Assessing the implications of the recent community opening policy on the street centrality in China: A GIS-based method and case study

Recently, the Chinese government raised an urban planning policy which suggests communities open their private roads for public transport and establish street networks with high spatial density in cities. In this context, the purpose of this study is to analyze the potential changes to street network accessibility using GIS techniques and to provide spatial information that may influence the decision making of urban managers. In addition to the existing street network data in the case study city, Shenzhen, intra-community roads are extracted from building footprints in GIS topographic database and used to construct a potential street network with respect to the community opening policy. An automatic GIS-based method is proposed here to analyze the location advantage information in the simulated urban environment, by combining Delaunay triangulation model and graph theory concepts. Specifically, we establish a two-step framework based on the spatial relationships between roads and buildings. Firstly, intra-community roads between neighboring building footprints are generated using a Delaunay triangulation skeleton model, and with the existing inter-community roads they form the simulated network. Secondly, street centrality indices of the current and simulated networks are compared in terms of closeness, betweenness and straightness. Results indicate that after applying the policy the global accessibility in the city would be increased at some places and decreased at others, and places' directness among others would be generally improved. In addition, the skeleton of central routes for through traffic would not change much. The presented method can also be applied to other cities.     

Geographical data handling and GIS in China

Abstract

Abstract Chinese governments have long paid a great deal of attention to the collection of geodata, and existing sources have been further enhanced by technological advances, notably in remote sensing. Progress has also been made with automated cartography. An expert working group has been examining GIS and it is recognized that a system combining both national and regional components is most appropriate for China. Three basic functions are envisaged for such GIS: comprehensive regional analysis, prediction of change and the sharing of data. Such systems must serve the needs of users and, in order to demonstrate potential applications, regional experiments are being undertaken, including examples of urban information systems, river basin information systems and specialized systems (e.g. for tourist and forest resources). In the next few years, the emphasis will be on research and on technological preparation for the adoption of GIS, including the use of an experimental set of geographical grid cells. Chinese scholars are also involved in international collaboration in the study of global problems and propose a cooperative programme for a global geographical information system.     

Mapping glacier velocities on Svalbard using ERS tandem DInSAR data

The results presented show how glacier velocities can be measured and calculated from Earth Resources Satellite (ERS) tandem interferometric synthetic aperture radar (InSAR) data by the use of geographical information systems (GIS) and non-interferometric software. A semi-automatic algorithm using existing and newly implemented modules in ESRI's GIS software Arc has been developed to unwrap existing height differentiated interferometric SAR (DInSAR) data. The algorithm is applied to a data set covering glaciers from the arctic archipelago Svalbard. Estimated DInSAR velocities are decomposed into the glacier surface flow direction using a digital elevation model (DEM). Velocity fields for the glaciers Isachsenfonna, Akademikerbreen and Nordbreen on Svalbard are presented.     

Multimodal accessibility modeling from coarse transportation networks in Africa

Accessibility is a key driving factor for economic development, social welfare, resources management, and land use planning. In many studies, modeling accessibility relies on proxy variables such as estimated travel time to selected destinations. In developing countries, estimating the travel time is hindered by scarce information about the transportation network, making it necessary to take into account off-network travel coupled with considerations of multimodal options available within the existing network. This research proposes such a hybrid approach that computes the travel time to selected destinations by optimizing together a fully modeled multimodal network and off-network travel. The model was applied in a region around Kisangani located in northeastern Democratic Republic of the Congo. Travel times to Kisangani from the hybrid approach were found to be in close agreement with field-based information (R ² = 0.98). The developed approach also proved to better support real-world transportation constraints (such as transfer points between travel modes or barriers) than cost-distance-based travel-time modeling. Demonstration results from the hybrid approach highlight the potential for impact assessment of road construction or rehabilitation, development of secondary towns or markets, and for land use planning in general.     

Planning of recreational trails in protected areas: Application of regression tree analysis and geographic information systems

One of the most important tasks for managers of many natural protected areas is achieving a balance between conservation of nature and recreational opportunity. This paper presents a framework based on geographic information system (GIS) and regression tree analysis of optimized recreational trail location for flexible, user-defined input parameters. The method utilizes: (1) the GIS to create a database containing field data and existing GIS/cartographic materials; (2) regression tree analysis to establish the relationships between indicators of degradation and environmental and use-related factors for existing trails, as well as to predict trail degradation for potential new trials; (3) least-cost path algorithm within a GIS framework to optimize trail route.The framework was applied to the Gorce National Park in the south of Poland. A large sample (>4500) of the field collected data about degradation of the existing trail network was linked with data about soil, geology, geomorphology and relief, and with information about the type and amount of recreational use. Based on the existing relationship, predicted trail degradation was calculated and routes for two examples of trails (hiking and motorized) were designated. The proposed methodology is objective and quantitative, and can also include knowledge of local stakeholders. The framework has the potential to design new trails (or to re-route old ones), characterized by the best possible solution for recreational and conservation functions to coexist, by routing visitors through trails with the lowest possible impact.     

OutDet: an algorithm for extracting the outer surfaces of building information models for integration with geographic information systems

The integration of Building Information Modelling (BIM) and geographic information systems (GIS) is a promising but challenging topic to solve problems in construction industry. However, loading and rendering rich BIM geometric data and large-scale GIS spatial information in a unified system is still technologically challenging. Current efforts mainly simplify the geometry in BIM models, or convert BIM geometric data to a lower level of detail (LOD). By noticing that only exterior features of BIM models are visible from outdoor observation points, culling BIM interior facilities can dramatically reduce the computational burden when visualizing BIM models in GIS. This study explores the outline detection problem and presents the OutDet algorithm, which selects representative observation points, transforms & projects the BIM geometric data into the same coordinate system, and detects the visible facilities. Empirical study results show that OutDet can cull a large portion of unnecessary features when rendering BIM models in GIS. The use of outlines of BIM models is not an alternative but rather a supplementary approach for current solutions. Jointly using LOD and outer surface can help improve the efficiency of integrated BIM-GIS visualization.Because OutDet retains BIM geometry and semantics, it can be applied to more BIM-GIS applications..     

Mapping fine-scale population distributions at the building level by integrating multisource geospatial big data

Fine-scale population distribution data at the building level play an essential role in numerous fields, for example urban planning and disaster prevention. The rapid technological development of remote sensing (RS) and geographical information system (GIS) in recent decades has benefited numerous population distribution mapping studies. However, most of these studies focused on global population and environmental changes; few considered fine-scale population mapping at the local scale, largely because of a lack of reliable data and models. As geospatial big data booms, Internet-collected volunteered geographic information (VGI) can now be used to solve this problem. This article establishes a novel framework to map urban population distributions at the building scale by integrating multisource geospatial big data, which is essential for the fine-scale mapping of population distributions. First, Baidu points-of-interest (POIs) and real-time Tencent user densities (RTUD) are analyzed by using a random forest algorithm to down-scale the street-level population distribution to the grid level. Then, we design an effective iterative building-population gravity model to map population distributions at the building level. Meanwhile, we introduce a densely inhabited index (DII), generated by the proposed gravity model, which can be used to estimate the degree of residential crowding. According to a comparison with official community-level census data and the results of previous population mapping methods, our method exhibits the best accuracy (Pearson R = .8615, RMSE = 663.3250, p < .0001). The produced fine-scale population map can offer a more thorough understanding of inner city population distributions, which can thus help policy makers optimize the allocation of resources.     

Spatial analysis of linguistic data with GIS functions

Abstract

During the 1980s techniques for analysis of geographical patterns have been refined to the point that they may be applied to data from many fields. Quantitative spatial analysis and existing functions available in geographical information systems (GIS) enable computerized implementations of these spatial analysis methods. This paper describes the application of quantitative spatial analysis and GIS functions to analysis of language data, using the extensive files of the Linguistic Atlas of the Middle and South Atlantic States (LAMSAS). A brief review of recent development of using quantitative and statistical methods for analysing linguistic data is also included.     

Discovering environmental data: metadatabases,network information resource tools and the GENIE system

Abstract

The existence of global computer networks in conjunction with various computer based tools offers the GIS community the possibility of identifying existing spatial data in a faster and more complete way. Using such tools could help the GIS community reduce the high costs of data collection. This paper focuses on the discovery of environmental data using metadatabases and network information resource tools and includes comments on some of the limitations of these computer based tools. The article will conclude by describing how the system developed by the GENIE project attempts to overcome some of these limitations.     

A linear tessellation model to identify spatial pattern in urban street networks

Street patterns reflect the distribution characteristics of a street network and affect the urban structure and human behavior. The recognition of street patterns has been a topic of interest for decades. In this study, a linear tessellation model is proposed to identify the spatial patterns in street networks. The street segments are broken into consecutive linear units with equal length. We define five focal operations using neighborhood analysis to extract the geometric and topological characteristics of each linear unit for the purpose of grid-pattern recognition. These are then classified by Support Vector Machine, and the result is optimized based on Gestalt principles. The experimental results demonstrate that our method is effective for mining grid patterns in a street network.     

Visualizing and quantifying the movement of vegetative drought using remote-sensing data and GIS

Remote-sensing-based drought monitoring methods provide fast and useful information for a sustainable management strategy of drought impact over a region. Common pixel-based monitoring methods are limited in the analysis of the dynamics of this impact at regional scale. For instance, these hardly allow us to quantify the movement of drought in space and time and to compare drought with rainfall deficits without losing the variability of these events within a region. This study proposed an object-based approach that allowed us to visualize and quantify the spatio-temporal movement of drought impact on vegetation, called vegetative drought, in a region. The GIS software Dynomap was used to extract and track objects. Measures of distance and angle were used for determining the speed and direction of vegetative drought and rainfall deficit objects, calculated from the National Oceanic and Atmospheric Administration's (NOAA's) normalized difference vegetation index and rainfall estimates data. The methods were applied to the two rainy seasons during the drought year 1999 in East Africa. Results showed that vegetative drought objects moved into the southwestern direction at an average angle of??138.5° during the first season and??144.5° during the second season. The speed of objects varied between 38 km dekad^?1 and 185 km dekad^?1 during the first season and between 33 km dekad^?1 and 144 km dekad^?1 during the second season, reflecting the rate of spread between dekads. Vegetative drought objects close to rainfall deficit objects showed similar trajectories and sometimes regions overlapped. This indicated that the two events are related. We conclude that a spatiotemporal relationship existed between the two types of events and that this could be quantified.     

Classifying natural-language spatial relation terms with random forest algorithm

The exponential growth of natural language text data in social media has contributed a rich data source for geographic information. However, incorporating such data source for GIS analysis faces tremendous challenges as existing GIS data tend to be geometry based while natural language text data tend to rely on natural language spatial relation (NLSR) terms. To alleviate this problem, one critical step is to translate geometric configurations into NLSR terms, but existing methods to date (e.g. mean value or decision tree algorithm) are insufficient to obtain a precise translation. This study addresses this issue by adopting the random forest (RF) algorithm to automatically learn a robust mapping model from a large number of samples and to evaluate the importance of each variable for each NLSR term. Because the semantic similarity of the collected terms reduces the classification accuracy, different grouping schemes of NLSR terms are used, with their influences on classification results being evaluated. The experiment results demonstrate that the learned model can accurately transform geometric configurations into NLSR terms, and that recognizing different groups of terms require different sets of variables. More importantly, the results of variable importance evaluation indicate that the importance of topology types determined by the 9-intersection model is weaker than metric variables in defining NLSR terms, which contrasts to the assertion of ‘topology matters, metric refines’ in existing studies.     

基于GIS和空间句法的历史街区保护更新规划方法研究—以江门市历史街区为例

基于GIS和空间句法集成的思路,以江门市历史街区保护更新规划为例,结合实地调研从空间实体的“点、线、面”3个层面分别建立建筑、街巷和地块GIS数据库,利用GIS多因子评价对建筑保护价值进行综合评价,运用空间句法定性定量的分析手段剖析历史街区的街巷路网结构及用地布局,尝试探索建立在量化、实证分析基础上的历史街区保护更新规划的技术方法和流程.研究结果表明:通过量算空间句法参数可以直观解析历史街区空间形态结构和智能性水平,对研究区的规划定位能提供较好的参考;通过空间句法与GIS集成分析对街巷和用地布局的合理性进     

摄影测量三维重建中多源信息融合方法探讨

分析摄影测量三维重建中不同数据源的特点,针对现有多源数据融合存在的有限数据源低层次融合问题,提出一种基于影像特征、GIS信息和3D TIN模型融合的层次结构与算法流程 .该算法主要包括多边形区域增长、几何推理和建筑物几何模型平差等处理,为基于摄影测 量的三维城市模型自动重建提供了一种新的思路与方法.     

基于网络形态的街道空间环境与“两抢一盗”犯罪的关系研究——以HS市原DP区为例

城市街道网络是承载人们日常行为的空间载体,街道网络及其中人的日常行为共同构成了主要的街道网络空间环境,并影响着犯罪行为的发生。既有研究表明街道空间中多发“两抢一盗”侵财类案件,并且街道网络形态和日常行为主体与犯罪存在联系,但对这一问题的深入探究较少。本文基于HS市原DP区的派出所辖区单元,使用路网数据、人口统计数据、手机信令数据等相关资料,采用空间分析和统计分析方法,对街道网络的几何形态和拓扑形态、动态人口和静态人口等因素与犯罪分布的关系展开分析,尝试探索街道网络空间环境对“两抢一盗”犯罪的影响。结果表明,街道网络空间环境中影响盗窃和两抢犯罪的要素存在较大差异。“X形单元”式街道网络、路网密度、低速交通的路网渗透性、静态人口密度和动态人口密度均对盗窃犯罪产生重要影响,与两抢犯罪相关的仅有“T形单元”式街道网络、静态人口中的实际居住户籍人员和外来人员密度,街道网络形态没有表现出与两抢犯罪的显著相关。最后提出,由于街道网络的通达性和危险性往往并存,如何通过调控或改善整体的街道网络空间环境来尽可能减少犯罪风险,是未来规划建设安全的街道网络空间需要思考的话题。     

Quality assessment of linear data

This paper presents methods to evaluate the geometric quality of spatial data. Firstly, a point‐based method is presented, adapting conventional assessment methods whereby common points between datasets are compared. In our approach, initial matches are established automatically and refined further through interactive editing. Second, a line‐based method which uses correspondences between line segments is proposed. Here, the geometry of line segments in vector is transformed into a set of rasterized values so that their combination at each pixel can restore their original vector geometry. Matching is performed on rasterized line segments and their matching lengths and displacements are measured. Experimental results show that the line‐based approach proposed is efficient to evaluate the geometric quality of spatial data without requirements of topological relationships among line features.     

Improving the throughput of transportation networks with a time-optimization routing strategy

Traffic congestion, a common and complicated phenomenon in urban transportation systems, is attracting increasing attention from researchers in Geographical Information Science (GIS) and other fields. In this study, we illustrate a general mechanism that reveals the relationship between travel time and dynamic traffic conditions. We measure a vehicle’s travel time to its destination along any path, where the travel time is calculated based on the path length and on the real-time traffic volume and transport capacity of each road segment on the path. On the basis of this measurement, we present a simple dynamic routing strategy that allows each vehicle to dynamically choose the path to its destination while imposing the minimum travel time. The application of our routing algorithm to the Chengdu street network, Barabási–Albert scale-free network and Erdös–Rényi random network shows that the proposed strategy remarkably improves network throughput and balances traffic load distribution. Our findings suggest that mining the time mechanism of network transport is important to explore efficient time-optimization routing algorithms to enhance the transport capacity of urban street networks and other kinds of networks.     

Landslide susceptibility evaluation based on BPNN and GIS: a case of Guojiaba in the Three Gorges Reservoir Area

A landslide susceptibility evaluation is vital for disaster management and development planning in the Yangtze River Three Gorges Reservoir Area. In this study, with the support of remote sensing and Geographic Information System, 4 factor groups comprising 10 separate subfactors of landslide-related data layers were selected to establish a susceptibility evaluation model based on the back-propagation neural network including slope, aspect, plan curvature, strata and lithology, distance to faults, land use/land cover, Normalized Difference Vegetation Index, Normalized Difference Water Index, distance from roads, and effect of rivers. During model development, a three-layered interconnected neural network structure of 10 (input layer) × 20 (hidden layer) × 1 (output layer) was used for evaluating the landslide susceptibility in Guojiaba. At the same time, a back-propagation algorithm was applied to calculate the weights between the input layer and the hidden layer and between the hidden layer and the output layer. The results showed that the effect of slope has the highest weight value (0.2051), which is more than two times that of the other factors, followed by strata and lithology (0.1213) and then the effect of rivers (0.1201). At the end of the susceptibility evaluation, the area was divided into four zones such as very high, high, moderate and low susceptibility. For verification, the receiver operating characteristic curve for the back-propagation neural network-derived landslide susceptibility evaluation model was drawn, and the results showed that the area under the receiver operating characteristic curve was 0.8790 and the prediction accuracy was 88%. Furthermore, the results obtained from this article were then verified by comparing with the existing landslide historical data and multiple field-verified results. Lastly, the landslide susceptibility map will help decision makers in risk management, site selection, site planning, and the design of control engineering.     

A spatio-temporal method for crime prediction using historical crime data and transitional zones identified from nightlight imagery

ABSTRACT

Accurate crime prediction can help allocate police resources for crime reduction and prevention. There are two popular approaches to predict criminal activities: one is based on historical crime, and the other is based on environmental variables correlated with criminal patterns. Previous research on geo-statistical modeling mainly considered one type of data in space-time domain, and few sought to blend multi-source data. In this research, we proposed a spatio-temporal Cokriging algorithm to integrate historical crime data and urban transitional zones for more accurate crime prediction. Time-series historical crime data were used as the primary variable, while urban transitional zones identified from the VIIRS nightlight imagery were used as the secondary co-variable. The algorithm has been applied to predict weekly-based street crime and hotspots in Cincinnati, Ohio. Statistical tests and Predictive Accuracy Index (PAI) and Predictive Efficiency Index (PEI) tests were used to validate predictions in comparison with those of the control group without using the co-variable. The validation results demonstrate that the proposed algorithm with historical crime data and urban transitional zones increased the correlation coefficient by 5.4% for weekdays and by 12.3% for weekends in statistical tests, and gained higher hit rates measured by PAI/PEI in the hotspots test.     

Using multiple data sources to enhance photogrammetry for mapping antarctic terrain

Extensive aerial photography cover is available for parts of the British Antarctic Territory, but the fisoffinfillijl characteristics of the photography, combined with the sparsity of ground control information and rugged snow-covered terrain, make photogrammetric mapping techniques difficult to apply. This paper shows, by reference to a new 1:50,000 scale topographic map of part of the Antarctic Peninsula, how merging topographic data from various sources in a GIS environment can make photogrammetric mapping more effective. Information sources used in the map compilation include three types of aerial photography, geo-referenced satellite imagery, surveyed points in a control network and satellite image-derived control points. A shape-from-shading algorithm was used to generate contours for snowfields where absence of surface detail prevented photogrammetric contouring. A horizontal and vertical accuracy of better than ±5 m was achieved in orientation of photography covering almost all of the map area. Such errors have allowed the construction of an accurate large-scale map for an area where previous mapping had been restricted to medium and small scales.