Geographical Analysis最新文献

Total demand suitably served and facility workload balance are two important considerations in location coverage. Previous work has dealt with workload balancing issues using a number of approaches, including imposing facility capacities and the use of multiple objectives focused on workload variation. However, a facility is usually restricted to a single service unit, inconsistent with strategies that allow for increased staffing such as multiple service units in dealing with higher levels of demand. This article proposes a new bi-objective optimization model that maximizes total demand coverage and minimizes workload differences simultaneously while allowing more than one service unit to be co-located at a site. Since the proposed model is strongly NP hard, a heuristic algorithm is developed for efficient solution. The model is applied to support postal delivery service planning. Results show that the proposed model offers improved performance compared to approaches that do not permit co-location. The proposed algorithm is able to produce high-quality solutions that evenly distribute allocated service demand, and does so much faster with higher-quality solutions compared to exact solution approaches.

Privacy and utility are two important objectives to consider when releasing census data. However, these two objectives are often conflicting, as protecting privacy usually necessitates introducing noise into the data, which compromises data utility. Determining the appropriate level of privacy protection presents a significant challenge in the data release. Therefore, it is necessary to investigate the tradeoff between privacy and utility before making a final decision on the level of privacy protection. In this article, we propose a multiobjective optimization framework to generate multiple optimal solutions that satisfy the two objectives of privacy and utility, as well as to analyze the tradeoff between privacy and utility for decision-making. This framework relocates individuals susceptible to revealing their identities to protect their privacy. We maximize the number of individuals relocated while maximizing the utility of the data after relocations. The proposed framework is tested using synthetic population data in Franklin County, Ohio. Our experimental results show that the framework can efficiently generate a collection of optimal solutions and can be used to effectively balance privacy and utility.

As cities adapt to new needs and challenges, their forms change in close relation to population dynamics. This article focuses on the link between population dynamics and the evolution of building hull types. The case study is the Osaka-Kobe city-region in Japan, a country globally witnessing an intense population decline. Morphometric indicators are coupled with a tree-like classificatory model in order to label buildings into consistent classes between two different periods (2003–2004 and 2013–2014). The building class distributions and their evolutions are studied in conjunction with population censuses. Urban adaptation processes are particularly accounted for through the study of the replacement of building types. Results show that, among other things, townhouses in traditional neighborhoods are gradually being replaced by small-size collective complexes. In far outlying areas, people are still eager to move and live in detached single-family homes despite a global context of population decline. Finally, central places are increasingly filled by narrow almost-adjoining towers. Relations between building types and population dynamics, detailed through maps and statistics, show that peoples are increasingly concentrating in central locations associated with specific building types, while some other peripheral locations are concerned by both a disappearance of specific building types and a population decline.

Multilevel models are one of the main statistical methods used in modeling contextual effects in social sciences. A common limitation of these methods is the use pre-set boundaries—usually administrative units—to define contexts, when these boundaries do not always match up with the “true” causally relevant contexts that may affect the outcomes of interest. In this study applied to the obesity geography in the Paris area (France), we propose a new spatially explicit two-step procedure to tackle this methodological issue. The first step consists in estimating a geographically weighted regression model, then using it to reveal and delineate relevant nonstationarity-based data-driven spatial contexts, and finally including them as a random effect into a random slope multilevel model. In applying this hybrid methodology for modeling body mass index within a sample of 9,089 French adults, we demonstrate that it outperforms administrative-based multilevel models in terms of decreasing Akaike information criteria, and is better at accounting for contextual effects through intraclass correlation coefficient and increasing slope variance. We suggest that this procedure might be generalized to quantitative geographical analyses involving contextual effects.

This research utilized global coverage, annual, high-quality land cover time-series data to explore the urban growth process in the core area, and in several buffer zones, of Beijing, Guangzhou, Shanghai, and Tokyo. We developed a conceptual model in which growth is characterized at the per-object level by four active growth events: introduction, establishment, dispersal, and coalescence, with a fifth inactivity event, stability. We developed a rule-base which allowed the direct measurement of establishment, dispersal and coalescence from observed inter-annual changes in the urban objects over time. By aggregating the object-level events to the landscape level we showed that these three events generally followed a synchronous temporal trend in terms of magnitude within the core area and within each buffer zone. There was no evidence for a logical sequence of events through time. The identified events dominated alternately over time, although synchronicity in magnitude far outweighed any differences in proportion between them. This points to a single underlying urbanization process: urban growth with a specific dynamic rate. Interestingly, synchronicity was not generally observed between the core and buffer zones. This proposed object-based method provides insights into the underlying urban growth process and could be used to build new urban growth models.

Spatially heterogeneous weights and a non-compensatory aggregation scheme, are two important properties needed to construct a composite indicator capable of summarizing properly the multidimensional phenomenon of local spatial units. Such a composite indicator takes into account, on the one hand, the latent characteristics of the specific units related to their location in the territory, and on the other hand, the relative importance of sub-indicators highlighting both positive and negative aspects of the studied phenomena. Under these premises, this article proposes a new method called Ordered Geographically Weighted Averaging (OGWA), which can consider different degrees of non-compensability between sub-indicators and, at the same time, the spatial heterogeneity for continuous, ordinal, and mixed data. The properties of the method are evaluated through a simulation study. Finally, the method is applied to construct a composite indicator to map the urban public infrastructure of São Sebastião do Paraíso, a city located in the southeastern region of Brazil.

The Valeriepieris (VP) circle is the smallest circle containing half of the world's population. The Valeriepieris circle acts as a spatial median, splitting spatial data into two halves in a unique way. In this article the idea of the VP circle is generalized and a fast algorithm to compute it is described. This algorithm has been implemented in Python and is available for download and use. The VP circle is compared to other measures of center and dispersion for population distributions and is shown to reflect expected differences between countries and changes over time. By studying the VP circle as a function of the included population fraction, a new way of representing population distributions is constructed, as well as a mathematical model of its expected behavior. Finally a measure of population “centralization” is constructed which measures the tendency of a territory to be dominated by a single population center or to have a more even distribution of population. Thus, VP circles unify measures of population center, dispersion and centralization while also being useful for more detailed modeling efforts.

Spatial neighborhood definitions are a consistent source of disagreement among geographic scholars. This research will focus on the implementation and evaluation of spatiotemporal variograms (STVs) as a source of spatial neighborhood definition. STVs show the similarity, measured by semivariance, of spatial events to each other when separated by time and space. Over both time and space, there should exist distances over which pairs of points become “independent” of one another. We seek seeks to answer two questions in relation to STVs and their use as neighborhood definitions: (1) What data and process adjustments are necessary to implement STVs to provide neighborhood search definitions in time and space, and (2) Given that there are many ways to define a neighborhood, STVs may provide a comprehensive method that uses the data themselves to inform the size and scope of neighborhoods, with the added advantage to evaluating both spatial and temporal axes at once. We demonstrate a well-defined neighborhood that accounts for temporal variation as well as spatial and will be a needed addition as the tools incorporating simultaneous spatial and temporal neighborhoods are implemented.

Despite representing a very accurate method for assessing spatial distribution, Marcon and Puech's M has been insufficiently exploited so far, most likely because its computation relies on pairing every point of interest (i.e., firms, plants) with every other point within the area under analysis. Such a figure rapidly grows to unmanageable levels when said area is larger than a neighborhood or when every industry is taken into account. Consequently, practical applications of M have been exclusively experimental and circumscribed to very limited areas or to a handful of industries. This seems much regrettable since M provides many advantages compared to conventional measures of spatial distribution and also to alternative distance measures. In this article, we assess the reliability of using small administrative units instead of exact postal addresses for the localization of plants, in order to reduce M's computational burden. Working with a dataset that provides the location, the specific industry and the number of employees for every single plant/establishment in Italy for both manufacturing and services, we can also draw a preliminary but certainly interesting picture of Sardinia's economic geography and its development through the Great Recession toughest years between 2007 and 2012.

The World Health Organization endorses the study of diseases from the perspective of the Determinants of Health (DH), that is, the circumstances in which people are born and raised, the environment in which they grow up and age and their lifestyle. The aim of this study is to analyze the spatial behavior of the prevalence of asthma in Aragon, a Mediterranean region in Spain, under the DH approach. The methodological process entailed building a spatial database collating asthma prevalence as dependent variable, and lifestyle, socioeconomic, and climate indicators as explanatory factors, and then evaluating the spatial variability of the relationships by combining the Principal Component Analysis (PCA), Multiscale Geographically Weighted Regression (MGWR) models and cartographic design techniques. MGWR evidenced spatially varying relationships operating at different scales. Lifestyles seem closely tied to the prevalence of asthma in most of the study area while urban functionality and local climate patterns seem to boost prevalence rates in some specific enclaves. Consequently, the social and environmental conditions that characterize the study area translate into several DH scenarios modulating the spatial distribution of asthma. This differential DH behavior detected by local regression models is relevant to guiding and refining public health decision-making.