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  • 1
    Online Resource
    Online Resource
    A Dynamic Spatial Weight Matrix and Localized Space–Time Autoregressive Integrated Moving Average for Network Modeling
    Wiley ; 2014
    In:  Geographical Analysis Vol. 46, No. 1 ( 2014-01), p. 75-97
    Details
    In: Geographical Analysis, Wiley, Vol. 46, No. 1 ( 2014-01), p. 75-97
    Abstract: Various statistical model specifications for describing spatiotemporal processes have been proposed over the years, including the space–time autoregressive integrated moving average ( STARIMA ) and its various extensions. These model specifications assume that the correlation in data can be adequately described by parameters that are globally fixed spatially and/or temporally. They are inadequate for cases in which the correlations among data are dynamic and heterogeneous, such as network data. The aim of this article is to describe autocorrelation in network data with a dynamic spatial weight matrix and a localized STARIMA model that captures the autocorrelation locally (heterogeneity) and dynamically (nonstationarity). The specification is tested with traffic data collected for central L ondon. The result shows that the performance of estimation and prediction is improved compared with standard STARIMA models that are widely used for space–time modeling. En los últimos años, se han propuesto diversas especificaciones de modelado estadístico para describir procesos espacio‐temporales. Esto incluye el modelo espacio‐temporal autorregresivo integrado de media móvil (STARIMA) y sus varios derivados. Estas especificaciones de modelo asumen que la correlación de los datos puede ser adecuadamente descrita por parámetros que se fijan a nivel global en el espacio y/o tiempo. Dichos parámetros son inadecuados para los casos en los que las correlaciones entre los datos son dinámicas y heterogéneas, como en el contexto de los datos de la red. El objetivo de este artículo es describir la autocorrelación en los datos de red con una matriz de ponderación espacial dinámica y un modelo STARIMA localizado (LSTARIMA) que captura la autocorrelación local (heterogeneidad) de forma dinámica (no estacionariedad). La especificación del modelo es evaluada con datos de tráfico recolectados en el centro de Londres. Los resultados demuestran que los rendimientos de estimación y predicción mejoran con el método propuesto en comparación con los modelos STARIMA estándar que son ampliamente utilizados para el modelado de espacio‐temporal. 通过设定多种统计模型来描述地理时空过程已提出多年，包括时空自回归移动平均(STARIMA)及其变形。此类模型通过假设数据相关性可由在时间域或者空间域上全局不变的参数加以充分描述。因此，上述模型不适用于具有动态或异质相关性的数据，如网络数据。本文试图采用一个动态空间权重矩阵与局部时空自回归移动平均(LSTARIMA)模型来描述数据的自相关程度，以此捕捉局域自相关(异质性)和动态自相关(非平稳性)。以伦敦市中心的交通数据作为模型的实证案例的测试结果显示，相对于广泛应用于时空过程分析的标准STARIMA模型，本文的模型在参数估计和预测性能上均有提升。
    Type of Medium: Online Resource
    ISSN: 0016-7363 , 1538-4632
    URL: Issue
    URL: Article
    DOI: 10.1111/gean.2014.46.issue-1
    DOI: 10.1111/gean.12026
    RVK:
    RA 2430
    Language: English
    Publisher: Wiley
    Publication Date: 2014
    detail.hit.zdb_id: 2074885-1
    SSG: 14
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  • 2
    Online Resource
    Online Resource
    Making Space in Geographical Analysis
    Wiley ; 2023
    In:  Geographical Analysis Vol. 55, No. 2 ( 2023-04), p. 325-341
    Details
    In: Geographical Analysis, Wiley, Vol. 55, No. 2 ( 2023-04), p. 325-341
    Abstract: In this commentary we reflect on the potential and power of geographical analysis, as a set of methods, theoretical approaches, and perspectives, to increase our understanding of how space and place matter for all . We emphasize key aspects of the field, including accessibility, urban change, and spatial interaction and behavior, providing a high‐level research agenda that indicates a variety of gaps and routes for future research that will not only lead to more equitable and aware solutions to local and global challenges, but also innovative and novel research methods, concepts, and data. We close with a set of representation and inclusion challenges to our discipline, researchers, and publication outlets.
    Type of Medium: Online Resource
    ISSN: 0016-7363 , 1538-4632
    URL: Issue
    URL: Article
    DOI: 10.1111/gean.v55.2
    DOI: 10.1111/gean.12325
    RVK:
    RA 2430
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 2074885-1
    SSG: 14
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  • 3
    Online Resource
    Online Resource
    A Spatiotemporal Bayesian Hierarchical Approach to Investigating Patterns of Confidence in the Police at the Neighborhood Level
    Wiley ; 2019
    In:  Geographical Analysis Vol. 51, No. 1 ( 2019-01), p. 90-110
    Details
    In: Geographical Analysis, Wiley, Vol. 51, No. 1 ( 2019-01), p. 90-110
    Abstract: Public confidence in the police is crucial to effective policing. Improving understanding of public confidence at the local level will better enable the police to conduct proactive confidence interventions to meet the concerns of local communities. Conventional approaches do not consider that public confidence varies across geographic space as well as in time. Neighborhood level approaches to modeling public confidence in the police are hampered by the small number problem and the resulting instability in the estimates and uncertainty in the results. This research illustrates a spatiotemporal Bayesian approach for estimating and forecasting public confidence at the neighborhood level and we use it to examine trends in public confidence in the police in London, UK, for Q2 2006 to Q3 2013. Our approach overcomes the limitations of the small number problem and specifically, we investigate the effect of the spatiotemporal representation structure chosen on the estimates of public confidence produced. We then investigate the use of the model for forecasting by producing one‐step ahead forecasts of the final third of the time series. The results are compared with the forecasts from traditional time‐series forecasting methods like naïve, exponential smoothing, ARIMA, STARIMA, and others. A model with spatially structured and unstructured random effects as well as a normally distributed spatiotemporal interaction term was the most parsimonious and produced the most realistic estimates. It also provided the best forecasts at the London‐wide, Borough, and neighborhood level.
    Type of Medium: Online Resource
    ISSN: 0016-7363 , 1538-4632
    URL: Issue
    URL: Article
    DOI: 10.1111/gean.v51.1
    DOI: 10.1111/gean.12160
    RVK:
    RA 2430
    Language: English
    Publisher: Wiley
    Publication Date: 2019
    detail.hit.zdb_id: 2074885-1
    SSG: 14
    Location Call Number Limitation Availability
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  • 4
    Online Resource
    Online Resource
    Toward Quantitative Measures for the Semantic Quality of Polygon Generalization
    University of Toronto Press Inc. (UTPress) ; 2006
    In:  Cartographica: The International Journal for Geographic Information and Geovisualization Vol. 41, No. 2 ( 2006-06), p. 135-148
    Details
    In: Cartographica: The International Journal for Geographic Information and Geovisualization, University of Toronto Press Inc. (UTPress), Vol. 41, No. 2 ( 2006-06), p. 135-148
    Abstract: Map generalization changes the semantics and geometry of map objects according to the context defined by users. How to evaluate and ensure the quality of generalization has become a major issue in contemporary digital cartography. The change in semantics after generalization has been studied much less than the other two aspects (geometry and topology). This research investigates the effect of generalization operations on the semantics of map objects. A set of quantitative measures for semantic change is put forward. A case study of a land-use map is implemented to illustrate the practical usefulness of these proposed measures, with a merging operation as an example of polygon generalization. The results indicate that these measures are not only sound in theory but also meaningful in practice.
    Type of Medium: Online Resource
    ISSN: 0317-7173 , 1911-9925
    URL: Article
    DOI: 10.3138/0172-6733-227U-8155
    Language: English
    Publisher: University of Toronto Press Inc. (UTPress)
    Publication Date: 2006
    SSG: 7,26
    SSG: 14,1
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