A citizen data-based approach to predictive mapping of spatial variation of natural phenomena

The vast accumulation of environmental data and the rapid development of geospatial visualization and analytical techniques make it possible for scientists to solicit information from local citizens to map spatial variation of geographic phenomena. However, data provided by citizens (referred to as citizen data in this article) suffer two limitations for mapping: bias in spatial coverage and imprecision in spatial location. This article presents an approach to minimizing the impacts of these two limitations of citizen data using geospatial analysis techniques. The approach reduces location imprecision by adopting a frequency-sampling strategy to identify representative presence locations from areas over which citizens observed the geographic phenomenon. The approach compensates for the spatial bias by weighting presence locations with cumulative visibility (the frequency at which a given location can be seen by local citizens). As a case study to demonstrate the principle, this approach was applied to map the habitat suitability of the black-and-white snub-nosed monkey (Rhinopithecus bieti) in Yunnan, China. Sightings of R. bieti were elicited from local citizens using a geovisualization platform and then processed with the proposed approach to predict a habitat suitability map. Presence locations of R. bieti recorded by biologists through intensive field tracking were used to validate the predicted habitat suitability map. Validation showed that the continuous Boyce index (B_cont(0.1)) calculated on the suitability map was 0.873 (95% CI: [0.810, 0.917]), indicating that the map was highly consistent with the field-observed distribution of R. bieti. B_cont(0.1) was much lower (0.173) for the suitability map predicted based on citizen data when location imprecision was not reduced and even lower (?0.048) when there was no compensation for spatial bias. This indicates that the proposed approach effectively minimized the impacts of location imprecision and spatial bias in citizen data and therefore effectively improved the quality of mapped spatial variation using citizen data. It further implies that, with the application of geospatial analysis techniques to properly account for limitations in citizen data, valuable information embedded in such data can be extracted and used for scientific mapping.     

整合已有土壤样点的数字土壤制图补样方案

我国多数地区经过两次土壤普查以及科研工作者的野外调查, 积累了一定数量的土壤样点“( 已有样点”)。本文在充分整合已有样点的基础上, 提出了逐次、高效地设计补充样点的方案, 它包括3 个步骤:首先, 确定已有样点集的空间代表范围“( 可推测范围”);然后, 将已有样点集不能代表范围内的每一个栅格都看作一个候选样点, 计算每一个候选样点的可扩推范围, 选择可扩推范围最大的点作为第一个补充样点;最后, 基于已有样点和补充样点更新样点集的空间代表范围图。重复以上过程, 直至新样点集的空间代表范围能够覆盖整个研究区。该方法在充分利用已有样点资源的基础上, 不仅能够确定补充样点的数量、位置, 而且能够给定补充样点的重要性次序, 在采样资源有限的情况下, 为采样者合理地选择样点提供了重要依据。     

Assessing the application and value of participatory mapping for community bushfire preparation

The increased ease for individuals to create, share and map geographic information combined with the need for timely, relevant and diverse information has resulted in a new disaster management context. Volunteered geographic information (VGI), or geographic information voluntarily created by private citizens enabled through technologies like social media and web-based mapping, has changed the ways people create and use information for crisis events. Research has focussed on disaster response while largely ignoring prevention and preparedness. Preparing for disasters can reduce negative impacts on life and property, but despite strategies to educate communities, preparation remains low. This study assesses the application and value of VGI in bushfire risk reduction through a participatory mapping approach. It examines VGI as a social practice and not simply a data source by considering the user experience of contributing VGI and the potential for these activities to increase community connectedness for building disaster resilience. Participatory mapping workshops were held in bushfire-risk communities in Tasmania. Workshop activities included a paper-mapping exercise and web-based digital mapping. Survey results from 31 participants at three workshops indicated the process of mapping and contributing local information for bushfire preparation with other community members can contribute to increased social connectedness, understanding of local bushfire risk, and engagement in risk reduction. Local knowledge exchange was seen as valuable, but the social dimension appeared even more engaging than the specific information shared. Participants reported collaborative maps as effective for collating and sharing community bushfire information with a preference for digital mapping. Some limitations of online sharing of information were also reported by participants, however, including potential issues of privacy, data quality and source trustworthiness. Further work is needed to extrapolate findings from the study sample to the broader population.     

Utilising volunteered geographic information to assess resident's flood evacuation shelters. Case study: Jakarta

Research on disaster response frequently uses volunteered geographic information (VGI), due to its capability to provide near real-time information during and after a disaster. It is much less commonly used in spatial planning related to disaster management. However, VGI appears to have considerable potential for use in spatial planning and offers some advantages over traditional methods. For example, VGI can capture residents' preferences in a much faster, more timely, and more comprehensive fashion than is possible with, for example, questionnaires and surveys. This research investigates the usefulness of VGI for planning flood evacuation shelters. Using Jakarta, Indonesia, as a case study, we use VGI to capture the locations of flood evacuation shelters based on residents' preferences during flood periods in 2013–2014 and 2014–2015 and compare these with the locations of official shelters. Floods frequently affect Jakarta and the city administration uses VGI in flood emergency responses. Moreover, Jakarta has been identified as having the largest number of active Twitter users among cities worldwide. Thus, Jakarta is an appropriate place to study the use of VGI for planning evacuation shelters. VGI generated by Twitter users was used to identify the shelter locations preferred by Jakarta residents, and more precisely the flood evacuees. Of 171,046 tweets using keywords relating to flood evacuation, the content of 306 tweets indicated that they had been sent from inside or near evacuation shelters. The spatial pattern showed that those tweets were sent from 215 locations, mostly located near flooded areas. The analysis further showed that 35.6% of these shelter locations preferred by residents intersected with the locations of official evacuation shelters. As a general conclusion, our study demonstrates the advantages of using VGI for spatial planning, which mainly relates to the ease of capturing community preferences over a large area.     

一种基于地理环境相似度的滑坡负样本可信度度量方法

滑坡负样本在统计型滑坡危险度制图中具有重要作用,能抑制统计模型对滑坡危险度的高估。当前滑坡负样本采样方法采集的负样本可信度未知,在负样本采样过程中,极有可能将那些潜在滑坡点错选为负样本,这些假的负样本会降低负样本集的质量和训练样本集的质量,进而影响统计模型的精度。本文基于“地理环境越相似、地理特征越相似”的地理学常识,认为与正样本有着相似地理环境的点极有可能是未来发生滑坡的点;与正样本的地理环境越不相似的点,则越有可能是负样本。基于此假设提出一种基于地理环境相似度的负样本可信度度量方法,将该方法应用于滑坡灾害频发的陇南山区油房沟流域,对油房沟进行滑坡负样本可信度评价制图;使用油房沟流域的滑坡发生初始面来验证该方法的有效性。结果发现：滑坡发生初始面上所有栅格点的负样本可信度平均值为0.26,超过95%的栅格点的负样本可信度都小于0.5,说明本文提出的负样本可信度度量方法合理。     

Geometric-based approach for integrating VGI POIs and road networks

Integrating heterogeneous spatial data is a crucial problem for geographical information systems (GIS) applications. Previous studies mainly focus on the matching of heterogeneous road networks or heterogeneous polygonal data sets. Few literatures attempt to approach the problem of integrating the point of interest (POI) from volunteered geographic information (VGI) and professional road networks from official mapping agencies. Hence, the article proposes an approach for integrating VGI POIs and professional road networks. The proposed method first generates a POI connectivity graph by mining the linear cluster patterns from POIs. Secondly, the matching nodes between the POI connectivity graph and the associated road network are fulfilled by probabilistic relaxation and refined by a vector median filtering (VMF). Finally, POIs are aligned to the road network by an affine transformation according to the matching nodes. Experiments demonstrate that the proposed method integrates both the POIs from VGI and the POIs from official mapping agencies with the associated road networks effectively and validly, providing a promising solution for enriching professional road networks by integrating VGI POIs.     

Quality assessment of building footprint data using a deep autoencoder network

Volunteered geographic information (VGI), OpenStreetMap (OSM), has been used in many applications, especially when official spatial data are unavailable or outdated. However, the quality of VGI remains a valid concern. In this paper, we use the matched results between OSM building footprints and official data as the samples for training an autoencoder network, which encodes and reconstructs the sample populations according to unknown complex multivariate probability distributions. Then, the OSM data are assessed based on the theory that small probability samples contribute little to the autoencoder network and that they can be recognized by the higher reconstructed errors during training. In the method described here, the selected measures, including data completeness, positional accuracy, shape accuracy, semantic accuracy and orientation consistency between OSM and official data, are used as the inputs for a deep autoencoder network. Finally, building footprint data from Toronto, Canada, are evaluated, and experiments show that the proposed method can assess the OSM data comprehensively, objectively and accurately.     

Quantification of spatial gradation of slope positions

Transition between slope positions (e.g., ridge, shoulder slope, back slope, foot slope, and valley) is often gradual. Quantification of spatial transitions or spatial gradations between slope positions can increase the accuracy of terrain parameterization for geographical or ecological modeling, especially for digital soil mapping at a fine scale. Current models for characterizing the spatial gradation of slope positions based on a gridded DEM either focus solely on the parameter space or depend on too many rules defined by topographic attributes, which makes such approaches impractical. The typical locations of a slope position contain the characteristics of the slope position in both parameter space and spatial context. Thus, the spatial gradation of slope positions can be quantified by comparing terrain characteristics (spatial and parametrical) of given locations to those at typical locations. Based on this idea, this paper proposes an approach to quantifying the spatial gradation of slope positions by using typical locations as prototypes. This approach includes two parts: the first is to extract the typical locations of each slope position and treat them as the prototypes of this position; and the second is to compute the similarity between a given location and the prototypes based on both local topographic attributes and spatial context. The new approach characterizes slope position gradation in both the attribute domain (i.e., parameter space) and the spatial domain (i.e., geographic space) in an easy and practicable way. Applications show that the new approach can quantitatively describe spatial gradations among a set of slope positions. Comparison of spatial gradation of A-horizon sand percentages with the quantified spatial gradation of slope positions indicates that the latter reflects slope processes, confirming the effectiveness of the approach. The comparison of a soil subgroup map of the study area with the maximum similarity map derived from the approach also suggests that the quantified spatial gradation of slope position can be used to aid geographical modeling such as digital soil mapping.     

Analysis of positional uncertainty of road networks in volunteered geographic information with a statistically defined buffer-zone method

Volunteered geographic information (VGI) is crowdsourced information that can enrich and enhance research and applications based on geo-referenced data. However, the quality of VGI is of great concern, and positional accuracy is a fundamental basis for the VGI quality assurance. A buffer-zone method can be used for its assessment, but the buffer radius in this technique is subjectively specified; as result, different selections of the buffer radius lead to different positional accuracies. To solve this problem, a statistically defined buffer zone for the positional accuracy assessment in VGI is proposed in this study. To facilitate practical applications, we have also developed an iterative method to obtain a theoretically defined buffer zone. In addition to the positional accuracy assessment, we have derived a measure of positional quality, which comprises the assessment of positional accuracy and the level of confidence in such assessment determined with respect to a statistically defined buffer zone. To illustrate and substantiate the theoretical arguments, both numerical simulations and real-life experiments are performed using OpenStreetMap. The experimental results confirm the high significance of the proposed statistical approach to the buffer zone-based assessment of the positional uncertainty in VGI.     

A review of volunteered geographic information quality assessment methods

With the ubiquity of advanced web technologies and location-sensing hand held devices, citizens regardless of their knowledge or expertise, are able to produce spatial information. This phenomenon is known as volunteered geographic information (VGI). During the past decade VGI has been used as a data source supporting a wide range of services, such as environmental monitoring, events reporting, human movement analysis, disaster management, etc. However, these volunteer-contributed data also come with varying quality. Reasons for this are: data is produced by heterogeneous contributors, using various technologies and tools, having different level of details and precision, serving heterogeneous purposes, and a lack of gatekeepers. Crowd-sourcing, social, and geographic approaches have been proposed and later followed to develop appropriate methods to assess the quality measures and indicators of VGI. In this article, we review various quality measures and indicators for selected types of VGI and existing quality assessment methods. As an outcome, the article presents a classification of VGI with current methods utilized to assess the quality of selected types of VGI. Through these findings, we introduce data mining as an additional approach for quality handling in VGI.     

Fine-resolution population mapping using OpenStreetMap points-of-interest

Data on population at building level is required for various purposes. However, to protect privacy, government population data is aggregated. Population estimates at finer scales can be obtained through areal interpolation, a process where data from a first spatial unit system is transferred to another system. Areal interpolation can be conducted with ancillary data that guide the redistribution of population. For population estimation at the building level, common ancillary data include three-dimensional data on buildings, obtained through costly processes such as LiDAR. Meanwhile, volunteered geographic information (VGI) is emerging as a new category of data and is already used for purposes related to urban management. The objective of this paper is to present an alternative approach for building level areal interpolation that uses VGI as ancillary data. The proposed method integrates existing interpolation techniques, i.e., multi-class dasymetric mapping and interpolation by surface volume integration; data on building footprints and points-of-interest (POIs) extracted from OpenStreetMap (OSM) are used to refine population estimates at building level. A case study was conducted for the city of Hamburg and the results were compared using different types of POIs. The results suggest that VGI can be used to accurately estimate population distribution, but that further research is needed to understand how POIs can reveal population distribution patterns.     

Computing the semantic similarity of geographic terms using volunteered lexical definitions

Volunteered geographic information (VGI) is generated by heterogenous ‘information communities’ that co-operate to produce reusable units of geographic knowledge. A consensual lexicon is a key factor to enable this open production model. Lexical definitions help demarcate the boundaries of terms, forming a thin semantic ground on which knowledge can travel. In VGI, lexical definitions often appear to be inconsistent, circular, noisy and highly idiosyncratic. Computing the semantic similarity of these ‘volunteered lexical definitions’ has a wide range of applications in GIScience, including information retrieval, data mining and information integration. This article describes a knowledge-based approach to quantify the semantic similarity of lexical definitions. Grounded in the recursive intuition that similar terms are described using similar terms, the approach relies on paraphrase-detection techniques and the lexical database WordNet. The cognitive plausibility of the approach is evaluated in the context of the OpenStreetMap (OSM) Semantic Network, obtaining high correlation with human judgements. Guidelines are provided for the practical usage of the approach.     

A framework to enhance semantic flexibility for analysis of distributed phenomena

While some geographic phenomena hold uniform properties, such as land‐use zones, many geographic phenomena are distributed such that their properties vary across an extended area. While such distributed phenomena are best represented as continuous surfaces, individual objects (or features) often emerge among clusters of high or low values in a field. For example, areas of relatively high elevation may be viewed as hills, while flat low‐lying areas are perceived as plains in a terrain. A comprehensive spatial analysis of distributed phenomena should examine both the spatial variance of its attribute surfaces and the characteristics of individual objects embedded in the field. An immediate research challenge to meet such spatial analysis needs is that these emerging features often have vague boundaries that vary according to the use and the user. The nature, and even existence, of these objects depend upon the range of values, or thresholds, used to define them. We propose a representation framework that takes a dual raster‐vector approach to capture both field‐ and object‐like characteristics of distributed phenomena and maintain multiple representations of embedded features delineated by boundaries that are likely to be relevant for the expected uses of the data. We demonstrate how boundaries influence the analysis and understanding of spatiotemporal characteristics of distributed phenomena. Using precipitation as a proof of concept, we show how the proposed framework enhances semantic flexibility in spatiotemporal query and analysis of distributed phenomena in geographic information systems.     

Relationships between spatial and non-spatial preferences and place-based values in national forests

The management of multiple-use public lands such as national forests in the U.S. is controversial because of the wide range of potential uses and contested public values for these lands. Previous research on national forest values and management preferences examined these relationships non-spatially. The development of public participation GIS (PPGIS) and volunteered geographic information (VGI) systems provide new opportunities to assess spatial relationships between perceived national forest values and preferred uses. This research used empirical data collected from a PPGIS/VGI study for three national forests in California to examine spatial and non-spatial relationships between place-based forest values and preferred uses. The relationships suggest public participants translated some of their non-spatial forest values and preferences into behavioral choices when mapping place-specific values and preferred uses. The empirical relationships between place-based forest values and uses were generally consistent with previous survey research results. Positive, non-spatial attitudes toward extractive uses of national forests were correlated with participant mapping of economic values and related extractive uses, while nonmaterial forest attitudes were correlated with participant mapping of amenity values and conservation-related uses. Further, spatial preferences for extractive forest uses such as timber harvesting were mapped proximate to economic values, while nonmaterial spatial preferences were mapped proximate to amenity-related forest values. PPGIS offers the potential to translate philosophical and ideology-based national forest preferences into place-specific discourse about forest management activities where public accommodation may be more achievable.     

Coverage inequality and quality of volunteered geographic features in Chinese cities: Analyzing the associated local characteristics using geographically weighted regression

The volunteered geographic information (VGI) gains increasing popularity with the general public and scientific community. However, the optimism about the VGI has been tempered by two critical issues: inequality in data coverage (social justice) and data quality. It therefore requires a better understanding of the mechanism driving VGI contributions and content quality. With a case of China, this paper demonstrates one potential avenue, examining the associations between VGI coverage/quality and local demographic and socioeconomic characteristics. In particular, VGI data are harvested from the OpenStreetMap for 333 cities in China. VGI coverage is measured by the total volume of different geographic features (point, line and polygon); and VGI quality is described from two aspects: completeness and accuracy. Geographically weighted regression (GWR) shows that both demographic and socioeconomic factors have statistically significant influences on VGI coverage and quality. More specifically, densely populous cities with more young, educated and non-agricultural people enjoy higher VGI coverage and quality. Cities with lower VGI coverage and quality are primarily located in the western and southwestern regions where the ethnic minorities concentrate. High VGI coverage and quality are possibly observed in economically developed cities with high marketization degree. Besides, possibility of high VGI coverage and quality occurs in cities with more labor in scientific research and greater percentage of employers in the tertiary industry. The GWR also demonstrates that the strength and nature of the obtained relationships vary across the 333 cities. The spatial non-stationary relationships may partially answer for the controversial empirical conclusions in earlier case studies at different scales. Quantitative analysis (Gini index, Lorenz curve and Moran's I index) further evidences the great inequality in VGI coverage and quality. It can be safely inferred that the differences in engagement and use of VGI, as a new digital divide, can raise troubling concerns on the social justice implications.     

The ‘cottage effect’ in citizen science? Spatial bias in aquatic monitoring programs

Citizen science aquatic monitoring programs often rely on opportunistic, incidental contributions, which can lead to spatial bias, the uneven geographical distribution of sample sites. It is less known how this spatial bias compares to professional monitoring activities, or how geospatial biases (e.g. terrain slope, population density, road density) influence aquatic citizen science and professional lake monitoring programs. This paper compares sample sites in Ontario’s volunteer Lake Partner Program, against those identified by a stratified random sampling method currently used by the Province of Ontario, Ministry of Natural Resources and Forestry. Exploration of spatial bias within each sampling method was conducted using Kernel Density Estimation, a nonparametric approach to interpolating the spatial trend of a given variable. Results indicate that two distinct patterns of sampling clusters exist between the two datasets, suggesting a ‘cottage effect’ in which volunteers are more likely to sample accessible locations associated with recreation and summer home ownership. Although professional monitoring programs are not exempt from spatial bias, our research suggests that citizen science lake monitoring programs in Ontario are more influenced by natural and demographic biases related to the location, accessibility, size and general attractiveness of lakes.     

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.     

基于地理信息元组的分类预测理论及应用——以中亚矿集区预测为例

地理信息科学的迅速发展为矿集区预测提供了新的理论依据,使地理信息理论、相应的技术方法组合及应用的整体研究成为可能。在大量理论和实验研究的基础上提出了基于地理信息元组的分类预测理论体系,该理论体系由六个概念、一个空间信息本质、两个空间信息特性以及六个空间信息相关方法和约定的构成。并在该理论体系基础上结合空间数据分析方法和探索式空间数据分析方法,建立了基于地理信息元组的分类预测模型。应用该模型在中亚区域实现了Au矿集区预测目标。为中亚成矿域中其它区域(如新疆)的矿集区预测研究奠定了理论与方法论基础。     

Automatic alignment of contemporary vector data and georeferenced historical maps using reinforcement learning

ABSTRACT

With large amounts of digital map archives becoming available, automatically extracting information from scanned historical maps is needed for many domains that require long-term historical geographic data. Convolutional Neural Networks (CNN) are powerful techniques that can be used for extracting locations of geographic features from scanned maps if sufficient representative training data are available. Existing spatial data can provide the approximate locations of corresponding geographic features in historical maps and thus be useful to annotate training data automatically. However, the feature representations, publication date, production scales, and spatial reference systems of contemporary vector data are typically very different from those of historical maps. Hence, such auxiliary data cannot be directly used for annotation of the precise locations of the features of interest in the scanned historical maps. This research introduces an automatic vector-to-raster alignment algorithm based on reinforcement learning to annotate precise locations of geographic features on scanned maps. This paper models the alignment problem using the reinforcement learning framework, which enables informed, efficient searches for matching features without pre-processing steps, such as extracting specific feature signatures (e.g. road intersections). The experimental results show that our algorithm can be applied to various features (roads, water lines, and railroads) and achieve high accuracy.     

Combining conventional and volunteered geographic information to identify and model forest recreational resources

It is now common for researchers in natural resource management and economics to develop spatial models of recreation demand for the purposes of valuation and planning. Such an approach has significant advantages but requires access to relatively detailed spatial data. In the absence of official data concerning resources, researchers may benefit from employing increasingly available volunteered geographic information (VGI) as a central source of data or to complement more traditional data sources. This paper describes the development of a map of forest recreational resources in Ireland by combining conventional forest cover data and VGI of recreational trails. As an extension the new map is combined with household survey data to explore respondent's ability to identify local recreational forests and models the impact of the characteristics of the closest forest on their annual visitation. The results suggest that VGI can be a useful resource to researchers in both the identification and characterisation of recreational resources. In addition, this paper finds that the distance to and characteristics of the nearest recreational forest has a significant impact on total annual visitation by members of the public.