are of limited use in developing spatially disaggregate and explicit models of land use change. Recent contributions from economists to the development of spatially explicit models are then discussed, in which an economic structural model of the land use decision is developed within a spatially explicit framework and from which an estimable model of land use change is derived. The advantages of this approach in terms of simulating policy scenarios and addressing econometric issues of spatial dependency and endogeneity are discussed. We use some specific examples from ongoing research in the Patuxent Watershed, Maryland, USA to illustrate our points. The paper concludes with some summary remarks and suggestions for further research.

The value of open spaces in residential land use.

Abstract The preservation of open spaces has become an important policy topic in many regions. Policy tools that have been used include: cluster zoning; transferable development rights; proposed land taxes to fund purchases of remaining open spaces; and private organizations that buy land. This paper develops a theoretical model of how different types of open spaces are valued by residential land owners living near these open spaces, and then, using a hedonic pricing model, tests hypotheses concerning the extent to which these different types of open spaces are capitalized into housing prices. The empirical results from Howard County, a rapidly developing county in Maryland, USA, show that “permanent” open space increases near-by residential land values over three times as much as an equivalent amount of “developable” open space. This methodology can be used to help inform policy decisions concerning open space preservation, such as effectively targeting certain areas for preservation, or as a means of creative financing of the purchase of conservation easements, through the increase in property taxes, resulting from the associated increase in property values.

Agricultural intensification and changes in cultivated areas, 1970–2005

Does the intensification of agriculture reduce cultivated areas and, in so doing, spare some lands by concentrating production on other lands? Such sparing is important for many reasons, among them the enhanced abilities of released lands to sequester carbon and provide other environmental services. Difficulties measuring the extent of spared land make it impossible to investigate fully the hypothesized causal chain from agricultural intensification to declines in cultivated areas and then to increases in spared land. We analyze the historical circumstances in which rising yields have been accompanied by declines in cultivated areas, thereby leading to land-sparing. We use national-level United Nations Food and Agricultural Organization data on trends in cropland from 1970–2005, with particular emphasis on the 1990–2005 period, for 10 major crop types. Cropland has increased more slowly than population during this period, but paired increases in yields and declines in cropland occurred infrequently, both globally and nationally. Agricultural intensification was not generally accompanied by decline or stasis in cropland area at a national scale during this time period, except in countries with grain imports and conservation set-aside programs. Future projections of cropland abandonment and ensuing environmental services cannot be assumed without explicit policy intervention.

Modeling tropical deforestation in the southern Yucatán peninsular region: comparing survey and satellite data

This paper presents some initial modeling results from a large, interdisciplinary research project underway in the southern Yucatan peninsular region. The aims of the project are: to understand, through individual household survey work, the behavioral and structural dynamics that influence land managers’ decisions to deforest and intensify land use; model these dynamics and link their outcomes directly to satellite imagery; model from the imagery itself; and, determine the robustness of modeling to and from the satellite imagery. Two complementary datasets, one from household survey data on agricultural practices including information on socio-economic factors and the second from satellite imagery linked with aggregate government census data, are used in two econometric modeling approaches. Both models test hypotheses concerning deforestation during different time periods in the recent past in the region. The first uses the satellite data, other spatial environmental variables, and aggregate socio-economic data (e.g., census data) in a discrete-choice (logit) model to estimate the probability that any particular pixel in the landscape will be deforested, as a function of explanatory variables. The second model uses the survey data in a cross-sectional regression (OLS) model to ask questions about the amount of deforestation associated with each individual farmer and to explain these choices as a function of individual socio-demographic, market, environmental, and geographic variables. In both cases, however, the choices of explanatory variables are informed by social science theory as to what are hypothesized to affect the deforestation decision (e.g., in a von Thunen model, accessibility is hypothesized to affect choice; in a Ricardian model, land quality; in a Chayanovian model, consumer–labor ratio). The models ask different questions using different data, but several broad comparisons seem useful. While most variables are statistically significant in the discrete choice model, none of the location variables are statistically significant in the continuous model. Therefore, while location affects the overall probability of deforestation, it does not appear to explain the total amount of deforestation on a given location by an individual.

Deforestation in the southern Yucatan peninsular region: an integrative approach

Abstract The tensions between development and preservation of tropical forests heighten the need for integrated assessments of deforestation processes and for models that address the fine-tuned location of change. As Mexico’s last tropical forest frontier, the southern Yucatan peninsular region witnesses these tensions, giving rise to a “hot spot” of tropical deforestation. These forests register the imprint of ancient Maya uses and selective logging in the recent past, but significant modern conversion of them for agriculture began in the 1960s. Subsequently, as much as 10% of the region’s forests have been disturbed anthropogenically. The precise rates of conversion and length of successional growth in both upland and wetland forests are tied to policy and political economic conditions. Pressures on upland forests are exacerbated by the development of infrastructure for El Mundo Maya, an archaeological and ecological activity predicated on forest maintenance, and by increased subsistence and market cultivation, including lands on the edge of Mexico’s largest tropical forest biosphere reserve. In this complex setting, the southern Yucatan peninsular region project seeks to unite research in the ecological, social, and remote sensing sciences to provide a firm understanding of the dynamics of deforestation and to work towards spatially explicit assessments and models that can be used to monitor and project forest change under different assumptions.

Capitalization of Open Spaces into Housing Values and the Residential Property Tax Revenue Impacts of Agricultural Easement Programs

Using a unique spatial database, a hedonic model is developed to estimate the value to nearby residents of open space purchased through agricultural preservation programs in three Maryland counties. After correcting for endogeneity and spatial autocorrelation, the estimated coefficients are used to calculate the potential changes in housing values for a given change in neighborhood open space following an agricultural easement purchase. Then, using the current residential property tax for each parcel, the expected increase in county tax revenue is computed and this revenue is compared to the cost of preserving the lands.

Modeling and Managing Urban Growth at the Rural-Urban Fringe: A Parcel-Level Model of Residential Land Use Change

As many local and state governments in the United States grapple with increasing growth pressures, the need to understand the economic and institutional factors underlying these pressures has taken on added urgency. From an economic perspective, individual land use decisions play a central role in the manifestation of growth pressures, as changes in land use pattern are the cumulative result of numerous individual decisions regarding the use of lands. In this study, the issue of growth management is addressed by developing a spatially disaggregated, microeconomic model of land conversion decisions suitable for describing residential land use change at the rural-urban fringe. The model employs parcel-level data on land use in Calvert County, Maryland, a rapidly growing rural-urban fringe county. A probabilistic model of residential land use change is estimated using a duration model, and the parameter estimates are employed to simulate possible future growth scenarios under alternative growth management scenarios. Results suggest that “smart growth” objectives are best met when policies aimed at concentrating growth in target areas are implemented in tandem with policies designed to preserve rural or open space lands.

Land use change: complexity and comparisons

Research on the determinants of land use change and its relationship to vulnerability (broadly defined), biotic diversity and ecosystem services (e.g. Gullison et al. 2007), health (e.g. Patz et al. 2004) and climate change (e.g. van der Werf et al. 2004) has accelerated. Evidence of this increased interest is demonstrated by several examples. Funding agencies in the US (National Institutes of Health, National Science Foundation, National Aeronautics and Space Administration and National Oceanic and Atmospheric Administration) and around the world have increased their support of land use science. In addition to research papers in disciplinary journals, there have been numerous edited volumes and special issues of journals recently (e.g. Gutman et al. 2004; Environment & Planning B 2005; Environment & Planning A 2006; Lambin and Geist 2006; Kok, Verburg and Veldkamp 2007). And in 2006, the Journal of Land Use Science was launched. Land use science is now at a crucial juncture in its maturation process. Much has been learned, but the array of factors influencing land use change, the diversity of sites chosen for case studies, and the variety of modeling approaches used by the various case study teams have all combined to make two of the hallmarks of science, generalization and validation, difficult within land use science. This introduction and the four papers in this themed issue grew out of two workshops which were part of a US National Institutes of Health (NIH) ‘Roadmap’ project. The general idea behind the NIH Roadmap initiative was to stimulate scientific advances by bringing together diverse disciplines to tackle a common, multi-disciplinary scientific problem. The specific idea behind our Roadmap project was to bring together seven multi-disciplinary case study teams, working in areas that could be broadly classified as inland frontiers, incorporating social, spatial and biophysical sciences, having temporal depth on both the social and biophysical sides, and having had long-term funding. Early in our Roadmap project, the crucial importance of modeling, particularly agent-based modeling, for the next phase of land-use science became apparent and additional modelers not affiliated with any of the seven case studies were brought into the project. Since agent-based simulations attempt to explicitly capture human behavior and interaction, they were of special interest. At the risk of oversimplification, it is worth briefly reviewing selected key insights in land use science in the past two decades to set the stage for the papers in this themed issue. One of the earliest realizations, and perhaps most fundamental, was accepting the crucial role that humans play in transforming the landscape, and concomitantly the distinction drawn between land cover (which can be seen remotely) and land use (which, in most circumstances, requires in situ observation; e.g. Turner, Meyer and Skole 1994). The complexity of factors influencing land use change became apparent and led to a variety of ‘box and arrow’ diagrams as conceptual frameworks, frequently put together by committees rarely agreeing with one another on all details, but agreeing among themselves that there were many components (social and biophysical) whose role needed to be measured and understood. A series of case studies emerged, recognizing the wide array of variables that needed to be incorporated, and typically doing so by assembling a multidisciplinary team (Liverman, Moran, Rindfuss and Stern 1998; Entwisle and Stern 2005). The disciplinary make-up of the team strongly influenced what was measured and how it was measured (see Rindfuss, Walsh, Turner, Fox and Mishra 2004; Overmars and Verburg 2005), with limited, if any, coordination across case studies (see Moran and Ostrom 2005 for an exception). In large part, the focus on case studies reflected the infancy of theory in land use science. Teams combined their own theoretical knowledge of social, spatial and ecological change with an inductive approach to understanding land use change – starting from a kitchen sink of variables and an in-depth knowledge of the site to generate theory on the interrelationships between variables and the importance of contextual effects. This lack of coordination in methods, documentation and theory made it very difficult to conduct meta-analyses of the driving factors of land use change across all the case studies to identify common patterns and processes (Geist and Lambin 2002; Keys and McConnell 2005). Recognizing that important causative factors were affecting the entire site of a case study (such as a new road which opens an entire area) and that experimentation was not feasible, computational, statistical and spatially explicit modeling emerged as powerful tools to understand the forces of land use change at a host of space–time scales (Veldkamp and Lambin 2001; Parker, Manson, Janssen, Hoffmann, and Deadman 2003; Verburg, Schot, Dijst and Veldkamp 2004). Increasingly, in recognition of the crucial role of humans in land use change, modeling approaches that represent those actors as agents have emerged as an important, and perhaps the dominant, modeling approach at local levels (Matthews, Gilbert, Roach, Polhil and Gotts 2007). In this introductory paper we briefly discuss some of the major themes that emerged in the workshops that brought together scientists from anthropology, botany, demography, developmental studies, ecology, economics, environmental science, geography, history, hydrology, meteorology, remote sensing, geographic information science, resource management, and sociology. A central theme was the need to measure and model behavior and interactions among actors, as well as between actors and the environment. Many early agent-based models focused on representing individuals and households (e.g. Deadman 1999), but the importance of other types of actors (e.g. governmental units at various levels, businesses, and NGOs) was a persistent theme. ‘Complexity’ was a term that peppered the conversation, and it was used with multiple meanings. But the dominant topic to emerge was comparison and generalization: with multiple case studies and agent-based models blooming, how do we compare across them and move towards generalization? We return to the generalization issue at the end of this introductory paper after a brief discussion of the other themes.

Modeling the Determinants of Semi-Subsistent and Commercial Land Uses in an Agricultural Frontier of Southern Mexico: A Switching Regression Approach

The authors analyze the consequences of imperfect output markets for the land-use decisions of semi-subsistence farmers in an agricultural frontier of southern Mexico. The approach is motivated by previous applications of the agriculture household model establishing that the farm household’s productionand consumptiondecisions are analytically nonseparable when markets are not used. Econometric results generated by a switching regression model suggest the importanceof distinguishing the discrete choiceof market participation from thatof area cultivated.