Spatial Proximity Relationships Research Articles

Spatial Patterns, Utility, and Limitations of Volunteered Reports of Urban Homeless Campsites in Portland, Oregon

Homelessness is a complex and diverse social issue that affects many urban areas in the United States. Robust knowledge of how and where homeless individuals subsist is essential for an assessment of the granular spatiotemporal context of homelessness in a given locale. Common means for surveying homeless rest sites are resource intensive and conducted infrequently, which warrants investigations into complementary approaches. In this article, we examine the spatial patterns of homeless campsites in Portland, Oregon, and their relationship to urban features based on a three-year data set of reports volunteered by members of the public. Our quantitative analysis employs a combination of spatial analysis, statistical methods, and cartographic mapping to determine prevailing spatial patterns and proximity relationships. In that context, we assess the characteristics of the volunteered data in terms of their suitability for capturing expected local patterns of homeless campsites. The data indicate concentrations of reported campsites and significant spatial proximity relationships between campsite locations, zoning, transit stops, and homelessness support services. Our findings evidence the utility of the volunteered data for the analysis of urban homeless campsite locations on an annual basis. We further discuss the limitations of the data and provide suggestions for improvements and future research.

A novel dual-domain clustering algorithm for inhomogeneous spatial point event

PurposeThe spatial and non-spatial attributes are the two important characteristics of a spatial point, which belong to the two different attribute domains in many Geographic Information Systems applications. The dual clustering algorithms take into account both spatial and non-spatial attributes, where a cluster has not only high proximity in spatial domain but also high similarity in non-spatial domain. In a geographical dataset, traditional dual spatial clustering algorithms discover homogeneous spatially adjacent clusters suffering from the between-cluster inhomogeneity where those spatial points are described in non-spatial domain. To overcome this limitation, a novel dual-domain clustering algorithm (DDCA) is proposed by considering both spatial proximity and attribute similarity with the presence of inhomogeneity.Design/methodology/approachIn this algorithm, Delaunay triangulation with edge length constraints is first employed to construct spatial proximity relationships amongst objects. Then, a clustering strategy based on statistical change detection is designed to obtain clusters with similar attributes.FindingsThe effectiveness and practicability of the proposed algorithm are illustrated by experiments on both simulated datasets and real spatial events. It is found that the proposed algorithm can adaptively and accurately detect clusters with spatial proximity and similar non-spatial attributes under the consideration of inhomogeneity.Originality/valueTraditional dual spatial clustering algorithms discover homogeneous spatially adjacent clusters suffering from the between-cluster inhomogeneity where those spatial points are described in non-spatial domain. The research here is a contribution to developing a dual spatial clustering method considering both spatial proximity and attribute similarity with the presence of inhomogeneity. The detection of these clusters is useful to understand the local patterns of geographical phenomena, such as land use classification, spatial patterns research and big geo-data analysis.

A dual spatial clustering method in the presence of heterogeneity and noise

AbstractThe detection of spatial clusters, taking into account both spatial proximity and attribute similarity, plays a vital role in spatial data analysis. Although several dual clustering methods are currently available in the literature, most of them have detected homogeneous spatially adjacent clusters suffering from between‐cluster inhomogeneity and noise, where those spatial points have been described in the attribute domain. This article aims to accommodate both spatial proximity and attribute similarity with the presence of heterogeneity and noise. In this algorithm, Delaunay triangulation with edge‐length constraints, with consideration of arbitrary geometrical shapes, different densities, and spatial noise, is first utilized to construct spatial proximity relationships among points. Then, a clustering strategy employing information entropy is designed to identify clusters having similar attributes. The attribute clustering can adaptively and accurately detect clusters under the consideration of heterogeneity and noise. The efficacy and practicability of the proposed algorithm are illustrated by experiments employing both simulated datasets and real spatial point events.

Adaptive detection of spatial point event outliers using multilevel constrained Delaunay triangulation

Spatial outlier detection is a research hot spot in the field of spatial data mining. Because of the lack of specific research on spatial point events, this study presents an adaptive approach for spatial point events outlier detection (SPEOD) using multilevel constrained Delaunay triangulation. First, the spatial proximity relationships between spatial point events are roughly captured by Delaunay triangulation. Then, three-level constraints are described and used to refine spatial proximity relationships with the consideration of statistical characteristics. Finally, those spatial point events connected by remaining edges are gathered to form a series of subgraphs. Those subgraphs containing very few point events are regarded as spatial outliers. Experiments on both synthetic and real-world spatial data sets are used to show that the proposed SPEOD algorithm can detect various types of spatial point event outliers with high efficiency. Moreover, there is no need to input any parameter in SPEOD.

A Spatial Anomaly Points and Regions Detection Method Using Multi‐Constrained Graphs and Local Density

AbstractSpatial anomalies may be single points or small regions whose non‐spatial attribute values are significantly inconsistent with those of their spatial neighborhoods. In this article, a Spatial Anomaly Points and Regions Detection method using multi‐constrained graphs and local density (SAPRD for short) is proposed. The SAPRD algorithm first models spatial proximity relationships between spatial entities by constructing a Delaunay triangulation, the edges of which provide certain statistical characteristics. By considering the difference in non‐spatial attributes of adjacent spatial entities, two levels of non‐spatial attribute distance constraints are imposed to improve the proximity graph. This produces a series of sub‐graphs, and those with very few entities are identified as candidate spatial anomalies. Moreover, the spatial anomaly degree of each entity is calculated based on the local density. A spatial interpolation surface of the spatial anomaly degree is generated using the inverse distance weight, and this is utilized to reveal potential spatial anomalies and reflect their whole areal distribution. Experiments on both simulated and real‐life spatial databases demonstrate the effectiveness and practicability of the SAPRD algorithm.

Spatial Cluster Detection in Spatial Flow Data

As a typical form of geographical phenomena, spatial flow events have been widely studied in contexts like migration, daily commuting, and information exchange through telecommunication. Studying the spatial pattern of flow data serves to reveal essential information about the underlying process generating the phenomena. Most methods of global clustering pattern detection and local clusters detection analysis are focused on single‐location spatial events or fail to preserve the integrity of spatial flow events. In this research we introduce a new spatial statistical approach of detecting clustering (clusters) of flow data that extends the classical local K‐function, while maintaining the integrity of flow data. Through the appropriate measurement of spatial proximity relationships between entire flows, the new method successfully upgrades the classical hot spot detection method to the stage of “hot flow” detection. Several specific aspects of the method are discussed to provide evidence of its robustness and expandability, such as the multiscale issue and relative importance control, using a real data set of vehicle theft and recovery location pairs in Charlotte, NC.

Building Displacement Based on the Topological Structure

Map data at smaller scales than their source can result in spatial conflict, whereby map symbols become too close, or overlaid. Server map generalisation operators may be applied to solve this problem, including displacement. In this paper, we show how an optimisation algorithm, the snake algorithm, was used to displace multiple objects in order to resolve spatial conflicts and maintain important spatial relationships between objects during displacement. Two principles based on the snake algorithm are proposed in this paper. First, the truss structure mirroring spatial proximity relationships between buildings and between building and road is formed based on the weighted proximity graph derived from constrained Delaunay triangulations (CDT) in each map partition. In the weighted proximity graph, each connecting line is determined as a snake and as an element unit to assemble the global stiffness matrix in snake algorithm. Second, a buffer method that calculates force between a building and a road (or other linear features) or between pair of buildings is adopted in the snake algorithm. This avoids the imbalance phenomenon caused by different force calculation methods during the displacement. The feasibility of the approach is demonstrated in obtaining real geographic data. Finally, the results are cartographically usable and in particular, the spatial relationships between objects are preserved.

Object-based spatial cluster analysis of urban landscape pattern using nighttime light satellite images: a case study of China

Previous studies have demonstrated urban built-up areas can be derived from nighttime light satellite (DMSP-OLS) images at the national or continent scale. This paper presents a novel object-based method for detecting and characterizing urban spatial clusters from nighttime light satellite images automatically. First, urban built-up areas, derived from the regionally adaptive thresholding of DMSP-OLS nighttime light data, are represented as discrete urban objects. These urban objects are treated as basic spatial units and quantified in terms of geometric and shape attributes and their spatial relationships. Next, a spatial cluster analysis is applied to these basic urban objects to form a higher level of spatial units – urban spatial clusters. The Minimum Spanning Tree (MST) is used to represent spatial proximity relationships among urban objects. An algorithm based on competing propagation of objects is proposed to construct the MST of urban objects. Unlike previous studies, the distance between urban objects (i.e., the boundaries of urban built-up areas) is adopted to quantify the edge weight in MST. A Gestalt Theory-based method is employed to partition the MST of urban objects into urban spatial clusters. The derived urban spatial clusters are geographically delineated through mathematical morphology operation and construction of minimum convex hull. A series of landscape ecologic and statistical attributes are defined and calculated to characterize these clusters. Our method has been successfully applied to the analysis of urban landscape of China at the national level, and a series of urban clusters have been delimited and quantified.

A density-based spatial clustering algorithm considering both spatial proximity and attribute similarity

Geometrical properties and attributes are two important characteristics of a spatial object. In previous spatial clustering studies, these two characteristics were often neglected. This paper addresses the problem of how to accommodate geometrical properties and attributes in spatial clustering. A new density-based spatial clustering algorithm (DBSC) is developed by considering both spatial proximity and attribute similarity. Delaunay triangulation with edge length constraints is first employed for modeling the spatial proximity relationships among spatial objects. A modified density-based clustering strategy is then designed and used to identify spatial clusters. Objects in the same cluster detected by the DBSC algorithm are proximal in a spatial domain and similar in an attribute domain. In addition, the algorithm is able to detect clusters of arbitrary shapes and non-homogeneous densities in the presence of noise. The effectiveness and practicability of the DBSC algorithm are validated using both simulated and real spatial datasets.

Lipid Association-induced N- and C-terminal Domain Reorganization in Human Apolipoprotein E3

Apolipoprotein E (apoE) is a 299 amino acid, anti-atherogenic protein that plays a key role in regulating plasma lipoprotein metabolism. It is composed of an N-terminal (NT) domain (residues 1-191) that is responsible for binding to members of the low density lipoprotein receptor family and a C-terminal (CT) domain (residues 216-299) that anchors the protein to lipoprotein particles by virtue of its high-affinity lipid binding characteristics. Isoform-specific differences in the NT domain that modulate the lipoprotein binding preference elicited by the CT domain suggest the existence and importance of domain interactions in this protein. Employing steady state fluorescence quenching and resonance energy transfer techniques, spatial proximity relationships between the N- and C-terminal domains were investigated in recombinant human apoE3. ApoE3 containing a single Trp at position 264 and an N-iodoacetyl-N'-(5-sulfo-1-napthyl) ethylenediamine (AEDANS) moiety covalently attached to the lone Cys residue at position 112 was used (AEDANS-apoE3/W@264). Fluorescence quenching studies revealed a solvent-exposed location for Trp-264. In the lipid-free state, fluorescence resonance energy transfer (FRET) was noted between Trp-264 and AEDANS, with a calculated distance of 27 A between the two fluorophores. Control experiments established that FRET observed in this system is intramolecular. FRET was abolished upon proteolysis in the linker region connecting the NT and CT domains. Lowering the solution pH to 4 induced an increase in the efficiency of intramolecular energy transfer, with the two domains reorienting about 5 A closer to one another. Interdomain FRET was retained in the presence of 0.6-1.0 m guanidine hydrochloride but was lost at higher concentrations, a manifestation of unfolding of the domains and increased distance between the donor-acceptor pair. Interaction of AEDANS-apoE3/W@264 with lipid induced a loss of FRET, attributed to spatial repositioning of the domains by >80 A. The data provide biophysical evidence that, in addition to reported conformational changes in the four-helix bundle configuration induced by lipid association, lipid binding of apoE is accompanied by reorientation of the tertiary disposition of the NT and CT domains.

Relationships among dominance, inter-animal spatial proximity and affiliative social behavior in stumptail macaques ( Macaca arctoides)

Dominance ranks were determined for each of 13 free-ranging stumptail macaques on the basis of competitive food tests. Detailed daily observations of the social behavior and inter-animal spatial proximity relationships were made on each of the animals for 8 weeks. The dominance ranks were correlated with rankings for the proximity data and total scores per animal for the behavioral variables Grooming, Groomed By, Huddling, Touching, and a composite of these four, Affiliation. Significantly high positive correlations were found between the dominance ranks and the ranks for the variables Groomed By, overall Affiliation and Pair Proximity Scores.