Bing She

Weighted network Voronoi Diagrams for local spatial analysis

Detection of spatial clusters among geographic events in a planar space often fails in real world practices. For example, events in urban areas often occurred on or along streets. In those cases, objects and their movements were limited to the street network in the urban area. This deviated from what a set of freely located points could represent. Consequently, many of the spatial analytic tools would likely produce biased results. To reflect this limitation, we developed a new approach, weighted network Voronoi diagrams, to modeling spatial patterns of geographic events on street networks whose street segments can be weighted based on their roles in the events. Using kernel density estimation and local Moran’s index statistics, the frequency of events occurring on a street segment can be used to produce a weight to associate with the street segment. The weights can then be normalized using a predefined set of intervals. The constructed weighted Voronoi network explicitly takes into account the characteristics of how events distribute, instead of being limited to assessing the spatial distribution of events without considering how the structure of a street network may affect the distribution. This approach was elaborated in a case study of Wuhan City, China. Constructing weighted network Voronoi diagrams of these partitioned networks could assist city planners and providers of public/private services to better plan for network-constrained service areas.

The Network-Max-P-Regions model

ABSTRACT This paper introduces a new p-regions model called the Network-Max-P-Regions (NMPR) model. The NMPR is a regionalization model that aims to aggregate n areas into the maximum number of regions (max-p) that satisfy a threshold constraint and to minimize the heterogeneity while taking into account the influence of a street network. The exact formulation of the NMPR is presented, and a heuristic solution is proposed to effectively compute the near-optimized partitions in several simulation datasets and a case study in Wuhan, China.

Integrating Spatial Data Linkage and Analysis Services in a Geoportal for China Urban Research

Many geoportals are now evolving into online analytical environments, where large amounts of data and various analysis methods are integrated. These spatiotemporal data are often distributed in different databases and exist in heterogeneous forms, even when they refer to the same geospatial entities. Besides, existing open standards lack sufficient expression of the attribute semantics. Client applications or other services thus have to deal with unrelated preprocessing tasks, such as data transformation and attribute annotation, leading to potential inconsistencies. Furthermore, to build informative interfaces that guide users to quickly understand the analysis methods, an analysis service needs to explicitly model the method parameters, which are often interrelated and have rich auxiliary information. This work presents the design of the spatial data linkage and analysis services in a geoportal for China urban research. The spatial data linkage service aggregates multisource heterogeneous data into linked layers with flexible attribute mapping, providing client applications and services with a unified access as if querying a big table. The spatial analysis service incorporates parameter hierarchy and grouping by extending the standard WPS service, and data-dependent validation in computation components. This platform can help researchers efficiently explore and analyze spatiotemporal data online.

Exploring Regionalization in the Network Urban Space

Isotropic homogeneity does not hold in urban areas. Street networks exert a great influence on human mobility. As a result, city structure is largely shaped by this network, especially the streets that carry a higher volume of traffic. In practice, small areas along network edges often need to be grouped into regions for management purposes. This work formalizes the extension to the P-regions problem that takes the network as the underlying constraint and proposes a heuristic-based approach to solve the problem to near optimality. The network is subdivided into aggregator edges that attract regions and separator regions that divide areas apart. Two types of regions emerge in the region formation process: regions that grow along a certain network edge (network regions) and regions that grow from areas that are far away from all the network edges (planar regions). The heuristic solution effectively uses pre-computed spatial contiguity and distance matrices. The global objective function consists of the original heterogeneity factor and the discounted network proximity factor. This approach is elaborated with both a simulated and a real-world dataset. The regionalization results help design, study, and service regions that explicitly consider the network configuration with flexible parameter controls.

Unpacking the Network Processes and Outcomes of Online and Offline Humanitarian Collaboration

Employing a bona fide network perspective, this study investigates the network processes and outcomes of organizational collaborative networks before and following Typhoon Haiyan, taking into account the influences of network factors, organizational attributes, and environmental exigencies. The analysis from an online survey with relief organizations and those organizations’ Twitter data showed the consistent influence of past relationships on the formation of subsequent relationships after the disaster. In the on-the-ground network, a few highly active organizations stood out and engaging in multiple modes of communication with resource contacts was seen as an adaptive practice that helped organizations to build resource ties after the typhoon. In the online domain, organizations developed post-typhoon networks by means of becoming directly linked to one another and becoming equally resourceful in building their ties. In addition, different forms of resilience were observed as outcomes of collaborative networks. Findings of this study present theoretical and practical implications by unveiling the network dynamics of contemporary humanitarian actions.

A Taxonomic Analysis of Perspectives in Generating Space-Time Research Questions in Environmental Sciences

Research questions in environment science can be decomposed into three basic dimensions: space, time and statistics. The combinations of these three dimensions reflect the diverse perspectives of observations across multiple scales. One can classify these scales into four types: individual, local, meso, and global. Following this multi-dimensional and multi-scale framework, this paper conducts a taxonomic analysis that systematically classifies research questions in environmental science. This taxonomic analysis includes papers from a leading environmental science journal. The results show that the majority of research questions are directed at local and global scale analyses. Studies that incorporate many scales of analysis are not necessarily more sophisticated than studies that investigate a single scale. Nonetheless, its beneficial to explore more possibilities by investigating data at different perspectives. This taxonomy could help generating research questions and providing guidance for building analytic workflow systems to fill the gaps in future scientific endeavors.

An open source toolkit for identifying comparative space-time research questions

Comparative space-time thinking lies at the heart of spatiotemporally integrated social sciences. The multiple dimensions and scales of socioeconomic dynamics pose numerous challenges for the application and evaluation of public policies in the comparative context. At the same time, social scientists have been slow to adopt and implement new spatiotemporally explicit methods of data analysis due to the lack of extensible software packages, which becomes a major impediment to the promotion of spatiotemporal thinking. The proposed framework will address this need by developing a set of research questions based on space-time-distributional features of socioeconomic datasets. The authors aim to develop, evaluate, and implement this framework in an open source toolkit to comprehensively quantify the changes and level of hidden variation of space-time datasets across scales and dimensions. Free access to the source code allows a broader community to incorporate additional advances in perspectives and methods, thus facilitating interdisciplinary collaboration. Being written in Python, it is entirely cross-platform, lowering transmission costs in research and education.

Network-constrained spatio-temporal clustering analysis of traffic collisions in Jianghan District of Wuhan, China

The analysis of traffic collisions is essential for urban safety and the sustainable development of the urban environment. Reducing the road traffic injuries and the financial losses caused by collisions is the most important goal of traffic management. In addition, traffic collisions are a major cause of traffic congestion, which is a serious issue that affects everyone in the society. Therefore, traffic collision analysis is essential for all parties, including drivers, pedestrians, and traffic officers, to understand the road risks at a finer spatio-temporal scale. However, traffic collisions in the urban context are dynamic and complex. Thus, it is important to detect how the collision hotspots evolve over time through spatio-temporal clustering analysis. In addition, traffic collisions are not isolated events in space. The characteristics of the traffic collisions and their surrounding locations also present an influence of the clusters. This work tries to explore the spatio-temporal clustering patterns of traffic collisions by combining a set of network-constrained methods. These methods were tested using the traffic collision data in Jianghan District of Wuhan, China. The results demonstrated that these methods offer different perspectives of the spatio-temporal clustering patterns. The weighted network kernel density estimation provides an intuitive way to incorporate attribute information. The network cross K-function shows that there are varying clustering tendencies between traffic collisions and different types of POIs. The proposed network differential Local Moran’s I and network local indicators of mobility association provide straightforward and quantitative measures of the hotspot changes. This case study shows that these methods could help researchers, practitioners, and policy-makers to better understand the spatio-temporal clustering patterns of traffic collisions.

Automating land parcel classification for neighborhood-scale urban analysis

ABSTRACT Homeowners’ Associations (HOAs) dictate landscape structure and management through legally enforceable land covenants at the neighborhood scale in the USA. Determining the location and spatial extent of HOAs is critical for examining its influence. However, such analysis is confounded by the lack of spatial data at the appropriate unit for such analysis. The purpose of this paper is to develop and realize an open source implementation to automate land parcel classification, which is an initial step towards the goal of determining the impact of HOAs on urban land management. Using Maricopa County, Arizona as a testbed, we found that parcel merging processes reduce the number of subdivisions from 26,042 to 17,269, such that boundaries better align with neighborhood units to which rule sets like land covenants apply. Moreover, after an initial training period, this process was completed in just over 7 hours. This research is an important first step in enabling a number of analysis including determining the location and spatial extent of HOAs regionally and, eventually, nationally and determining proposed links between HOAs and land management outcomes.

Mapping Forest Composition in China: GIS Design and Implementation

Geographic Information System (GIS)-based mapping and decision support systems use in forestry in China is still relatively limited, especially the large-scale mapping and specialized software development. However this application area has witnessed a growth in its market from various customers. This research has designed and implemented a new Forest Form Mapping System for China’s Forest Resources, as well as a platform for research, education and decision support to achieve the goal of sustainable environmental management. The project has demonstrated how GPS data and data from the second national forest survey of China can be integrated, visualized, and reported in a.NET and ArcEngine-based system. Given its flexible architecture and user friendly interface, it is suitable for a variety of applications in forest resource management.