Spatial localization of closeness and betweenness measures: a self-contradictory but useful form of network analysis

Closeness and betweenness are forms of spatial network analysis grounded in a long-standing tradition of measuring accessibility and flow potential. More recently, these measures have been enhanced by the concept of spatial localization, producing effective models for the prediction of pedestrian and vehicle driver behaviour.

A contradiction arises where the distance metric used to define locality does not match the distance metric used to define shortest paths for closeness and betweenness. A typical case is the use of angular shortest paths within a Euclidean buffer as a pedestrian flow model. Such a model assumes that people make a mode choice based on distance, but a route choice based on least angular change – even when this results in an excessively long ‘problem route’, which conflicts with their criterion for mode choice.

This study examines the prevalence of problem routes and the magnitude of their effect on some pedestrian and vehicle models. We show that while in a weighted analysis, pathological cases could invalidate an entire model, in the models presented the effect of this contradiction is minor. We do this by comparing model predictions to real flow data, using four strategies for handling problem routes: ignore, discard, reroute and strict locality. Strict locality is justified on the grounds of bounded rationality. We find all strategies to give broadly similar results, although the reroute and strict strategies give marginally better simulation accuracy. We also present a discussion of the characteristics of each strategy, and findings on computational efficiency.

We conclude that it is prudent in any computation of localized closeness and betweenness to consider the impact of problem routes; however, they do not necessarily invalidate these forms of analysis, which remain useful.     

The discrete dynamics of small-scale spatial events: agent-based models of mobility in carnivals and street parades

Small-scale spatial events are situations in which elements or objects vary in such a way that temporal dynamics are intrinsic to their representation and explanation. Some of the clearest examples involve local movement, from conventional traffic modeling to disaster evacuation where congestion, crowding, panic, and related safety issues are key features. We propose that such events can be simulated using new variants of pedestrian model, which embody ideas about how behavior emerges from the accumulated interactions between small-scale objects. We present a model in which the event space is first explored by agents using ‘swarm intelligence’. Armed with information about the space, agents then move in unobstructed fashion to the event. Congestion and problems over safety are then resolved through introducing controls in an iterative fashion, rerunning the model until a ‘safe solution’ is reached. The model has been developed to simulate the effect of changing the route of the Notting Hill Carnival, an annual event held in west central London over 2 days in August each year. One of the key issues in using such simulation is how the process of modeling interacts with those who manage and control the event. As such, this changes the nature of the modeling problem from one where control and optimization is external to the model to one where it is intrinsic to the simulation.     

An artificial bee colony-based multi-objective route planning algorithm for use in pedestrian navigation at night

Pedestrian navigation at night should differ from daytime navigation due to the psychological safety needs of pedestrians. For example, pedestrians may prefer better-illuminated walking environments, shorter travel distances, and greater numbers of pedestrian companions. Route selection at night is therefore a multi-objective optimization problem. However, multi-objective optimization problems are commonly solved by combining multiple objectives into a single weighted-sum objective function. This study extends the artificial bee colony (ABC) algorithm by modifying several strategies, including the representation of the solutions, the limited neighborhood search, and the Pareto front approximation method. The extended algorithm can be used to generate an optimal route set for pedestrians at night that considers travel distance, the illumination of the walking environment, and the number of pedestrian companions. We compare the proposed algorithm with the well-known Dijkstra shortest-path algorithm and discuss the stability, diversity, and dynamics of the generated solutions. Experiments within a study area confirm the effectiveness of the improved algorithm. This algorithm can also be applied to solving other multi-objective optimization problems.     

Optimal routing for waste collection: a case study in Singapore

ABSTRACT

To collect and dispose growing amounts of municipal solid waste (MSW) changed to be a hot topic along with the rapid urbanization in past decades. Cities are more and more dependent on the incineration instead of landfilling due to the cost-efficiency and environmental concerns. Considering the limited number of incineration plants and complicated situation of transportation in both spatial and temporal dimensions in different cities, the optimal routing for waste collection turns to be meaningful research topic. In this research, the ant colony optimization (ACO)-based multi-objective routing model coupled with min-max model and Dijkstra’s algorithm is proposed to address the question of which route to take from these waste-generating points to the target incineration plant(s) considering travel time, accident probability (black spots), and population exposure, so as to support the routing decision-making. The model is successfully implemented in Singapore and the effectiveness of the model has also been justified. Besides, few limitations of this research have also been discussed, some of which would also be the future directions of our research, especially the design and integration of a web-based routing decision-making support system.     

Integrating Fire Behavior and Pedestrian Mobility Models to Assess Potential Risk to Humans from Wildfires Within the U.S.–Mexico Border Zone∗

Wildfires create a risk to pedestrians traveling through rural areas, because they might not be aware of the presence of a wildfire or its direction and rate of spread until is too late to successfully evacuate. In wildland areas of southern San Diego County, immigrants crossing the U.S.–Mexico border and border security agents are particularly at risk to wildfires. The objective of this study is to develop a framework of analysis and associated tools for examining the combined behavior of wildfires and pedestrian mobility to assess the potential threat of fire to pedestrians in wildland areas. Outputs from a geographic information system (GIS) overlay model for determining potentially dangerous fire zones, the Wildland–Urban Interface Evacuation (WUIVAC) model, and a model of pedestrian mobility in wildland areas were combined to generate wildfire risk to pedestrian maps. The key technical contributions of the study are the development and testing of the pedestrian mobility model and the framework and logic for integrating the results of three GIS-based models. The applied geography contribution is the testing of two scenarios of high risk from wildfires to pedestrians within the U.S.–Mexico border zone of San Diego County, California.

The study results show that the travel times calculated by the pedestrian mobility model appear to be realistic and are affected by the terrain and vegetation characteristics of a study site, whereas the evacuation trigger buffers (ETBs) from WUIVAC are mostly influenced by the wind speed and direction parameters of the FlamMap fire spread model. A moderate fire danger to pedestrians in the most remote wildland locations of the study area is determined. The scenario test results suggest that if a wildfire occurs within 2 km (extreme southwesterly winds) or 6 km (extreme northeasterly wind) of pedestrians in the worst case location within the San Diego border region they would likely not have a sufficient amount of time to reach a nearby safety zone.     

Pedestrian network map generation approaches and recommendation

With the advanced capabilities of mobile devices and the success of car navigation systems, interest in pedestrian navigation systems is on the rise. A critical component of any navigation system is a map database, which represents a network (e.g., road networks for car navigation) and supports key functionality such as map display, geocoding, and routing. Road networks, mainly due to the popularity of car navigation systems, are well defined and publicly available. However, in pedestrian navigation systems, as well as other applications including urban planning and physical activity studies, road networks do not adequately represent the paths that pedestrians usually travel. Currently, there is a void in literatures discussing the challenges, methods, and best practices for pedestrian network map generation. This coupled with the increased demand for pedestrian networks is the prime motivation for development of new approaches and algorithms to automatically generating pedestrian networks. Three approaches, network buffering, using existing road networks, collaborative mapping, using Global Positioning System (GPS) traces collected by volunteers, and image processing, using high-resolution satellite and laser imageries, were implemented and evaluated with a pedestrian network baseline as a ground truth. The results of the experiments indicate that these three approaches, while differing in complexity and outcome, are viable for automatic pedestrian network map generation. The recommendation of a suitable approach for generating pedestrian networks for a given set of sources and requirements is provided.     

基于应急疏散智能体模型模拟的城市避难所空间配置——以上海市静安区为例

城市应急避难所的空间配置一直是灾害防治和城市安全研究领域的热点问题。本文以城市居民尽快地,尽少拥挤地到达满足容纳需求的应急避难所为目标,整合遥感影像数据、高精度人口分布数据、交通路网数据和专家知识等数据,综合运用智能体模型和多准则决策方法,对城市避难所空间配置开展研究。本文设计了三类与应急疏散相关的智能体：政府智能体、避难所智能体和居民智能体,来实现应急疏散的模拟,并根据模拟结果支持应急避难所的空间选址和配置。选址方法上运用了多准则决策方法和权重敏感性分析,在选址高适宜区域内选定避难所的新建方案。以新的避难所空间布局和配置为条件,执行新一轮的应急疏散模拟过程,实现选址的循环优化,从而获得最终的避难所空间配置方案。本文以上海市静安区的应急避难所空间配置分析为案例,生成了该区域应急避难所的详细空间配置方案,该方案能帮助居民在尽少拥挤风险下尽快疏散到附近的避难所。本文提出的方法充实了中国城市避难所选址的相关理论与可操作性的技术基础,为其他地区开展避难所的配置工作提供借鉴与参考。     

Defining agents' behaviour based on urban economic theory to simulate complex urban residential dynamics

In recent years, agent-based models (ABMs) have become a prevalent approach for modelling complex urban systems. As a class of bottom-up method, ABMs are capable of simulating the decision-making as well as the multiple interactions of autonomous agents and between agents and the environment. The definition of agents' behaviour is a vital issue in implementing ABMs to simulate urban dynamics. Urban economic theory has provided effective ways to cope with this problem. This theory argues that the formation of urban spatial structure is an endogenous process resulting from the interactions among individual actors that are spatially distributed. However, this theory is used to explain urban phenomena regardless of spatial heterogeneity in most cases. This study combines GIS, ABM and urban economic models to simulate complex urban residential dynamics. The time-extended model is incorporated into an ABM so as to define agents' behaviour on a solid theoretical basis. A spatial variable is defined to address the neighbourhood effect by considering spatial heterogeneity. The proposed model is first verified by the simulation of three scenarios using hypothetical data: (1) single dominated preference; (2) varying preferences on the basis of income level; and (3) spatially heterogeneous environment. Then the model is implemented by simulating the residential dynamics in Guangzhou, China.     

Microsimulation of large-scale evacuations utilizing metrorail transit

Public transit plays an important role in emergency evacuations, particularly for areas where public transit serves as a major commute mode for commuters. Microsimulation techniques provide great flexibility in assessing different scenarios in emergency situations. Combining GIS-based network analysis with microsimulation techniques, this study developed a framework to simulate emergency evacuations using rail transit. Applying the framework to a hypothetical attack on the Pentagon, this study investigated the performance of the Washington Metropolitan Area Transit Authority’s Metrorail for large-scale evacuations. A network-based analysis was performed to estimate the number of riders in association with each metro line and station. Using VISSIM, a multi-mode micro-simulation software package, this study integrated a pedestrian model with a Metrorail transit model to evaluate the performance of the Metrorail in the hypothetical evacuations. The simulation results suggested that if the potential of Metrorail transit is effectively utilized, it could be very useful resource during a large-scale evacuation. This study demonstrated the great potential and flexibility of microsimulation techniques for evaluating complex evacuation scenarios and strategies. The framework and approach used in this study can be applied to analyze other similar situations and help develop effective evacuation plans.     

Book Reviews

Abstract

This paper summarizes a research project conducted to investigate the strategies developed by middle school students to solve a route-finding problem using Arc View GIS software. Three different types of route-finding strategies were identified. Some students were visual route-finders and used a highly visual strategy; others were logical route finders and used GIS software tools to apply a more analytical and systematic approach. A third group were transitional route-finders who used an approach that showed evidence of one that was shifting from a visual strategy to one that was more logical. The findings suggest that to be most effective, GIS teaching and curriculum development strategies should begin with an assessment of student understanding of spatial relationships and continue with a progression that spirals according to the evolution of student cognitive mapping skills over time.     

西安入境游客目的地空间意象认知序列研究

选取西安入境游客为研究对象,依托324 份调查问卷中所提取和统计的认知地图相关数据,分析了西安城市目的地空间意象特征,并重点探讨了认知地图类型及构成要素在游客停留天数上的动态变化过程,据此提出了入境游客目的地城市空间意象认知过程。结果显示：① 西安入境游客的认知地图共分为四大类型,其中以空间型为主,单体型、序列型和混合型次之。② 从空间意象认知要素上看,标志物出现频率最高,其次为区域、边界、节点和道路。以钟楼为中心结合城墙以及东西南北4 条大街,构成了入境游客西安城市目的地空间意象的基本框架。③ 西安入境游客目的地空间认知过程,在认知地图主导类型上,呈现“空间型+单体型→空间型→序列型（混合型）→单体型”的演变序列,在空间认知主导要素上,则为“标志物→标志物+道路→标志物”的发展序列。这一空间认知过程反映了游客对目的地空间意象的关注重点由个性到结构,再到意义的转换。     

Microsimulation of Hurricane Evacuation Strategies of Galveston Island

This article investigates the effectiveness of simultaneous and staged evacuation strategies for hurricane evacuations of Galveston Island using agent-based microsimulation techniques. In the simultaneous strategy the entire population is informed to evacuate simultaneously, whereas in a staged evacuation strategy, people are informed to evacuate in a sequence. The results suggest that (1) the most efficient staged evacuation strategy can help reduce the evacuation time for Galveston Island by approximately one hour, (2) previous studies might have underestimated the evacuation time of Galveston, and (3) an evacuation under the rapid response assumption does not necessarily lead to an effective evacuation.     

认知地图的空间句法研究

将意识层面的空间意象外化为可被度量及分析的对象,一直是行为学、地理学、旅游学等学科关注的重要课题。本文主要从构建空间意象的结构而非意义入手,对该领域研究中的两种重要方法,即认知地图法和空间句法进行了比较,意图找到两种方法在空间结构性认知方面的相关性。基于句法空间结构的表达模型,分别就长沙太平街历史街区集成度及其认知地图的调研结果进行了比对,通过真实地图句法集成度与579 份认知地图意象要素出现频率等指标的比较,发现集成度指标与意象出现频率等指标存在较好的相关性。在对认知地图样本进行轴线化转换并比照真实地图的轴线索引后,对真实地图句法集成度与579 份认知地图平均句法集成度进行比较,发现认知地图与真实地图的句法分析在结果上仍有较好的相关性,且节点、标志物两要素的出现频次等指标也与所在街道的集成度存在较为明显的对应性。结论说明空间构形对人们结构性意象的形成有着重要的影响力,认知地图的句法分析对揭示人们空间知识的形成机制具有一定的参考价值,两种方法有相互补充的可能性。     

杭州商业街道空间布局形态关键要素对周边风环境的影响

利用基于计算流体力学（CFD）理论的数值模拟软件,在不影响街道连续性面貌的前提下,通过控制变量法分别改变街道贴线率、街道界面密度、街道高宽比3个关键要素的数值来观察街道风环境的变化情况。结论主要有： 街道内的风速大小随着街道贴线率的增大呈抛物线趋势变化,先增大后减小,在街道贴线率接近70%时,城市街道内风速达到最大; 随着界面密度的不断减小,城市街道内各个测点的风速变化的幅度越来越大,这样会让在其中行走的行人感受到强烈的风速变化,舒适度大为下降; 城市街道内的平均风速和街道高宽比成反比例关系。     

基于多智能体的就业与居住空间演化多情景模拟——快速工业化区域研究

就业与居住空间关系是城市规划与管理研究的热点问题。已有研究主要基于传统宏观模型对就业—居住空间结构进行现状分析或对城市理论进行实证研究,在微观尺度的机制探讨与过程模拟方面较为缺乏。本文基于多智能体自下而上的建模思想,提出基于就业市场的人口居住区位选择模型(Labor Market Based Model of Residential Location-LMBMRL)。以典型的快速工业化地区—东莞市主城区为实验区,通过多情景模拟对就业与居住空间的互动关系进行机制探讨与过程分析。模拟结果充分反映了就业选择对人口居住区位决策的影响,定量评估了住房与交通对职住空间均衡性与职住分离的影响规律。当住房成本提高时,城市职住均衡性降低;当交通可达性提高时,城市空间结构可能出现较为显著的职住分离现象。最后通过多情景模拟揭示不同行业劳动人口群体的就业—居住空间特征与组织模式。研究结果有助于深刻理解城市就业—居住空间互动关系及其内部因果,能够为城市规划与管理提供决策参考。     

Simulation of spatial population dynamics based on labor economics and multi-agent systems: a case study on a rapidly developing manufacturing metropolis

Spatial population dynamics affects resource allocation in urban planning. Simulation of population dynamics can provide useful information to urban planning for rapidly developing manufacturing metropolises. In such a metropolis with a concentration of immigrant labor forces, individual employment choices could have a significant effect on their residential decisions. There remains a need for an efficient method, which can simulate spatial population dynamics by considering the interactions between employment and residential choices. This article proposes an agent-based model for simulation of spatial population dynamics by addressing the influence of labor market on individual residential decisions. Labor economics theory is incorporated into a multi-agent system in this model. The long-term equilibrium process of labor market is established to define the interactions between labor supply and labor demand. An agent-based approach is adopted to simulate the economic behaviors and residential decisions of population individuals. The residential decisions of individuals would eventually have consequences on spatial population dynamics. The proposed model has been verified by the spatial dynamics simulation (2007 to 2010) of Dongguan, an emerging and renowned manufacturing metropolis in the Pearl River Delta, China. The results indicate that the simulated population size and spatial distribution of each town in Dongguan are close to those obtained from census data. The proposed model is also applied to predict spatial population dynamics based on two economic planning scenarios in Dongguan from 2010 to2015. The predicted results provide insights into the population dynamics of this fast-growing region.     

Recommending attractive thematic regions by semantic community detection with multi-sourced VGI data

Attractive regions can be detected and recommended by investigating users’ online footprints. However, social media data suffers from short noisy text and lack of a-priori knowledge, impeding the usefulness of traditional semantic modelling methods. Another challenge is the need for an effective strategy for the selection/recommendation of candidate regions. To address these challenges, we propose a comprehensive workflow which combines semantic and location information of social media data to recommend thematic urban regions to users with specific interests. This workflow is novel in: (1) developing a data-driven geographic topic modelling method which utilizes the co-occurrence patterns of self-explanatory semantic information to detect semantic communities; (2) proposing a new recommendation strategy with the consideration of region’s spatial scale. The workflow was implemented using a real-world dataset and evaluation conducted at three different levels: semantic representativeness, topic identification and recommendation desirability. The evaluation showed that the semantic communities detected were internally consistent and externally differentiable and that the recommended regions had a high degree of desirability. The work has demonstrated the effectiveness of self-explanatory semantic information for geographic topic modelling and highlighted the importance of including region spatial scale into the model for an effective region recommending strategy.     

Multi-granularity periodic activity discovery for moving objects

With the development of location-based services, more moving objects can be traced and a great deal of trajectory data can be collected. Periodicity is very commonly used to analyse the habits of moving objects, so finding objects’ periodic patterns can aid in understanding their behaviour. However, objects’ periodic patterns are always unknown previously, and describing their periods with different granularities will create some surprised findings. This article proposes a multi-granularity periodic activity discovery (MPAD) approach for moving objects. First, a multi-granularity model is introduced to describe the spatial and temporal information of an object’s activities. Then, two algorithms, namely, spatial first and temporal first multi-granularity activity discovery algorithms, are provided to transfer objects’ activities into different granularities. Finally, a novel periodic discovery algorithm is described to find the periodicities of objects’ activities. Experiments on both synthetic and real datasets demonstrate both the efficiency and effectiveness of the proposed work and its notably improved running performance compared to the same algorithms. Additionally, the discovered periodic patterns are more practically significant.     

藏、汉对比视角下的城市空间环境认知研究——以中国西藏日喀则市为例

以分布式认知理论为基础,采用质性研究法、数量分析法以及GIS空间分析技术,探讨日喀则市藏族、汉族居民对城市空间环境认知的异同性。研究表明,藏族对城市空间环境的认知得分高于汉族对城市空间环境的认知得分。藏族、汉族对城市空间环境的认知水平可分为高、中、低3类。认知空间呈现圈层结构与混合结构模式,可分为藏汉一致性、藏族独立以及汉族独立3种认知空间类型。影响居民对城市空间环境的认知因素有共性因素和差异性因素。景观走廊质量、宜居性、基础设施两项是藏族居民关注的主要影响因素,汉族居民更关注公共服务设施的服务能力以及对当地文化的融入感。