Affinity propagation (AP) is a clustering algorithm for point data used in image recognition that can be used to solve various problems, such as initial class representative point selection, large-scale sparse matrix calculations, and large-scale data with fewer parameter settings. However, the AP clustering algorithm does not consider spatiotemporal information and multiple thematic attributes simultaneously, which leads to poor performance in discovering patterns from massive spatiotemporal points (e.g., trajectory points). To resolve this issue, a multidimensional spatiotemporal affinity propagation (MDST-AP) algorithm is proposed in this study. First, the similarity of spatial and nonspatial attributes is measured in Gaussian kernel space instead of Euclidean space, which helps address the multidimensional linear inseparability problem. Then, the Davies-Bouldin (DB) index is applied to optimize the parameter value of the MDST-AP algorithm, which is applied to analyze road congestion in Beijing via taxi trajectories. Experiments on different datasets and algorithms indicated that the MDST-AP algorithm can process multidimensional spatiotemporal data points faster and more effectively.
The spatio-temporal relationship between tourism product similarity and spatial proximity has not been adequately studied empirically because of data and methodological limitations. New forms of data available at high temporal frequencies and low levels of spatial aggregation, together with large commercial data and expanding computational ability allow a variety of theories, old and new to be explored and evaluated more meticulously and systemically than has been possible hitherto. This study uses spatial visualization and data harvesting to synthesize a variety of data for exploring the evolution of hotel clusters and co-location synergies in US cities. The findings question the reliability of the current data to be used for identifying and analyzing the formation of tourist destination clusters and their dynamics. We conclude that synthesizing social media and large commercial data can generate a more robust database for research on tourism development and planning and improving opportunities for the examining spatial patterns of tourism activities. We also devise a protocol to combine ‘social media’ sources with big commercial sources for tourism
development and planning, and eventually other sectors.
This article focuses on the manner in which affordable housing fits into anchor-based strategies for urban revitalization. It involves quantitative analysis of the location of existing HUD-subsidized housing in relation to neighborhood characteristics. The goal of the article is twofold. First, we examine the degree to which neighborhood characteristics associated with neighborhoods of opportunity correlate with the location of HUD-subsidized housing in shrinking cities. Second, we make recommendations for more equitable approaches to anchor-based urban revitalization. Our analysis uses a unique database developed to measure neighborhood characteristics in shrinking US cities. Our findings suggest that the location of affordable housing is not correlated with proximity to institutional and neighborhood amenities, where anchor-based revitalization is targeted. As a result, we make recommendations to link future affordable housing siting to anchor-based strategies for inner-city revitalization.
This article synthesizes existing literature to examine the emerging concept of neighborhoods of opportunity and places it in the context of past efforts to define neighborhood opportunity. Place-based and people-based approaches to urban revitalization and community development are linked to this concept. The place-based approach focuses on promoting inner-city revitalization in order to create neighborhoods of opportunity and the people-based approach focuses on connecting people to opportunities that already exist in the regions where they live. These approaches are
examined in relation to how they influence emerging models for siting affordable housing in both distressed inner-cities and more opportunity rich suburbs that surround them. The article concludes with recommendations for a new tiered approach to place-based and people-based strategies for affordable housing siting in core city and regional contexts