Suchergebnisse

The objective of this study was to investigate the impact of political history on the dynamics of the interrelationship between land use and road networks within cities. Political history, in this study, is defined as the combination of the regional-level government programs and political events that affect the pattern of urbanization in a region. The study focused on urbanization in the cities of Pordenone and Gorizia, both situated in the Friuli-Venezia Giulia region of northeastern Italy, over the period 1950–2000. Being located adjacent to an international boundary, the city of Gorizia has a long history of political instability dating back to the beginning of the 20th century. It is assumed that this political instability has led to the application of differential socioeconomic policies which have affected the process of urbanization in the city. The aim of the study was to capture this effect by investigating the structural changes over the period of fifty years in both land use and the road network. In order to understand the extent of the effect Pordenone was used for comparison, since it has experienced a relatively peaceful past and regular growth. MOLAND (monitoring land-use/cover dynamics) data for land use and the road network were used for the study. Graph theory measures were used for a comparative analysis of the structural properties of road networks in both cities and their development over time. In order to understand the spatial relationship between change in land use and the road network, a nonparametric test of the spatial correspondence of areal distribution was used and tested at multiple spatial scales. The results suggest that political history does affect the land-use and road-network changes individually, but it did not affect the type of spatial relationship that exists between the two for those particular cities. This research makes a unique attempt to analyze the impact of policy on land-use and road-network change by using spatial data and methods of analysis which can help to understand their overall dynamics and that can be used as an alternative to data-intensive and time-intensive simulation models.

Urban growth models have been used for decades to forecast urban development in metropolitan areas. Since the 1990s cellular automata, with simple computational rules and an explicitly spatial architecture, have been heavily utilized in this endeavor. One such cellular-automata-based model, SLEUTH, has been successfully applied around the world to better understand and forecast not only urban growth but also other forms of land-use and land-cover change, but like other models must be fed important information about which particular lands in the modeled area are available for development. Some of these lands are in categories for the purpose of excluding urban growth that are difficult to quantify since their function is dictated by policy. One such category includes voluntary differential assessment programs, whereby farmers agree not to develop their lands in exchange for significant tax breaks. Since they are voluntary, today's excluded lands may be available for development at some point in the future. Mapping the shifting mosaic of parcels that are enrolled in such programs allows this information to be used in modeling and forecasting. In this study, we added information about California's Williamson Act into SLEUTH's excluded layer for Tulare County. Assumptions about the voluntary differential assessments were used to create a sophisticated excluded layer that was fed into SLEUTH's urban growth forecasting routine. The results demonstrate not only a successful execution of this method but also yielded high goodness-of-fit metrics for both the calibration of enrollment termination as well as the urban growth modeling itself.

A critical challenge of global change is managing the uncontrolled spread of cities into their surrounding rural and other land. The phenomenon of urban 'sprawl' is well known, but it remains controversial because there are no universal definitions about its etiology, nor of the causes and variables related to it. The goal of this study is to depict the temporal trend of sprawl, so as to identify a 'sprawl signature' and its evolution for the Italian Province of Pordenone focusing exclusively on spatial dispersion features. Data were compiled from multitemporal remote sensing and used to delimit urban expansion over time. We aim to describe the spatiotemporal patterns associated with urban sprawl using the perspective of the cyclical urban growth theory and focusing on measures that can detect the degree of spatial dispersion during time related to sprawl both in past and projected urban forms. Exactly how the spatiotemporal patterns of urban growth are identified is crucial for urban planners, as knowledge of them allows more efficient calibration of policies to control land-use change in order to satisfy specific needs of the population and prevent the risks and costs related to sprawl.

Land-development scenarios as a means of representing the future have been in the planner's toolkit for several decades. In this paper we provide a systematic view of four basic issues that concern scenarists and scenario users—the concepts, functions, credentials, and efficacy of land-development scenarios. Drawing upon the wealthy and expanding pool of knowledge and experience as reported in the literature, we put forward the notion that a land-development scenario set is both a bridge that connects the process of modeling with that of planning and a cognitive apparatus that stretches people's thinking and broadens their views in planning. The dual function entitles a scenario set to be a favored member of a family of innate instruments that humans operate in making decisions. Under this overarching framework, we propose three credentials that are by no means exhaustive yet are claimed to be essential for a scenario set to perform best the dual function. These are plausible unexpectedness, informational vividness, and cognitively ergonomic design. After exploring the efficacy issue of a scenario set with respect to its impacts on communities at large, we suggest that basic research efforts be underway that aim at the development of unified theories of land-development scenarios, or even scenarios in general, under a possible name of scenariology—the study of scenarios.

Remote sensing technology has great potential for acquisition of detailed and accurate land-use information for management and planning of urban regions. However, the determination of land-use data with high geometric and thematic accuracy is generally limited by the availability of adequate remote sensing data, in terms of spatial and temporal resolution, and digital image analysis techniques. This study introduces a methodology using information on image spatial form—landscape metrics—to describe urban land-use structures and land-cover changes that result from urban growth. The analysis is based on spatial analysis of land-cover structures mapped from digitally classified aerial photographs of the urban region Santa Barbara, CA. Landscape metrics were calculated for segmented areas of homogeneous urban land use to allow a further characterization of the land use of these areas. The results show a useful separation and characterization of three urban land-use types: commercial development, high-density residential, and low-density residential. Several important structural land-cover features were identified for this study. These were: the dominant general land cover (built up or vegetation), the housing density, the mean structure and plot size, and the spatial aggregation of built-up areas. For two test areas in the Santa Barbara region, changes (urban growth) in the urban spatial land-use structure can be described and quantified with landscape metrics. In order to discriminate more accurately between the three land-cover types of interest, the landscape metrics were further refined into what are termed 'landscape metric signatures' for the land-use categories. The analysis shows the importance of the spatial measurements as second-order image information that can contribute to more detailed mapping of urban areas and towards a more accurate characterization of spatial urban growth pattern.