Applying VGI to collaborative research in the humanities: the case of ARTL@S

ABSTRACT

Using Artl@s as an example of a project that relies on volunteered geographic information (VGI), this article examines the specific challenges that exist, beyond those frequently discussed in general VGI systems (e.g., participants’ motivation and data quality control) in regard to sharing research data in humanities: (1) most data from the humanities is qualitative and collected from multiple data sources which are often inconsistent and unmappable; (2) data is usually interconnected with multiple relationships among different tables which creates challenges for both mapping and query functionality; (3) data is both geographical and historical. Consequently addresses that no longer exist have to be geolocated and visualized on historical basemaps and spaces must be represented diachronically; (4) the design of web map application needs to balance both sophisticated research requirements and a user-friendly interface; (5) finally contributors expect their data to be cited or acknowledged when used in other studies and users need metadata and citation information in order to reuse and repurpose datasets.

In this article, we discuss how Artl@s, a project which developed a georeferenced historical database of exhibition catalogues, addresses these challenges. Artl@s provides a case study for VGI adoption by digital humanities scholars for research data sharing, as it offers features, such as flexible batch data contribution, interrelated spatial query, automatic geolocalization of historical addresses, and data citation mechanisms.     

Modeling the environmental susceptibility of landfill sites in California

??The proper management of solid waste (SW) is a global environmental challenge. A major issue is the proper disposal of SW while balancing a wide range of criteria and working with different spatial data. In this study, we used geographic information system as a tool to perform multi-criteria decision analysis with an analytical hierarchy process to develop an environmental impact susceptibility model (EISM) for landfills. The model was applied to the state of California, USA and results are presented herein. In particular, the EISM considers factors such as geology, pedology, geomorphology, water resources, and climate as represented by 13 associated environmental indicators. The results of the EISM indicate that more than 75% of California’s territory is situated in areas with very low, low, and medium environmental impact susceptibility categories. However, in the remaining 25% of the state’s land, 61 landfills are located in the high and very high categories. These results are alarming because during the period from 2000 to 2015, these 61 landfills received approximately 308 million tons of SW, which corresponds to more than 57% of all SW disposed in California. The model results can be used toward mitigating the environmental impacts of these facilities.     

Automatic selection of weights for GIS-based multicriteria decision analysis: site selection of transmission towers as a case study

Transmission line (TL) siting consists of finding suitable land to build transmission towers. This is just one of the numerous complex geographical problems often solved using GIS-based multicriteria decision analysis (MCDA), which is a set of techniques that weight several geographical features to identify suitable locations. This technique is mostly employed using expert knowledge to identify the correct set of weights; thus adding a certain amount of subjectivity to the analysis, meaning that for the same problem if we change the experts involved, we may reach different results.This research is a first attempt to try and solve this issue. We employed a statistical analysis to quantitatively calculate these weights and we tested our method on a case study about transmission line siting in Switzerland. We compared the distances between each sample in our dataset, in this case study these are location of transmission towers, with each geographical feature, e.g. distance from water features. Then we calculate the same distances but for random points, sampled throughout the study area. The reasoning behind this method is that if samples present a distance from a geographic feature statistically different from the random, it means that the feature played an important role in dictating the location of the sample. In this case for instance, high-voltage transmission towers are purposely built as far away as possible from urban areas. Random points are on the contrary by definition sampled without any constraint. Therefore, when comparing the two datasets, we should find that transmission towers have a larger average distance from urban areas than random points. This allows us to determine that this criterion (i.e. distance from urban centers) is important for planning new TL.The results indicate that this method can successfully weight and rank the most important criteria to be considered for an MCDA analysis, in line with weights proposed in the literature. The advantage of the proposed technique is that it completely excludes human factors, thus potentially increasing the social acceptance of the MCDA results.     

The Beach Crowding Index: A Tool for Assessing Social Carrying Capacity of Vulnerable Beaches

The aim of this article is to introduce the Beach Crowding Index (BCI), a procedure to assess the social carrying capacity of vulnerable beaches. The study uses the people at one time (PAOT) approach and data gathered weekly throughout the bathing season regarding the number of beachgoers in 100 m² cells of the beach to assess how many beachgoers it can comfortably hold. The procedure is based on fieldwork, interviews with beachgoers, and geographic information system (GIS) analysis and has been tested on four beaches in protected areas on the Spanish Mediterranean coast. On a scale from 0 to 4, minimum scores throughout the bathing season are 0.7 and maximum 3.7, although results showed wide variation between the beaches, the section of the beach, and the time of day. This study suggests that determining the location of beachgoers and collecting a long-term series of data is fundamental to assessing social carrying capacity and that the BCI procedure can be used for a large number of applications.     

Using Web Maps to Analyze the Construction of Global Scale Cognitive Maps

Game-based Web sites and applications are changing the ways in which students learn the world map. In this study, a Web map-based digital learning tool was used as a study aid for a university-level geography course in order to examine the way in which global scale cognitive maps are constructed. A network analysis revealed that clicks were negatively correlated with familiarity, but were positively correlated with area. More significantly, well-known countries did not act as central nodes within network-based mental models, but served a more complex mediating role in structuring the space between subregions.     

地理信息技术支撑下的南海岛礁资源环境研究进展与展望

面积辽阔的南海是中国未来重要的能源接续区与资源基地,也是涉及国家海洋权益最为集中的区域。本文针对地理信息技术支撑下的南海岛礁研究现状,从南海岛礁现有基础数据成果、遥感手段岛礁基础地理信息提取方法及提取成果所做的岛礁分析与评价3方面,系统地回顾了国内外涉及南海岛礁资源环境研究的相关进展,评述了现有研究技术方法存在的不足,并对未来的研究进行了展望,在此基础上,提出了3个主要方向作为未来研究的重点：①促进遥感数据获取多源化,构建海量多源、多尺度南海岛礁遥感数据仓库;②加强技术协同创新,结合地理信息技术的发展,提升岛礁信息提取与监测的准确性与可靠性;③提升遥感分析智能化,研究并建立综合分析与情势推演平台及战略决策辅助支撑系统,以期为南海岛礁的进一步深入研究提供参考。     

Big Data Resource Planning for Food Safety: a Preliminary Exploration of the “Environment,Food and Health” Information Chain

Environmental pollution, food safety and health are closely linked. A key challenge in addressing the problem of food safety and protecting public health is building an integrated knowledge base to inform policy and strengthen governance. This requires breaking down the trans-departmental information barrier across the environment, food and health domains to ensure the effective flow of data and the efficient utilization of resources, and facilitate the collaborative governance of food safety. Achieving this will be crucial for the development of health and medical care in China in the era of big data. Currently, the information resources commanded by various departments are incomplete and fragmented. Data resources are also organized in vertical silos and there is a lack of data sharing within and across policy streams. To provide the basis for more effective integrated collection and analysis of data in future, this study summarizes the information resources of various departments whose work relates to interactions between environment, food and health, and presents measures to strengthen top-down design, and establish unified data standards and a big data sharing platform. It also points to the need for increased training of data analysts with interdisciplinary expertise.     

基于卫星资料梯度信息的新型变分同化方法对于台风数值模拟的研究

卫星资料凭着卫星遥感的全球性、连续性和高频次观测等优势,成为一种重要的非常规资料源,但卫星观测仍然存在各种各样的观测误差,其中包含由于观测偶然性所造成的统计学上的随机误差及仪器本身和辐射传输模式等造成的系统性偏差,这些误差在很大程度上影响了卫星资料的质量。文中提出了一种能有效订正卫星观测资料系统性偏差的梯度信息同化算法,该方法用一个梯度算子进行模式变量与观测变量的梯度变换,从而达到订正系统性偏差的目的。本文利用WRF（Weather Research Forecast）模式及其同化模式WRFDA（WRF Data Assimilation system）,以及AIRS（Atmospheric Infrared Sounder）资料,对台风“圆规”进行了实际的数值模拟和同化试验,数值结果表明,梯度信息同化方法能明显改善台风路径的模拟,在处理可信度较低的资料时仍然适用。另外,通过同化诊断分析,发现卫星资料的系统性偏差对于台风数值模拟有较大影响,而文中提出的梯度信息同化方法能较好的解决此类问题。     

The geometry of space-time prisms with uncertain anchors

Space-time prisms envelop all spatio-temporal locations that moving objects may have visited between two of their known spatio-temporal locations, given a bound on their travel speed. In this context, the known locations are often the result of observations or measurements, and they are called ‘anchor points’. The classic space-time prism, in isotropic two-dimensional space, as well as in transportation networks, assumes that the measurements of these anchor points are exact. Whereas, in many applications, we can assume that time can be measured fairly precisely, this assumption is unrealistic for the spatial components of measured locations (we think of Global Positioning System (GPS) errors, for instance). In this paper, we extend the classical prism from anchor points to circular ‘anchor regions’ that capture the uncertainty or error on their measurement. We define the notion of a space-time prism with uncertain anchor points, called uncertain prism, for short. We study the geometry of uncertain prisms in an arbitrary metric space to make this concept as widely applicable as possible. We also focus on the rims of uncertain space-time prisms, which demarcate the area that a moving object can have visited between two anchor regions (given some local speed limitations).     

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.