Distance Decay Effect Research Articles

Extracting and analyzing semantic relatedness between cities using news articles

ABSTRACTNews articles capture a variety of topics about our society. They reflect not only the socioeconomic activities that happened in our physical world, but also some of the cultures, human interests, and public concerns that exist only in the perceptions of people. Cities are frequently mentioned in news articles, and two or more cities may co-occur in the same article. Such co-occurrence often suggests certain relatedness between the mentioned cities, and the relatedness may be under different topics depending on the contents of the news articles. We consider the relatedness under different topics as semantic relatedness. By reading news articles, one can grasp the general semantic relatedness between cities; yet, given hundreds of thousands of news articles, it is very difficult, if not impossible, for anyone to manually read them. This paper proposes a computational framework which can ‘read’ a large number of news articles and extract the semantic relatedness between cities. This framework is based on a natural language processing model and employs a machine learning process to identify the main topics of news articles. We describe the overall structure of this framework and its individual modules, and then apply it to an experimental dataset with more than 500,000 news articles covering the top 100 US cities spanning a 10-year period. We perform exploratory visualizations of the extracted semantic relatedness under different topics and over multiple years. We also analyze the impact of geographic distance on semantic relatedness and find varied distance decay effects. The proposed framework can be used to support large-scale content analysis in city network research.

Regional co-location pattern scoping on a street network considering distance decay effects of spatial interaction.

Regional co-location scoping intends to identify local regions where spatial features of interest are frequently located together. Most of the previous researches in this domain are conducted on a global scale and they assume that spatial objects are embedded in a 2-D space, but the movement in urban space is actually constrained by the street network. In this paper we refine the scope of co-location patterns to 1-D paths consisting of nodes and segments. Furthermore, since the relations between spatial events are usually inversely proportional to their separation distance, the proposed method introduces the “Distance Decay Effects” to improve the result. Specifically, our approach first subdivides the street edges into continuous small linear segments. Then a value representing the local distribution intensity of events is estimated for each linear segment using the distance-decay function. Each kind of geographic feature can lead to a tessellated network with density attribute, and the generated multiple networks for the pattern of interest will be finally combined into a composite network by calculating the co-location prevalence measure values, which are based on the density variation between different features. Our experiments verify that the proposed approach is effective in urban analysis.

Revisiting the death of geography in the era of Big Data: the friction of distance in cyberspace and real space

ABSTRACTMany scholars have argued that the importance of geographic proximity in human interactions has been diminished by the use of the Internet, while others disagree with this argument. Studies have noted the distance decay effect in both cyberspace and real space, showing that interactions occur with an inverse relationship between the number of interactions and the distance between the locations of the interactors. However, these studies rarely provide strong evidence to show the influence of distance on interactions in cyberspace, nor do they quantify the differences in the amount of friction of distance between cyberspace and real space. To fill this gap, this study used massive amounts of social media data (Twitter) to compare the influence of distance decay on human interactions between cyberspace and real space in a quantitative manner. To estimate the distance decay effect in both cyberspace and real space, the distance decay function of interactions in each space was modeled. Estimating the distance decay in cyberspace in this study can help predict the degree of information flow across space through social media. Measuring how far ideas can be diffused through social media is useful for users of location-based services, policy advocates, public health officials, and political campaigners.

Spatial effects of accessibility to parks on housing prices in Shenzhen, China

Accessibility to parks could be an important determinant of housing prices. This article applies the gravity model to calculate accessibility based on park classification in Shenzhen, China. Unlike most traditional studies that use the ratio method and nearest distance (including straight-line distance and network distance) to measure accessibility to given facilities, in this study, we use gravity-based accessibility by park type. Then, we explore the relationships between accessibility to parks and housing prices using a hedonic price model. In addition, we apply a geographical detector method to assess the association between housing price and related factors. The results indicate the following conclusions: (1) compared to traditional methods, the gravity model provides a more effective and objective measure of accessibility to parks because it considers distance decay effects, supply, and demand; (2) it is necessary and important to investigate the effects of the accessibility to different park types on housing prices; and (3) geographical detector models can efficiently detect correlations and interactions among housing prices and related factors.

The Rational Foraging Terrorist: Analysing the Distances Travelled to Commit Terrorist Violence

This paper applies the distance-to-crime approach to the case of Improvised Explosive Device (IED) and shooting attacks conducted by the Provisional Irish Republican Army (PIRA) during the Northern Ireland conflict, 1970–1998. The aim is to (a) measure the typical ‘distance to crime’ (b) detect whether a distance-decay effect is noticeable and (c) investigate whether there is a discernible difference in the distance traveled depending upon individual offender characteristics or aspects of how the offence was committed. In particular, it highlights that many of the same dynamics that influence offender decision making within the volume crime world, also apply within the terrorism realm. Five findings stand out in particular. First, a distance decay effect is identifiable. Second, younger offenders travel significantly smaller distances. Third, complex attacks typically involve greater distances. Fourth, our results show the ability of leading decision-makers within PIRA to impact upon the day-to-day operations of the field operatives. Together the results reinforce the argument that when we focus on terrorism from a preventative angle, we should focus on their behaviors: what they do rather than remain preoccupied with concerns about who they are and/or what they might be like. Collectively the results also highlight the fact that for a finer-grained understanding of terrorist behavior we need to disaggregate on a number of levels: within the cadre of operatives, across terrorist attacks, across targets and within conflicts.

Depicting urban boundaries from a mobility network of spatial interactions: a case study of Great Britain with geo-located Twitter data

ABSTRACTExisting urban boundaries are usually defined by government agencies for administrative, economic, and political purposes. However, it is not clear whether the boundaries truly reflect human interactions with urban space in intra- and interregional activities. Defining urban boundaries that consider socioeconomic relationships and citizen commute patterns is important for many aspects of urban and regional planning. In this paper, we describe a method to delineate urban boundaries based upon human interactions with physical space inferred from social media. Specifically, we depicted the urban boundaries of Great Britain using a mobility network of Twitter user spatial interactions, which was inferred from over 69 million geo-located tweets. We define the non-administrative anthropographic boundaries in a hierarchical fashion based on different physical movement ranges of users derived from the collective mobility patterns of Twitter users in Great Britain. The results of strongly connected urban regions in the form of communities in the network space yield geographically cohesive, nonoverlapping urban areas, which provide a clear delineation of the non-administrative anthropographic urban boundaries of Great Britain. The method was applied to both national (Great Britain) and municipal scales (the London metropolis). While our results corresponded well with the administrative boundaries, many unexpected and interesting boundaries were identified. Importantly, as the depicted urban boundaries exhibited a strong instance of spatial proximity, we employed a gravity model to understand the distance decay effects in shaping the delineated urban boundaries. The model explains how geographical distances found in the mobility patterns affect the interaction intensity among different non-administrative anthropographic urban areas, which provides new insights into human spatial interactions with urban space.

A co-location pattern-mining algorithm with a density-weighted distance thresholding consideration

With the rapid development of science technology, finding prevalent spatial patterns from urban facility data has gradually become an important issue in smart city applications. Co-location pattern mining is a valid method for identifying patterns whose instances are prevalent in geographical proximity. However, existing methods treat the space as homogeneous and non-directional, and both the proximity levels and connecting directions of instances are neglected during the mining process, causing the minded patterns to be unsatisfactory. Tobler's First Law indicates that the contributions of instances to their pattern's significance diminish as the distance decreases. Moreover, the directions in which instances are connected should also be considered due to the distance decay effects. Following these threads, this paper proposes a novel co-location pattern mining algorithm mixed with a density-weighted distance thresholding consideration. Based on the framework of our previous SGCT algorithm, which concentrates on efficiency and storage issues, this solution comprises an extension called SGCT-K, which is mainly concerned with improving the effectiveness of the mined results. It adopts a kernel-density-estimation-based model to measure the proximity level of different instance types and respects their directions during the instance connecting process. We implement experiments using both synthetic and real-facility datasets in Beijing, China. The results provide sound evidence that the algorithm improves both accuracy and sensitivity compared to previous methods.

Spatial Dependence and Determinants of Dairy Farmers’ Adoption of Best Management Practices for Water Protection in New Zealand

This paper analyses spatial dependence and determinants of the New Zealand dairy farmers' adoption of best management practices to protect water quality. A Bayesian spatial durbin probit model is used to survey data collected from farmers in the Waikato region of New Zealand. The results show that farmers located near each other exhibit similar choice behaviour, indicating the importance of farmer interactions in adoption decisions. The results also address that information acquisition is the most important determinant of farmers' adoption of best management practices. Financial problems are considered a significant barrier to adopting best management practices. Overall, the existence of distance decay effect and spatial dependence in farmers' adoption decisions highlights the importance of accounting for spatial effects in farmers' decision-making, which emerges as crucial to the formulation of sustainable agriculture policy.

The Political Cost? Religious Segregation, Peace Walls, and House Prices

The longstanding issue of discrimination and segregation in housing markets has enveloped housing market analysis and particularly the understanding of house price determination. Whilst the relationship of segregation in residential space is diverse and encompasses numerous taxonomies, in Northern Ireland, the high level of market segregation is compounded by the addition of tactile barriers such as Peace walls. The existence of these tactile barriers serves to physically segregate communities and have the capacity to prevent the restoration of normal community interactions and market processes. This paper attempts to quantify and measure the disamenity implications and costs of these hard structures on segregated communities. It achieves this by measuring the pricing effect of peace walls within segregated market areas within the Belfast housing market employing a hedonic pricing approach using data obtained from 3,836 house sales transactions over a one year period in 2014. The findings emerging from the research demonstrate that peace walls have clearly had a more detrimental and adverse effect on the consistency and application of policy and practice. Indeed, the existence of the peace walls appear to have occasioned or facilitated differential practices in housing strategies either ‘side of the wall’. The results show a clear differential distance decay effect limiting market and efficiency opportunities.

Distance-decay effect in stone tool transport by wild chimpanzees.

Stone tool transport leaves long-lasting behavioural evidence in the landscape. However, it remains unknown how large-scale patterns of stone distribution emerge through undirected, short-term transport behaviours. One of the longest studied groups of stone-tool-using primates are the chimpanzees of the Taï National Park in Ivory Coast, West Africa. Using hammerstones left behind at chimpanzee Panda nut-cracking sites, we tested for a distance-decay effect, in which the weight of material decreases with increasing distance from raw material sources. We found that this effect exists over a range of more than 2 km, despite the fact that observed, short-term tool transport does not appear to involve deliberate movements away from raw material sources. Tools from the millennia-old Noulo site in the Taï forest fit the same pattern. The fact that chimpanzees show both complex short-term behavioural planning, and yet produce a landscape-wide pattern over the long term, raises the question of whether similar processes operate within other stone-tool-using primates, including hominins. Where hominin landscapes have discrete material sources, a distance-decay effect, and increasing use of stone materials away from sources, the Taï chimpanzees provide a relevant analogy for understanding the formation of those landscapes.

Stable models for metastable systems? Lessons from sensitivity analysis of a Cellular Automata urban land use model

This research suggests that the degree of variability in a Cellular Automata (CA) urban land use model application may be linked to the application's suitability for modelling complex urban systems. Although highly stable models may be perceived as desirable, because they produce reliable, realistic-looking land use simulations, there is a risk that they may not be able to simulate true urban complexity. To test this hypothesis, variability was analysed through a sensitivity analysis in which a calibrated CA land use model application was modified repeatedly to produce a range of model variants with different characteristics. Since model scale is a key attribute known from literature to strongly influence model results, sensitivity analysis was conducted with reference to the scale-related elements (cell resolution, neighbourhood effect) in the model. Variation was found to be slight even between applications having widely differing cell resolutions and neighbourhood distance decay effects. It is contended that this is not an application-specific question, but a feature of these types of models more generally, where simple rules, strong constraints and a low degree of stochastic variation tend to produce highly stable simulation outcomes. To address the question of whether such stable model applications are really suitability for simulating urban complexity, the applications are discussed with relevance to three key indicators of complexity; 1) spontaneous emergence, 2) bifurcation; and 3) critical transitions. Finally, we ask whether the requirement of metastability necessary for calibration of such models violates the assumption of freedom from systemic constraints that would allow true complexity to be simulated. Some suggestions are made as to how these issues might be resolved in future, allowing a new generation of models to emerge.

Exploring inter-country connection in mass media: A case study of China

The development of theories and techniques for big data analytics offers tremendous possibility for investigating large-scale events and patterns that emerge over space and time. In this research, we utilize a unique open dataset “The Global Data on Events, Location and Tone” (GDELT) to model the image of China in mass media, specifically, how China has related to the rest of the world and how this connection has evolved upon time. The results of this research contribute to both the methodological and the empirical perspectives: We examined the effectiveness of the dynamic time warping (DTW) distances in measuring the differences between long-term mass media data. We identified four types of connection strength patterns between China and its top 15 related countries. With that, the distance decay effect in mass media is also examined and compared with social media and public transportation data. While using multiple datasets and focusing on mass media, this study generates valuable input regarding the interpretation of the diplomatic and regional correlation for the nation of China. It also provides methodological references for investigating international relations in other countries and regions in the big data era.

Exploring Multi-Scale Spatiotemporal Twitter User Mobility Patterns with a Visual-Analytics Approach

Understanding human mobility patterns is of great importance for urban planning, traffic management, and even marketing campaign. However, the capability of capturing detailed human movements with fine-grained spatial and temporal granularity is still limited. In this study, we extracted high-resolution mobility data from a collection of over 1.3 billion geo-located Twitter messages. Regarding the concerns of infringement on individual privacy, such as the mobile phone call records with restricted access, the dataset is collected from publicly accessible Twitter data streams. In this paper, we employed a visual-analytics approach to studying multi-scale spatiotemporal Twitter user mobility patterns in the contiguous United States during the year 2014. Our approach included a scalable visual-analytics framework to deliver efficiency and scalability in filtering large volume of geo-located tweets, modeling and extracting Twitter user movements, generating space-time user trajectories, and summarizing multi-scale spatiotemporal user mobility patterns. We performed a set of statistical analysis to understand Twitter user mobility patterns across multi-level spatial scales and temporal ranges. In particular, Twitter user mobility patterns measured by the displacements and radius of gyrations of individuals revealed multi-scale or multi-modal Twitter user mobility patterns. By further studying such mobility patterns in different temporal ranges, we identified both consistency and seasonal fluctuations regarding the distance decay effects in the corresponding mobility patterns. At the same time, our approach provides a geo-visualization unit with an interactive 3D virtual globe web mapping interface for exploratory geo-visual analytics of the multi-level spatiotemporal Twitter user movements.

Understanding the effects of administrative boundary in sampling spatially embedded networks

Abstract When analyzing spatially embedded networks, networks consisting of nodes and connections within an administrative boundary are commonly analyzed directly without considering possible errors or biases due to lost connections to nodes outside the network. However, connections exist not only within administrative boundaries but also to nodes outside of the boundaries. This study empirically analyzed the geographical boundary problem using a mobile communication network constructed based on mobile phone data collected in Heilongjiang province, China. We find that although many connections outside of the administrative boundary are lost, sampled networks based on administrative boundaries perform relatively well in terms of degree and clustering coefficient. We find that the mechanisms behind the reliability of these sampled networks include the effects of distance decay and cohesion strength in administrative regions on spatially embedded networks.

Geographical impacts on social networks from perspectives of space and place: an empirical study using mobile phone data

Space and place are two fundamental concepts in geography. Geographical factors have long been known as drivers of many aspects of people’s social networks. But whether and how space and place affect social networks differently are still unclear. The widespread use of location-aware devices provides a novel source for distinguishing the mechanisms of their impacts on social networks. Using mobile phone data, this paper explores the effects of space and place on social networks. From the perspective of space, we confirm the distance decay effect in social networks, based on a comparison between synthetic social ties generated by a null model and actual social ties derived from real-world data. From the perspective of place, we introduce several measures to evaluate interactions between individuals and inspect the trio relationship including distance, spatio-temporal co-occurrence, and social ties. We found that people’s interaction is a more important factor than spatial proximity, indicating that the spatial factor has a stronger impact on social networks in place compared to that in space. Furthermore, we verify the hypothesis that interactions play an important role in strengthening friendships.

Simulating infrastructure networks in the Yangtze River Delta (China) using generative urban network models

This paper explores the urban-geographical potential of simulation approaches combining spatial and topological processes. Drawing on Vertes et al.'s (2012) economical clustering model, we propose a generative network model integrating factors captured in traditional spatial models (e.g., gravity models) and more recently developed topological models (e.g., actor-oriented stochastic models) into a single framework. In our urban network-implementation of the generative network model, it is assumed that the emergence of inter-city linkages can be approximated through probabilistic processes that speak to a series of contradictory forces. Our exploratory study focuses on the outline of the infrastructure networks connecting prefecture-level cities in the highly urbanized Yangtze River Delta (China). Possible hampering factors in the emergence of these networks include distance and administrative boundaries, while stimulating factors include a measure of city size (population, gross domestic product) and a topological rule stating that the formation of connections between cities sharing nearest neighbors is more likely (i.e., a transitive effect). Based on our results, two wider implications of our research are discussed: (1) it confirms the potential of the proposed method in urban network simulation in that the inclusion of a topological factor alongside geographical factors generates an urban network that better approximates the observed network; (2) it allows exploring the differential extent to which driving forces influence the structure of different urban networks. For instance, in the Yangtze River Delta, transitivity plays a less important role in the Internet-network formation; GDP and boundaries more strongly affect the rail network; and distance decay effects play a more prominent role in the road network.

Integrating the spatial proximity effect into the assessment of changes in ecosystem services for biodiversity conservation

The assessment of the value of ecosystem services is a valuable tool for biodiversity conservation that can facilitate better environmental policy decision-making and land management, and can help land managers develop interventions to compensate for biodiversity loss at the patch level. Previous studies have suggested that it is appropriate to assess the value of biodiversity for conservation planning by considering both the condition of the landscape and the spatial configuration of adjacent land uses that can be reflected as a proximity effect. This research examines the influence of spatial proximity on biodiversity conservation from the ecosystem service perspective based on the assumption that the variation in the proximity effect caused by land cover change has positive or negative impacts on ecological services. Three factors related to the spatial characteristics of the landscape were considered in this approach: the relative artificiality of the land cover types, the distance decay effect of patches and the impact of one land cover type on others. The proximity effect change (PEC) parameter reflected the relationship between the spatial proximity effect and biodiversity conservation. The results of a quantitative and spatial comparative analysis of the proposed method and the conventional method in Yingkou for the periods of 2000–2005 and 2005–2010 showed that the former can account for the temporal and spatial changes in ecosystem services for biodiversity conservation that were caused by patch-level changes as well as the interaction between the altered and adjacent patches from a spatial perspective. The metric can also identify the most critical areas for biodiversity protection and inform the efficient allocation of limited land resources for nature conservation to maximize the benefit to biodiversity by guiding the process of land-use change, particularly urbanization and agriculture. Future studies should focus on the other important factors that are applicable to the assessment of the value of biodiversity conservation in socio-ecological systems, where society and nature are mutually capable of fulfilling their roles.

Parasite community similarity in Athabasca River trout-perch (Percopsis omiscomaycus) varies with local-scale land use and sediment hydrocarbons, but not distance or linear gradients.

Parasite communities have been shown to be structured by processes at scales ranging from continental to microhabitat, but few studies have simultaneously considered spatial and environmental variables, measured at different scales, to assess their relative influences on parasite abundance, species richness, and community similarity. Parasite abundance, diversity, and community similarity in Athabasca River trout-perch (Percopsis omiscomaycus) were examined in relation to water quality, substrate profile, metal and organic compound levels in water and sediment, and landscape use patterns at different scales, as well as distance among sites and upstream-downstream position along the river. Although species richness did not differ among sites, there were significant differences in abundance of individual taxa and community structure. We observed a shift from communities dominated by larval trematodes Diplostomum spp. to domination by gill monogeneans Urocleidus baldwini, followed by a reversion further downstream. Variations in the abundance of these taxa and of overall community similarity were strongly correlated with sediment hydrocarbons (alkanes and polycyclic aromatic hydrocarbons (PAHs)) as well as landscape use within 5km of study sites. No correlations were noted with any other predictors, indicating that parasite populations and communities in this system were likely primarily influenced by habitat level and landscape-scale filters, rather than larger-scale processes such as distance decay or river continuum effects.

Spatial co-location pattern mining of facility points-of-interest improved by network neighborhood and distance decay effects

ABSTRACTThe aim of mining spatial co-location patterns is to find the corresponding subsets of spatial features that have strong spatial correlation in the real world. This is an important technology for the extraction and comprehension of implicit knowledge in large spatial databases. However, existing methods of co-location mining consider events as taking place in a homogeneous and isotropic context in Euclidean space, whereas the physical movement in an urban space is usually constrained by a road network. Furthermore, previous works do not take the ‘distance decay effect’ of spatial interactions into account, which may reduce the effectiveness of the result. Here we propose an improved spatial co-location pattern mining method, including the network-constrained neighborhood and addition of a distance-decay function, to find the spatial dependence between network phenomena (e.g. urban facilities). The underlying idea is to utilize a model function in the interest measure calculation to weight the contribution of a co-location to the overall interest measure instance inversely proportional to the separation distance. Our approach was evaluated through extensive experiments using facility points-of-interest data sets. The results show that the network-constrained approach is a more effective method than the traditional one in network-structured space. The proposed approach can also be applied to other human activities (e.g. traffic accidents) constrained by a street network.

Analyzing Urban Human Mobility Patterns through a Thematic Model at a Finer Scale

Taxi trajectories reflect human mobility over a road network. Pick-up and drop-off locations in different time periods represent origins and destinations of trips, respectively, demonstrating the spatiotemporal characteristics of human behavior. Each trip can be viewed as a displacement in the random walk model, and the distribution of extracted trips shows a distance decay effect. To identify the spatial similarity of trips at a finer scale, this paper investigates the distribution of trips through topic modeling techniques. Firstly, trip origins and trip destinations were identified from raw GPS data. Then, different trips were given semantic information, i.e., link identification numbers with a semantic enrichment process. Each taxi trajectory was composed of a series of trip destinations corresponding to the same taxi. Subsequently, each taxi trajectory was analogous to a document consisting of different words, and all taxi’s trajectories could be regarded as document corpora, enabling a semantic analysis of massive trip destinations. Finally, we obtained different trip destination topics reflecting the spatial similarity and regional property of human mobility through LDA topic model training. The effectiveness of this approach was illustrated by a case study using a large dataset of taxi trajectories collected from 2 to 8 June 2014 in Wuhan, China.