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Copyright of Architectural Journal / Jian Zhu Xue Bao is the property of Architectural Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We demonstrate how sophisticated graph properties, such as small distances and scale-free degree distributions, arise naturally from a reinforcement mechanism on layered graphs. Every node is assigned an a-priori i.i.d. fitness with max-stable distribution. The fitness determines the node attractiveness w.r.t. incoming edges as well as the spatial range for outgoing edges. For max-stable fitness distributions, we thus obtain a complex spatial network, which we coin extremal linkage network. [ABSTRACT FROM AUTHOR]

Abstract The spatial distribution of population affects disease transmission, especially when shelter in place orders restrict mobility for a large fraction of the population. The spatial network structure of settlements therefore imposes a fundamental constraint on the spatial distribution of the population through which a communicable disease can spread. In this analysis we use the spatial network structure of lighted development as a proxy for the distribution of ambient population to compare the spatiotemporal evolution of COVID-19 confirmed cases in the USA and China. The Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band sensor on the NASA/NOAA Suomi satellite has been imaging night light at ~ 700 m resolution globally since 2012. Comparisons with sub-kilometer resolution census observations in different countries across different levels of development indicate that night light luminance scales with population density over ~ 3 orders of magnitude. However, VIIRS’ constant ~ 700 m resolution can provide a more detailed representation of population distribution in peri-urban and rural areas where aggregated census blocks lack comparable spatial detail. By varying the low luminance threshold of VIIRS-derived night light, we depict spatial networks of lighted development of varying degrees of connectivity within which populations are distributed. The resulting size distributions of spatial network components (connected clusters of nodes) vary with degree of connectivity, but maintain consistent scaling over a wide range (5 × to 10 × in area & number) of network sizes. At continental scales, spatial network rank-size distributions obtained from VIIRS night light brightness are well-described by power laws with exponents near −2 (slopes near −1) for a wide range of low luminance thresholds. The largest components (104 to 105 km2) represent spatially contiguous agglomerations of urban, suburban and periurban development, while the smallest components represent isolated rural settlements. Projecting county and city-level numbers of confirmed cases of COVID-19 for the USA and China (respectively) onto the corresponding spatial networks of lighted development allows the spatiotemporal evolution of the epidemic (infection and detection) to be quantified as propagation within networks of varying connectivity. Results for China show rapid nucleation and diffusion in January 2020 followed by rapid decreases in new cases in February. While most of the largest cities in China showed new confirmed cases approaching zero before the end of February, most of these cities also showed distinct second waves of cases in March or April. Whereas new cases in Wuhan did not approach zero until mid-March, as of December 2020 it has not yet experienced a second wave of cases. In contrast, the results for the USA show a wide range of trajectories, with an abrupt transition from slow increases in confirmed cases in a small number of network components in January and February, to rapid geographic dispersion to a larger number of components shortly before mobility reductions occurred in March. Results indicate that while most of the upper tail of the network had been exposed by the end of March, the lower tail of the component size distribution has only shown steep increases since mid-June.

The interurban network of criminal collaboration in Canada is described, and possible explanations for its structure are explored. The data include all police-reported co-offences in the 32 major cities of Canada during 2006–09. Component analysis and graph drawings in network space and in geospace elucidate the structure of the network. Quadratic assignment procedure multiple regressions, repeated separately on the networks of instrumental and noninstrumental co-offences, test hypotheses about possible determinants of the network structure. The cities form one connected component, containing two clusters connected by a link between Toronto and Vancouver. One cluster, centred on the triad of Toronto, Montreal, and Ottawa, comprises the cities in Ontario and Quebec, with weak links to cities in the Atlantic provinces. The other cluster, centred on Vancouver, comprises the cities in the four western provinces. The structure is strongly correlated with the residential mobility of the general population, which in turn is strongly correlated with intercity distances. The correlation with mobility is less strong for instrumental than for noninstrumental crimes. The structure of this co-offending network can be explained by criminals' routine activities, namely ordinary residential mobility, but the alternative explanation of purposive interurban criminal collaboration is more plausible for instrumental crime. [ABSTRACT FROM AUTHOR]

Urban areas are expanding globally with far-reaching ecological consequences, including for wildlife-pathogen interactions. Wildlife show tremendous variation in their responses to urbanization; even within a single population, some individuals can specialize on urban or natural habitat types. This specialization could alter pathogen impacts on host populations via changes to wildlife movement and aggregation. Here, we build a mechanistic model to explore how habitat specialization in urban landscapes affects interactions between a mobile host population and a density-dependent specialist pathogen that confers no immunity. We model movement on a network of resource-stable urban sites and resource-fluctuating natural sites, where hosts are urban specialists, natural specialists, or generalists that use both patch types. We find that for generalists, natural and partially urban landscapes produce the highest infection prevalence and mortality, driven by high movement rates at natural sites and high densities at urban sites. However, habitat specialization protects hosts from these negative effects of partially urban landscapes by limiting movement between patch types. These findings suggest that habitat specialization can benefit populations by reducing infectious disease transmission, but by reducing movement between habitat types it could also carry the cost of reducing other movement-related ecosystem functions, such as seed dispersal and pollination. [ABSTRACT FROM AUTHOR]

Abstract In this study, we propose novel centrality measures considering multiple perspectives of nodes or node groups based on the facility location problem on a spatial network. The conventional centrality exclusively quantifies the global properties of each node in a network such as closeness and betweenness, and extracts nodes with high scores as important nodes. In the context of facility placement on a network, it is desirable to place facilities at nodes with high accessibility from residents, that is, nodes with a high score in closeness centrality. It is natural to think that such a property of a node changes when the situation changes. For example, in a situation where there are no existing facilities, it is expected that the demand of residents will be satisfied by opening a new facility at the node with the highest accessibility, however, in a situation where there exist some facilities, it is necessary to open a new facility some distance from the existing facilities. Furthermore, it is natural to consider that the concept of closeness differs depending on the relationship with existing facilities, cooperative relationships and competitive relationships. Therefore, we extend a concept of centrality so as to considers the situation where one or more nodes have already been selected belonging to one of some groups. In this study, we propose two measures based on closeness centrality and betweenness centrality as behavior models of people on a spatial network. From our experimental evaluations using actual urban street network data, we confirm that the proposed method, which introduces the viewpoints of each group, shows that there is a difference in the important nodes of each group viewpoint, and that the new store location can be predicted more accurately.

The settlements in pastoral areas have the spatial distribution characteristics of “broad scattering and small agglomeration”. The extension of range makes their physical information attributes change into a spatial awareness with psychological information attributes. In this paper, the psychological cognitive range in herdsmen’s spatial awareness was taken as the basis for establishing a spatial network and the social network analysis method was used to quantitatively study the spatial network relationship of settlements and the layout of public service facilities in pastoral areas of Inner Mongolia. The results show that the construction of control equilibrium point of interaction between spatial characteristics of settlements and public service facilities in pastoral areas can be rebuilt by using 10 km psychological cognitive range information; the zoning characteristics of spatial settlement network constructed on the basis of Pajek social network and ArcGIS spatial analysis are obvious; It is of great theoretical and practical significance for pastoral areas and low population density areas to take the index of network node degree as the basis of constructing dynamic layer layouts of public service facilities network and to establish “spatial Network + dynamic layout” layer model of public service facilities layout according to the characteristics of spatial network zoning.

Abstract Illicit wildlife trafficking poses a threat to the conservation of species and ecosystems, and represents a fundamental source of biodiversity loss, alongside climate change and large-scale land degradation. Despite the seriousness of this issue, little is known about various socio-cultural demand sources underlying trafficking networks, for example the forthright consumption of endangered species on different cultural contexts. Our study illustrates how wildlife trafficking represents a wicked problem at the intersection of criminal enforcement, cultural heritage and environmental systems management. As with similar network-based crimes, institutions are frequently ineffective at curbing wildlife trafficking, partly due to the lack of information detailing activities within illicit trading networks. To address this shortcoming, we leverage official government records documenting the illegal trade of reptiles in Colombia. As such, our study contributes to the understanding of how and why wildlife trafficking persists across robust trafficking networks, which are conduits for a broader range of black-market goods. Leveraging geo-spatial data, we construct a multiplex representation of wildlife trafficking networks, which facilitates identifying network properties that are signatures of strategic trafficker behavior. In particular, our results indicate that traffickers’ actions are constrained by spatial and market customs, a result which is apparent only within an integrated multiplex representation. Characteristic levels of sub-network coupling further indicate that traffickers strategically leverage knowledge of the entire system. We argue that this multiplex representation is essential for prioritizing crime enforcement strategies aimed at disrupting robust trade networks, thereby enhancing the effectiveness and resources allocation of institutions charged with curbing illicit trafficking. We develop a generalizable model of multiplex criminal trade networks suitable for communicating with policy makers and practitioners, thereby facilitating rapid translation into public policy and environmental conservation efforts.

City systems are characterized by the functional organization of cities on a regional or country scale. While there is a relatively good empirical and theoretical understanding of city size distributions, insights about their spatial organization remain on a conceptual level. Here, we analyze empirically the correlations between the sizes of cities (in terms of area) across long distances. Therefore, we (i) define city clusters, (ii) obtain the neighborhood network from Voronoi cells, and (iii) apply a fluctuation analysis along all shortest paths. We find that most European countries exhibit long-range correlations but in several cases these are anti-correlations. In an analogous way, we study a model inspired by Central Places Theory and find that it leads to positive long-range correlations, unless there is strong additional spatial disorder-contrary to intuition. We conclude that the interactions between cities extend over large distances reaching the country scale. Our findings have policy relevance as urban development or decline can affect cities at a considerable distance., (© The Author(s) 2024. Published by Oxford University Press on behalf of National Academy of Sciences.)

Background: A public elementary school has traditionally functioned as an important center of a neighborhood, but this role has diminished with sprawling urban developments. Despite the large number of studies of children's walking to/from school (WTS), the school's location in relation to the larger neighborhood context has not been fully explored. This study is to examine the relationship between school's spatial centrality and children's WTS in urban, suburban and rural settings. Methods: this study used school travel tally (11,721 students), environment audit, GIS and census data from 71 elementary school/neighborhoods in Texas, and employed the closeness centrality index to estimate a school's spatial centrality. Data were collected from 2009-2012. Results: After controlling for neighborhood characteristics, it was found that more centrally located schools are likely to have higher proportions of WTS in the neighborhoods. And, among urban, suburban and rural settings, urban schools were the most and rural schools were the least likely to be centrally-located in the neighborhoods. Conclusions: The findings offer implications on school and community planning policies that can help promote WTS. Spatial centrality measures can be effective tools to identify environmental factors in complex urban networks related to human behaviors and community-based activities. [ABSTRACT FROM AUTHOR]

The interaction of regional ecological efficiency is important for promoting ecological efficiency. Using a gravity model and social network analysis, this study investigated the spatial network characteristics of the sustainable total-factor ecology efficiency (STFEcE) in 30 provinces of China from 2005 to 2016 for the first time. The quadratic assignment procedure (QAP) was also used to analyze the factors affecting the network. The results are as follows. (1) The STFEcE between regions exhibited a spatial network relationship. (2) Jiangsu, Guangdong, Shandong, Ningxia, and other provinces were in the center of the network, whereas Guangxi, Anhui, and other provinces were on the edge. (3) The 30 provinces were divided into four plates, and the connections in the network were primarily based on the relationship between plates. (4) The difference between urban population, energy structure, and technical advancement negatively impacted the network relationship. The provinces should fully understand the value of the STFEcE network and implement appropriate measures to achieve collaborative improvement of regional ecological efficiency according to their roles in the network. [ABSTRACT FROM AUTHOR]

We propose a general framework of opening and closing shops in group competitive environment, i.e., shops in the same group work cooperatively and those in different groups competitively, based on stochastic utility which is given by a function of shop distance and attractiveness with an explicit traveling time-bound imposed. The framework allows to derive a specific prediction model by choosing a specific function form of utility, including the one we propose, its variants and the conventional state-of-the-art gravity model which we chose as a reference. We compute a marginal gain of the market share which is derived from the utility function and the consumers buying power as a measure to rank the candidate location. Using the real dataset of three convenience store groups in four cities in Japan, we analyzed how the derived models behave with respect to the time-bound and the other parameters and how each model compare with others. We confirm that, despite the simplification we made in the model, inferred rankings of the shops newly opened in the real data are shown to be high implying that our prediction model and other variants are reasonable. We show that our model gives much more realistic results than the gravity model, which indicates that our group competitive mechanism with the time-bounded stochastic utility is vital and promising. Inclusion of the time-bound constraint is crucially important. Analyses of the dynamics of opening and closing shops indicate that competition indeed affects the market share of each group over time, and the total share eventually increases although small, and the difference of the share within each group gradually becomes smaller, revealing that the spatial distribution of the shops in each group becomes more uniform. [ABSTRACT FROM AUTHOR]

Copyright of Tropical Geography is the property of Tropical Geography Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Self-organized patterns with abundant structure characteristics in epidemiology are of great significance. The reaction–diffusion (RD) equations in continuous space have made plentiful achievements in exploration of this direction, but their spatiotemporal dynamics are limitedly supported in essence by the RD equations defined on a class of regular lattices as their counterparts discretized in space. However, patterns in complex spatial networks beyond lattice networks remain largely unexplored. In this paper, we creatively develop an epidemic reaction–diffusion model defined on our well-designed basic and modified spatially embedded networks to investigate the epidemiological patterns in spatial networks. We apply some basic properties of the Kronecker product to determine the eigenvalues and their corresponding eigenvectors of a high-dimensional matrix, which leads us to derive the necessary and sufficient conditions for Turing instability. With series and groups of comparative simulations, we systematically study the influence of factors including network size, nonlocal connectivity, asymmetrical connectivity, degree heterogeneity and randomly connected links on the pattern formations in spatial networks, and obtain some scarcely documented results deepening and broadening our understanding about the epidemiological patterns in space and networks. Especially, we find that the degree heterogeneity in spatial networks whose degrees of nodes follow even the power law distribution does not trigger the essential change of pattern types. Remarkably, the randomly connected links in spatial networks act as a mechanism to induce irregular stationary patterns being substitutes for regular ones, and narrow prevalence difference of diseases in the whole spatial networks, even prevent the occurrence of Turing instability. [ABSTRACT FROM AUTHOR]

The problem of network disintegration, such as suppression of an epidemic spread and destabilization of terrorist networks, possesses extensive applications and has lately been the focus of growing interest. Many real-world complex systems are represented by spatial networks in which nodes and edges are spatially embedded. However, existing disintegration approaches for spatial network disintegration focus on singular aspects such as geospatial information or network topography, with insufficient modeling granularity. In this paper, we propose an effective and computationally efficient virtual node model that essentially integrates the geospatial information and topology of the network by modeling edges as virtual nodes with weights. Moreover, we employ Kernel Density Estimation, a well-known non-parametric technique for estimating the underlying probability density function of samples, to fit all nodes, comprising both network and virtual nodes, to identify the critical region of the spatial network, which is also the circular geographic region where disintegration occurs. Extensive numerical experiments on synthetic and real-world networks demonstrate that our method outperforms existing methods in terms of both effectiveness and efficiency, which provides a fresh perspective for modeling spatial networks. • Introduces a virtual node model integrating geospatial data and topology in spatial network disintegration. • Employs Kernel Density Estimation for identifying critical regions in spatial networks. • Demonstrates superior effectiveness and efficiency over existing methods in real-world applications. • Proposes adaptive method linking disintegration circle radius with method parameters. • Provides new insights for spatial network modeling. [ABSTRACT FROM AUTHOR]

Abstract In a metropolis, people movements design intricate patterns that change on very short temporal scales. Population mobility obviously is not random, but driven by the land uses of the city. Such an urban ecosystem can interestingly be explored by integrating the spatial analysis of land uses (through ecological indicators commonly used to characterize natural environments) with the temporal analysis of human mobility (reconstructed from anonymized mobile phone data). Considering the city of Milan (Italy) as a case study, here we aimed to identify the complex relations occurring between the land-use composition of its neighborhoods and the spatio-temporal patterns of occupation made by citizens. We generated two spatially explicit networks, one static and the other temporal, based on the analysis of land uses and mobile phone data, respectively. The comparison between the results of community detection performed on both networks revealed that neighborhoods that are similar in terms of land-use composition are not necessarily characterized by analogous temporal fluctuations of human activities. In particular, the historical concentric urban structure of Milan is still under play. Our big data driven approach to characterize urban diversity provides outcomes that could be important (i) to better understand how and when urban spaces are actually used, and (ii) to allow policy makers improving strategic development plans that account for the needs of metropolis-like permanently changing cities.

Abstract Many networks including spatial networks, social networks, and web networks, are not deterministic but probabilistic due to the uncertainty of link existence. From networks with such uncertainty, to extract densely connected nodes, we propose connectedness centrality and its extended version, group connectedness centrality, where the connectedness of each node is defined as the expected size of its connected component over all possible graphs produced by an uncertain graph. In a large-scale network, however, since the number of combinations of possible graphs is enormous, it is difficult to strictly calculate the expected value. Therefore, we also propose an efficient estimation method based on Monte Carlo sampling. When applying our method to road networks, the extracted nodes can be regarded as candidate sites of evacuation facilities that many residents can reach even in the situation where roads are stochastically blocked by natural disasters. In our experimental evaluations using actual road networks, we show the following promising characteristics: our proposed method 1) works stably with respect to the number of simulations; 2) extracts nodes set reachable from more nodes even in a situation that many links are deleted; and 3) computes much more efficient, compared to existing centrality measures and community extraction methods.

The spatial arrangement of habitat patches in a metapopulation and the dispersal connections among them influence metapopulation persistence. Metapopulation persistence emerges from a dynamic process, namely the serial extinctions and recolonizations of local habitat patches, while measures of persistence are typically based solely on structural properties of the spatial network (e.g., spatial distance between sites). Persistence estimators based on static properties may be unable to capture the dynamic nature of persistence. Understanding the shape of the distribution of extinction times is a central goal in population ecology. Here, we examine the goodness of fit of the power law to patch persistence time distributions using data on a foundational metapopulation system—the Glanville fritillary butterfly in the Åland islands. Further, we address the relationship between structural measures of metapopulation persistence (i.e., metapopulation capacity) and our temporal distributional fits to patch persistence times based on a power law. Patch persistence time distributions were well fit by a power law for the majority of semi-independent networks. Power law fits to persistence time distributions were related to metapopulation capacity, linking structural and temporal measures of metapopulation persistence. Several environmental variables and measures of network topology were correlated with both measures of metapopulation persistence, though correlations tended to be stronger for the structural measure of metapopulation persistence (i.e., metapopulation capacity). Together, our findings suggest that persistence time distributions are useful dynamic properties of metapopulations, and provide evidence of a relationship between metapopulation structure and metapopulation dynamics. [ABSTRACT FROM AUTHOR]

The movement of atmospheric air masses can be seen as a continuous flow of gases and particles hovering over our planet, and it can be locally simplified by means of three-dimensional trajectories. These trajectories can hence be seen as a way of connecting distant areas of the globe during a given period of time. In this paper we present a mathematical formalism to construct spatial and spatiotemporal networks where the nodes represent the subsets of a partition of a geographical area and the links between them are inferred from sampled trajectories of air masses passing over and across them. We propose different estimators of the intensity of the links, relying on different bio-physical hypotheses and covering adjustable time periods. This construction leads to a new definition of spatiotemporal networks characterized by adjacency matrices giving, e.g., the probability of connection between distant areas during a chosen period of time. We applied our methodology to characterize tropospheric connectivity in two real geographical contexts: the watersheds of the French region Provence-Alpes-Côte d’Azur and the coastline of the Mediterranean Sea. The analysis of the constructed networks allowed identifying a marked seasonal pattern in air mass movements in the two study areas. If our methodology is applied to samples of air-mass trajectories, with potential implications in aerobiology and plant epidemiology, it could be applied to other types of trajectories, such as animal trajectories, to characterize connectivity between different components of the landscape hosting the animals.

Real-world road networks have an approximate scale-invariance property; can one devise mathematical models of random networks whose distributions are exactly invariant under Euclidean scaling? This requires working in the continuum plane. We introduce an axiomatization of a class of processes we call "scale-invariant random spatial networks", whose primitives are routes between each pair of points in the plane. We prove that one concrete model, based on minimum-time routes in a binary hierarchy of roads with different speed limits, satisfies the axioms, and note informally that two other constructions (based on Poisson line processes and on dynamic proximity graphs) are expected also to satisfy the axioms. We initiate study of structure theory and summary statistics for general processes in this class.

Context: Metapopulation theory makes useful predictions for conservation in fragmented landscapes. For randomly distributed habitat patches, it predicts that the ability of a metapopulation to recover from low occupancy level (the "metapopulation capacity") linearly increases with habitat amount. This prediction derives from describing the dispersal between two patches as a function of their features and the distance separating them only, without interaction with the rest of the landscape. However, if individuals can stop dispersal when hitting a patch ("habitat detection and settling" ability), the rest of habitat may modulate the dispersal between two patches by intercepting dispersers (which constitutes a "shadow" effect). Objectives: We aim at evaluating how habitat detection and settling ability, and the subsequent shadow effect, can modulate the relationship between the metapopulation capacity and the habitat amount in the metapopulation. Methods: Considering two simple metapopulation models with contrasted animal movement types, we used analytical predictions and simulations to study the relationship between habitat amount and metapopulation capacity under various levels of dispersers' habitat detection and settling ability. Results: Increasing habitat detection and settling ability led to: (i) larger metapopulation capacity values than expected from classic metapopulation theory and (ii) concave habitat amount–metapopulation capacity relationship. Conclusions: Overlooking dispersers' habitat detection and settling ability may lead to underestimating the metapopulation capacity and misevaluating the conservation benefit of increasing habitat amount. Therefore, a further integration of our mechanistic understanding of animals' displacement into metapopulation theory is urgently needed. [ABSTRACT FROM AUTHOR]

This paper considers k-farthest neighbor (kFN) join queries in spatial networks where the distance between two points is the length of the shortest path connecting them. Given a positive integer k, a set of query points Q, and a set of data points P, the kFN join query retrieves the k data points farthest from each query point in Q. There are many real-life applications using kFN join queries, including artificial intelligence, computational geometry, information retrieval, and pattern recognition. However, the solutions based on the Euclidean distance or nearest neighbor search are not suitable for our purpose due to the difference in the problem definition. Therefore, this paper proposes a cluster nested loop join (CNLJ) algorithm, which clusters query points (data points) into query clusters (data clusters) and reduces the number of kFN queries required to perform the kFN join. An empirical study was performed using real-life roadmaps to confirm the superiority and scalability of the CNLJ algorithm compared to the conventional solutions in various conditions.

Abstract Dividing a geographical region into some subregions with common characteristics is an important research topic, and has been studied in many research fields such as urban planning and transportation planning. In this paper, by network analysis approach, we attempt to extract functionally similar regions, each of which consists of functionally similar nodes of a road network. For this purpose, we previously proposed the Functional Cluster Extraction method, which takes a large amount of computation time to output clustering results because it treats too many high-dimensional vectors. To overcome this difficulty, we also previously proposed a transfer learning-based clustering method that selects approximate medoids from the target network using the K medoids of a previously clustered network and divides all the nodes into K clusters. If we select an appropriate network with similar structural characteristics, this method produces highly accurate clustering results. However it is difficult to preliminarily know which network is appropriate. In this paper, we extend this method to ensure accuracy using the K medoids of multiple networks rather than a specific network. Using actual urban streets, we evaluate our proposed method from the viewpoint of the improvement degree of clustering accuracy and computation time.

We review mathematically tractable models for connected networks on random points in the plane, emphasizing the class of proximity graphs which deserves to be better known to applied probabilists and statisticians. We introduce and motivate a particular statistic R measuring shortness of routes in a network. We illustrate, via Monte Carlo in part, the trade-off between normalized network length and R in a one-parameter family of proximity graphs. How close this family comes to the optimal trade-off over all possible networks remains an intriguing open question. The paper is a write-up of a talk developed by the first author during 2007-2009. © Institute of Mathematical Statistics, 2010.

Travel decisions are fundamental to understanding human mobility, urban economy, and sustainability, but measuring it is challenging and controversial. Previous studies of taxis are limited to taxi stands or hail markets at aggregate spatial units. Here we estimate the dynamic demand and supply of taxis in New York City (NYC) at street segment level, using in-vehicle Global Positioning System (GPS) data which preserve individual privacy. To this end, we model taxi demand and supply as non-stationary Poisson random fields on the road network, and pickups result from income-maximizing drivers searching for impatient passengers. With 868 million trip records of all 13,237 licensed taxis in NYC in 2009 – 2013, we show that while taxi demand are almost the same in 2011 and 2012, it declined about 2% in spring 2013, possibly caused by transportation network companies (TNCs) and fare raise. Contrary to common impression, street-hail taxis out-perform TNCs such as Uber in high-demand locations, suggesting a taxi/TNC regulation change to reduce congestion and pollution. We show that our demand estimates are stable at different supply levels and across years, a property not observed in existing matching functions. We also validate that taxi pickups can be modeled as Poisson processes. Our method is thus simple, feasible, and reliable in estimating street-hail taxi activities at a high spatial resolution; it helps quantify the ongoing discussion on congestion charges to taxis and TNCs. [ABSTRACT FROM AUTHOR]

This study aims to interpret the relationships among the various actors (human including residents, tourists, artists, and so on, and non‐human environments including blogs, topographical features, and destination images) at a tourist site, in which those actors come together. Using the perspectives of actor‐network theory, the research observes how networks surrounding destinations are formed and changed by human and non‐human actors. It was discovered that once a tourism‐industry‐directed network had been newly introduced, the actors actively participated in network activities to achieve continuing benefits greater than those that would have been possible without this network. [ABSTRACT FROM AUTHOR]

This paper provides evidence about the relevant role of geography on spatial local networks in trade credit between industrial reduced size companies and distin- guishing the spatial behavior according to the firms' size and technological intensity. Using data from a survey of small and medium companies in the municipality of Madrid (Spain), we find significant spatial effects in trade credit between spatially close industrial companies. Whereas prior work has examined the temporal dynamics in trade credit with aggregated information, the spatial dimension with firm level data has not been considered before in trade credit studies and is the aim of this study. We find significant results for the spatio-temporal networks between geographically close companies which cause interaction effects in the proposed models by concluding that firms' surrounding local characteristics impact the issue of trade credit for each company. [ABSTRACT FROM AUTHOR]

Protein structures are complex spatial systems, formed by the three-dimensional arrangement of amino acids, that interact through atomic contacts. It is fundamental to understand the perturbation mechanisms associated with the amino acid mutations that lead to changes in a protein dynamics, as such mutations can lead to diseases. We present a methodology based on Amino Acid Networks and the use of Induced Perturbation Networks to infer the impact of amino acid mutations on a protein's dynamics and the use of a tiling formalism to model protein aggregation. We apply this methodology to the case study of the L55P Transthyretin (TTR) variant, one of the most pathogenic mutations of TTR, due to its increased tendency to aggregate into amyloid fibers. We show that another pathogenic variant, V30M TTR, produces different results, reflecting a different pathogenic mechanism compared to L55P TTR and that the results differ for pathogenic and non-pathogenic variants (L55P and V30M TTR pathogenic variants versus Tl 19Y and Tl 19M non-pathogenic variants). [ABSTRACT FROM AUTHOR]

This study aimed to investigate the association of spatial configuration with social interaction for elderly. A social housing in Seoul was selected for the case study. Using space syntax and social network analysis, the association was examined statistically. This research employed an integration indicator which is most closely related to space use pattern. Questionnaire and interview surveys were conducted to illustrate the pattern of social network. Using the collected data, NetMiner was utilized to conduct a quantitative analysis. Degree, closeness and betweenness indicators were employed to measure relationships in these networks and between individuals. The characteristics of the association established by the statistical analysis between spatial network of housing estate and social network of elderly were discussed. Our results show that spatial network properties can explain characteristics of social network. The accessibility of residential spaces for elderly individuals in social housing apartment complex has an effect on the strength of the social network with neighbours. Also, analysis of the spatial configuration accessibility for the elderly population with integration values has illustrated that the result was opposite to the general theory that 'the locations with high accessibility could foster more interactions'. Our findings have suggested that we can have a better knowledge to foster more social network among elderly by planning improved spatial network. [ABSTRACT FROM AUTHOR]

In designing a network to link n points in a square of area n, we might be guided by the following two desiderata. First, the total network length should not be much greater than the length of the shortest network connecting all points. Second, the average route length (taken over source-destination pairs) should not be much greater than the average straight-line distance. How small can we make these two excesses? Speaking loosely, for a nondegenerate configuration, the total network length must be at least of order n and the average straight-line distance must be at least of order n1/2, so it seems implausible that a single network might exist in which the excess over the first minimum is o(n) and the excess over the second minimum is o(n1/2). But in fact we can do better: for an arbitrary configuration, we can construct a network where the first excess is o(n) and the second excess is almost as small as O (log n). The construction is conceptually simple and uses stochastic methods: over the minimum-length connected network (Steiner tree) superimpose a sparse stationary and isotropic Poisson line process. Together with a few additions (required for technical reasons), the mean values of the excess for the resulting random network satisfy the above asymptotics; hence, a standard application of the probabilistic method guarantees the existence of deterministic networks as required (speaking constructively, such networks can be constructed using simple rejection sampling). The key ingredient is a new result about the Poisson line process. Consider two points a distance r apart, and delete from the line process all lines which separate these two points. The resulting pattern of lines partitions the plane into cells; the cell containing the two points has mean boundary length approximately equal to 2r + constant(log r). Turning to lower bounds, consider a sequence of networks in [0√n-]2 satisfying a weak equidistribution assumption. We show that if the first excess is O (n) then the second excess cannot be o(√log n). © Applied Probability Trust 2008.