A new algorithm for distance cartogram construction

A distance cartogram is a diagram that visualizes the proximity indices between points in a network, such as time–distances between cities. The Euclidean distances between the points on the distance cartogram represent the given proximity indices. This is a useful visualization tool for the level of service of transport, e.g. difference in the level of service between regions or points in a network and its improvement in the course of time. The two previously proposed methods—multidimensional scaling (MDS) and network time–space mapping—have certain advantages and disadvantages. However, we observe that these methods are essentially the same, and the merits of both these methods can be combined to formulate a generalized solution. In this study, we first formulate the time–space mapping problem, which includes the key features of both of the above stated methods, and propose a generalized solution. We then apply this solution to the time–distances of Japan's railway networks to confirm its applicability.     

Exploring space–time paths in physical and social closeness spaces: a space–time GIS approach

Exploring the evolution of people’s social interactions along with their changing physical locations can help to achieve a better understanding of the processes that generate the relationships between physical distance and social interactions, which can benefit broad fields of study related to social networks. However, few studies have examined the evolving relationships between physical movements and social closeness evolution. This is partially related to the shortage of longitudinal data in both physical locations and social interactions and the lack of an exploratory analysis environment capable of effectively investigating such a process over space and time. With the increasing availability of sociospatiotemporal data in recent years, it is now feasible to examine the relationships between physical separation and social interactions at the individual level in a space–time context. This research was intended to offer a spatiotemporal exploratory analysis approach to address this challenge. The first step was to propose the concept of a social closeness space–time path, which is an extension of the space–time path concept in time geography, to represent evolving human relationships in a social closeness space. A space–time geographical information system (GIS) prototype was then designed to support the representation and analysis of space–time paths in both physical and social closeness spaces. Finally, the effectiveness of the proposed concept and design in gaining insight into the impact of physical migration on online social closeness was demonstrated through an empirical study. The contributions of this study include an extension of the time–geographic framework from physical space to social closeness space, the development of a multirepresentation approach in a GIS to integrate an individual’s space–time paths in both physical and social closeness spaces, and an exploratory analysis of the evolving relationships between physical separation and social closeness over time.     

Spatio-temporal analysis of tree height in a young cork oak plantation

Cork oak is one of the most valuable natural forest genera in the Mediterranean basin. Modelling cork oak growth has been a challenge for foresters in recent years because of strong site and genetic influences, below-ground competition, management regimes and age effects. Because cork productivity is related to forest height, which is, in turn, related directly with site characteristics, an increase in the accuracy of height prediction implies improved productivity estimation. A Bayesian maximum entropy (BME) geostatistical model was applied to characterize the space–time pattern of height of young cork oak in a forest stand from central Sardinia in the years 2000, 2002, 2003, 2006 and 2008. Cork oak height maps were produced for each of the 5 years. The main goals were to analyse and interpret through time (i) the changes in spatial correlation and (ii) the changes in spatial distribution of cork oak height. The plantation was characterized by an increasing spatial dependence through time, whereas the temporal range was 2 years. Cork oak height was significantly correlated with wind speed (reduced by a neighbouring forest) in all the years implying a single trend. The correlations were larger for 2006 and 2008 than for previous years. Three other environmental variables (shade, elevation and slope) were less significant and their influence restricted to 2 years only. This research has several implications for the management of cork oak in the young phase.     

A heuristic cellular automata approach for modelling urban land-use change based on simulated annealing

This article presents a novel cellular automata (CA) approach to simulate the spatio-temporal process of urban land-use change based on the simulated annealing (SA) algorithm. The SA algorithm enables dynamic optimisation of the CA's transition rules that would otherwise be difficult to configure using conventional mathematical methods. In this heuristic approach, an objective function is constructed based on a theoretical accumulative disagreement between the simulated land-use pattern and the actual land-use pattern derived from remotely sensed imagery. The function value that measures the mismatch between the actual and the simulated land-use patterns would be minimised randomly through the SA process. Hence, a set of attribution parameters that can be used in the CA model is achieved. An SA optimisation tool was developed using Matlab and incorporated into the cellular simulation in GIS to form an integrated SACA model. An application of the SACA model to simulate the spatio-temporal process of land-use change in Jinshan District of Shanghai Municipality, PR China, from 1992 to 2008 shows that this modelling approach is efficient and robust and can be used to reconstruct historical urban land-use patterns to assist with urban planning policy-making and actions. Comparison of the SACA model with a typical CA model based on a logistic regression method without the SA optimisation (also known as LogCA) shows that the SACA model generates better simulation results than the LogCA model, and the improvement of the SACA over the LogCA model is largely attributed to higher locational accuracy, a feature desirable in most spatially explicit simulations of geographical processes.     

A new discovery of transition rules for cellular automata by using cuckoo search algorithm

This paper presents an intelligent approach to discover transition rules for cellular automata (CA) by using cuckoo search (CS) algorithm. CS algorithm is a novel evolutionary search algorithm for solving optimization problems by simulating breeding behavior of parasitic cuckoos. Each cuckoo searches the best upper and lower thresholds for each attribute as a zone. When the zones of all attributes are connected by the operator ‘And’ and linked with a cell status value, one CS-based transition rule is formed by using the explicit expression of ‘if-then’. With two distinct advantages of efficient random walk of Lévy flights and balanced mixing, CS algorithm performs well in both local search and guaranteed global convergence. Furthermore, the CA model with transition rules derived by CS algorithm (CS-CA) has been applied to simulate the urban expansion of Nanjing City, China. The simulation produces encouraging results, in terms of numeric accuracy and spatial distribution, in agreement with the actual patterns. Preliminary results suggest that this CS approach is well suitable for discovering reliable transition rules. The model validation and comparison show that the CS-CA model gets a higher accuracy than NULL, BCO-CA, PSO-CA, and ACO-CA models. Simulation results demonstrate the feasibility and practicability of applying CS algorithm to discover transition rules of CA for simulating geographical systems.     

Preliminary investigation of seismic damage to two steel space structures during the 2013 Lushan earthquake

Severe damage to steel space structures is rarely reported when compared to other structural systems damaged during past major earthquakes around the world.Two gymnasiums of steel space structures in downtown Lushan County that were damaged during the 2013 M7.0 Lushan earthquake in China were investigated and the observations are summarized in this paper.Typical damage to these two steel space structures ranges from moderate to severe.Moderate damage includes global buckling and dislocation of bolted connections of truss members,and inelastic elongation of anchor bolts and sliding of pedestal plates of supports.Severe damage includes member fracture caused by local buckling,and fracture failure of anchor bolts and welds.The distribution of structural damage to these two structures is described in detail and future research opportunities are suggested.     

“数字重庆”地理空间信息定位基准体系的建设

介绍了"数字重庆"地理空间信息定位基准建设的基本情况,着重阐述了CQGNSS、高精度GPS控制网、精密水准网和区域似大地水准面精化等方面的数据处理情况,展示了山城地区的基准体系建设特点。     

数字增城地理信息公共服务平台的建设与研究

增城市地理信息公共服务平台的建设是利用Webservice、历史回溯等技术以满足各个部门对于基础地理信息的需求,实现地理信息跨部门共享、历史回溯和多元空间无缝集成,并最终达到建立在线服务系统,完成水利局等客户端试点、绿色通道3维景观演示,进一步提高服务端数据管理水平的目标,使得基础地理信息真正能为增城市各部门的建设提供良好的发展平台。     

基于双重逆极限空间的地貌信息数据挖掘方法

为了挖掘不完备的地貌数据信息,将地貌信息描述为拓扑空间的形式,从逆极限定义出发,利用映射诱导法建立与原地貌信息空间拓扑同构的双重逆极限空间作为目标信息空间,然后利用拓扑传递性分析原地貌信息空间与目标信息空间的拓扑相似度,实现不完备的地貌信息空间数据挖掘.本文利用GIS软件,结合地貌的坡度、坡向等地貌特征因子分别模拟了地貌信息数据挖掘前后的模型并进行了对比,结果表明此方法是可行的.