A Review of Sampling Effects and Response Bias in Internet Participatory Mapping (PPGIS/PGIS/VGI)

Global interest in participatory mapping described as public participation GIS (PPGIS), participatory GIS (PGIS), and volunteered geographic information (VGI) continues to grow, but systematic study of spatial data quality and sampling effects is limited. This article provides a review and meta‐analysis of Internet‐based PPGIS studies conducted during the period 2006–2015 (n=26) to answer the following research questions: (1) How does mapping effort, as a proxy measure for spatial data quality, differ by sampling group? (2) Does the purpose and context of PPGIS influence mapping results? (3) What is the potential for mapping bias through sampling design? (4) Given the results, what should be the focus of future PPGIS research? Mapping effort was highest in sampling groups whose livelihoods were closely related to the purpose of the study, there was greater mapping effort in household sampling groups compared to volunteer groups, and participant domicile had strong effects on mapped results through spatial discounting. The use of online Internet panels provides higher response rates but lower spatial data quality. Future research should focus on increasing sampling response rates, assessing social trade‐offs using alternative spatial weighting schemes, and examining the capacity of the public to select land use alternatives as a complement to traditional expert‐driven planning systems.     

Crowdsourcing functions of the living city from Twitter and Foursquare data

ABSTRACT

Urban functions are closely related to people’s spatiotemporal activity patterns, transportation needs, and a city’s business distribution and development trends. Studies investigating urban functions have used different data sources, such as remotely sensed imageries, observation, photography, and cognitive maps. However, these data sources usually suffer from low spatial, temporal, and thematic resolution. This article attempts to investigate human activities to understand urban functions through crowdsourcing social media data. In this study, we mined Twitter and Foursquare data to extract and analyze six types of human activities. The spatiotemporal analysis revealed hotspots for different activity intensities at different temporal resolution. We also applied the classified model in a real-time system to extract information of various urban functions. This study demonstrates the significance and usefulness of social sensing in analyzing urban functions. By combining different platforms of social media data and analyzing people’s geo-tagged city experience, this article contributes to leverage voluntary local knowledge to better depict human dynamics, discover spatiotemporal city characteristics, and convey information about cities.     

Geo‐questionnaire: A Method and Tool for Public Preference Elicitation in Land Use Planning

Geo‐questionnaire involves an integration of sketchable maps with questions, aimed at eliciting public preferences and attitudes about land allocation and services. Respondents can link their answers with corresponding locations on a map by marking points or sketching polygon features. Geo‐questionnaires have been used to learn about perceptions and preferences of city residents for specific types of land use, place‐based services, and development projects. This article reports on results of an empirical study, in which an online geo‐questionnaire was designed and implemented to elicit preferences of residents in guiding an urban development plan. Preferences collected in the form of polygon sketches were processed using GIS operations and mapped for visual interpretation. The article focuses on aggregation and analysis of respondent preferences including the analysis of positional and attribute uncertainty. Results of the study show that geo‐questionnaire is a scalable method for eliciting public preferences with a potential for meaningfully informing land use planning.     

Spatially explicit urban green indicators for characterizing vegetation cover and public green space proximity: a case study on Brussels,Belgium

ABSTRACT

Cities often have a substantial green infrastructure, which provides local ecosystem services that improve the quality of life of urban residents. These services should be explicitly addressed in urban development policies, and areas with insufficient vegetation and limited access to public green spaces should be identified. This paper presents two spatially explicit urban green indicators that are derived using remote sensing imagery, freely available map data and spatial analysis tools from open source geospatial libraries and commercial software. The first indicator represents proportional green cover (public as well as private) in the vicinity of each building within a city. The second indicator quantifies the proximity of public green spaces as walking distances from buildings to actual park entrances. A dasymetric mapping approach was used to take spatial variations in population density into account. This allows representing the indicators from the perspective of citizens instead of buildings, which may be more meaningful for deriving statistics at city level or at the level of neighbourhoods or administrative zones. The potential use of these indicators in a planning context is discussed on a case study carried out for the city of Brussels, Belgium.     

Evaluation of the quality of an online geocoding resource in the context of a large Brazilian city

Geocoding urban addresses usually requires the use of an underlying address database. Under the influence of the format defined for TIGER files decades ago, most address databases and street geocoding algorithms are organized around street centerlines, associating numbering ranges to thoroughfare segments between two street crossings. While this method has been successfully employed in the USA for a long time, its transposition to other countries may lead to increased errors. This article presents an evaluation of the centerline‐geocoding resources provided by Google Maps, as compared to the point‐geocoding method used in the city of Belo Horizonte, Brazil, which we took as a baseline. We generated a textual address for each point object found in the city's point‐based address database, and submitted it to the Google Maps geocoding API. We then compared the resulting coordinates with the ones recorded in Belo Horizonte's GIS. We demonstrate that the centerline segment interpolation method, employed by the online resources following the American practice, has problems that can considerably influence the quality of the geocoding outcome. Completeness and accuracy have been found to be irregular, especially within lower income areas. Such errors in online services can have a significant impact on geocoding efforts related to social applications, such as public health and education, since the online service can be faulty and error‐prone in the most socially demanding areas of the city. In the conclusion, we point out that a volunteered geographic information (VGI) approach can help with the enrichment and enhancement of current geocoding resources, and can possibly lead to their transformation into more reliable point‐based geocoding services.     

利用位置签到数据探索城市热点与商圈

众源地理数据（Crowd Sourcing Geographic Data）是指由大众采集并向大众开放共享的地理空间数据。众源位置签到数据作为众源地理数据的一种,客观真实的反映了大众日常生活行为,包含大量丰富的社会化属性信息。本文提出了一种基于众源位置签到数据的城市热点探测与商圈挖掘方法,首先对位置签到数据时空分布特性进行了研究,设计并提出了众源位置签到数据时空数据库模型;其次,提出了位置签到数据探索性空间分析方法,通过对众源位置签到数据的空间聚类分析,实现基于位置签到数据的商圈热点探测;最后,以武汉市为例,对街旁网截止2011年9月30日的众源位置签到数据进行了城市热点探测与商圈挖掘分析实验,结果表明,基于众源位置签到数据挖掘的武汉市商圈分布与城市规划商圈具有强相关性,可用于城市社会经济发展预测与区域经济规划。     

Characterizing European urban settlements from population data: a cartographic approach

ABSTRACT

The cartographic identification and characterization of urban settlements is problematic, particularly in varied areas or in large geographical extensions. This is due to the divergence between morphological area structures and data attribution, which is generally available at the level of administrative units. Most existing experiences focus on small regions or use indirect data, from which only indirect information can be derived. This paper proposes an identification of urban settlements in the European Union space, which combines urban population and shape through geographic information system (GIS) techniques. For that purpose, 1 km² cells with associated population data are used. A procedure is developed establishing three different types of urban settlements, according to population and population density values: high-density urban clusters, small and medium-sized towns, and very small towns. A validation of this procedure is performed using direct survey from local experts in 10 selected study areas. The final results show that 78,307 urban settlements can be identified: 88.17% of them are classified as “very small towns,” 10.75% as “small and medium-sized towns,” and 1.08% as high-density urban clusters. This exercise generates new information for the European Union and surrounding countries, and yields the basis for further research. All GIS operations are performed using vector layers, which is not usual in large-area regional studies.     

The socio-environmental data explorer (SEDE): a social media–enhanced decision support system to explore risk perception to hazard events

ABSTRACT

Social media are increasingly recognized as a useful data source for understanding social response to hazard events in real time and in post-event analysis. This article establishes social media–enhanced decision support systems (SME-DSS) as a synergistic integration of social media and decision support systems (DSSs) to provide structured access to native, near real-time data from a large and diverse population to assess social response to social, environmental, and technological risk and hazard events. We introduce a prototype SME-DSS entitled socio-environmental data explorer (SEDE) to explore the opportunities and challenges of leveraging social media for decision support. We use a winter storm during 25–28 January 2015 that accumulated record amounts of snow along the East Coast of the United States as a case study to evaluate SEDE in helping assess social response to environmental risk and hazard events as well as evaluate social media as a theoretical component within the social amplification of risk framework (SARF) that serves as a theoretical foundation for SME-DSS.     

Reference data enhancement for geographic information retrieval using linked data

Gazetteers are instrumental in recognizing place names in documents such as Web pages, news, and social media messages. However, creating and maintaining gazetteers is still a complex task. Even though some online gazetteers provide rich sets of geographic names in planetary scale (e.g. GeoNames), other sources must be used to recognize references to urban locations, such as street names, neighborhood names or landmarks. We propose integrating Linked Data sources to create a gazetteer that combines a broad coverage of places with urban detail, including content on geographic and semantic relationships involving places, their multiple names and related non‐geographic entities. Our final goal is to expand the possibilities for recognizing, disambiguating and filtering references to places in texts for geographic information retrieval (GIR) and related applications. The resulting ontological gazetteer, named LoG (Linked OntoGazetteer), is accessible through Web services by applications and research initiatives on GIR, text processing, named entity recognition and others. The gazetteer currently contains over 13 million places, 140 million attributes and relationships, and 4.5 million non‐geographic entities. Data sources include GeoNames, Freebase, DBPedia and LinkedGeoData, which is based on OpenStreetMap data. An analysis on how these datasets overlap and complement one another is also presented.     

Understanding demographic and socioeconomic biases of geotagged Twitter users at the county level

ABSTRACT

Massive social media data produced from microblog platforms provide a new data source for studying human dynamics at an unprecedented scale. Meanwhile, population bias in geotagged Twitter users is widely recognized. Understanding the demographic and socioeconomic biases of Twitter users is critical for making reliable inferences on the attitudes and behaviors of the population. However, the existing global models cannot capture the regional variations of the demographic and socioeconomic biases. To bridge the gap, we modeled the relationships between different demographic/socioeconomic factors and geotagged Twitter users for the whole contiguous United States, aiming to understand how the demographic and socioeconomic factors relate to the number of Twitter users at county level. To effectively identify the local Twitter users for each county of the United States, we integrate three commonly used methods and develop a query approach in a high-performance computing environment. The results demonstrate that we can not only identify how the demographic and socioeconomic factors relate to the number of Twitter users, but can also measure and map how the influence of these factors vary across counties.     

A GIS Approach to Estimation of Building Population for Micro-spatial Analysis

Population data used in GIS analyses is generally assumed to be homogeneous and planar (i.e. census tracts, townships or prefectures) due to the public unavailability of building population data. However, information on building population is required for micro-spatial analysis for improved disaster management and emergency preparedness, public facility management for urban planning, consumer and retail market analysis, environment and public health programs and other demographic studies. This article discusses a GIS approach using the Areametric and Volumetric methods for estimating building population based on census tracts and building footprint datasets. The estimated results were evaluated using actual building population data by visual, statistical and spatial means, and validated for use in micro-spatial analysis. We have also implemented a standalone GIS tool (known as 'PopShape GIS') for generating new building footprint with population attribute information based on user-defined criteria.     

An Evaluation of Internet Versus Paper‐based Methods for Public Participation Geographic Information Systems (PPGIS)

Public participation geographic information systems (PPGIS) are an increasingly important tool for collecting spatial information about the social attributes of place. The availability of Internet‐based options for implementing PPGIS presents new opportunities for increased efficiency and new modes of access. Here we used a mixed‐mode approach to evaluate paper versus Internet mapping methods for the same PPGIS survey in Wyoming. We compared participant characteristics, mapping participation, and the spatial distribution of mapped attributes between participants who responded to the paper versus Internet option. The response rate for those who completed the paper version of the survey was nearly 2.5 times the response rate of the Internet version. Paper participants also mapped significantly more places than did Internet participants (43 vs. 18). Internet participants tended to be younger, more likely to have a college degree, and had lived in the region for less time than paper participants. For all but one attribute there was no difference in the spatial distribution of places mapped between Internet and paper methods. Using a paper‐based PPGIS survey resulted in a higher response rate, reduced participant bias, and greater mapping participation. However, survey mode did not influence the spatial distribution of the PPGIS data.     

Latent spatio-temporal activity structures: a new approach to inferring intra-urban functional regions via social media check-in data

Abstract

This article introduces a novel low rank approximation (LRA)-based model to detect the functional regions with the data from about 15 million social media check-in records during a year-long period in Shanghai, China. We identified a series of latent structures, named latent spatio-temporal activity structures. While interpreting these structures, we can obtain a series of underlying associations between the spatial and temporal activity patterns. Moreover, we can not only reproduce the observed data with a lower dimensional representative, but also project spatio-temporal activity patterns in the same coordinate system. With the K-means clustering algorithm, five significant types of clusters that are directly annotated with a combination of temporal activities can be obtained, providing a clear picture of the correlation between the groups of regions and different activities at different times during a day. Besides the commercial and transportation dominant areas, we also detected two kinds of residential areas, the developed residential areas and the developing residential areas. We further interpret the spatial distribution of these clusters using urban form analytics. The results are highly consistent with the government planning in the same periods, indicating that our model is applicable to infer the functional regions from social media check-in data and can benefit a wide range of fields, such as urban planning, public services, and location-based recommender systems.     

Bayesian networks and agent-based modeling approach for urban land-use and population density change: a BNAS model

Land-use change models grounded in complexity theory such as agent-based models (ABMs) are increasingly being used to examine evolving urban systems. The objective of this study is to develop a spatial model that simulates land-use change under the influence of human land-use choice behavior. This is achieved by integrating the key physical and social drivers of land-use change using Bayesian networks (BNs) coupled with agent-based modeling. The BNAS model, integrated Bayesian network–based agent system, presented in this study uses geographic information systems, ABMs, BNs, and influence diagram principles to model population change on an irregular spatial structure. The model is parameterized with historical data and then used to simulate 20 years of future population and land-use change for the City of Surrey, British Columbia, Canada. The simulation results identify feasible new urban areas for development around the main transportation corridors. The obtained new development areas and the projected population trajectories with the“what-if” scenario capabilities can provide insights into urban planners for better and more informed land-use policy or decision-making processes.     

城市公共交通覆盖度空间统计分析研究

为了合理分析城市公共交通发展现状并对其发展提供可参考的依据,本文利用地理信息系统空间分析和统计方法,在确立多个评价指标的基础上,对城市公共交通覆盖度情况进行不同角度、不同方面的评价。以沈阳市内部分区域为研究对象,提取研究区域基础地理数据,结合人口数据以及公共交通线网缓冲区,选取城市主次干道为边界划分分析单元,将人口密度与道路数据相结合,将评价体系与数据进行对比分析。根据评价指标,得到研究范围内城市公交资源的分布状况。实验结果表明,该方法能够较好地分析城市公共交通分布特征,为城市交通规划设计提供思路。     

网络RTK技术和传统测量技术结合在数字城管中的应用

随着经济和社会的不断发展,城市人口不断增多,城市规模不断扩大,城市管理范围越来越广,城市公共部件越来越多,如何获得这些公共部件数据并通过信息化手段进行管理以提高城市管理的效率是城市管理迫切需要解决的问题,本文在分析网络RTK特点的基础上提出了采用网络RTK技术和传统测量技术相结合的方式来获取城市公共部件,一方面可以提高数据采集效率,另一方面也可以保证数据采集的精度。     

Statistical analysis of surface urban heat island intensity variations: A case study of Babol city,Iran

The urban heat island is considered as one of the most important climate change phenomena in urban areas, which can result in remarkable negative effects on flora, concentration of pollutants, air quality, energy and water consumption, human health, ecological and economic impacts, and even on global warming. The variation analysis of the surface urban heat island intensity (SUHII) is important for understanding the effect of urbanization and urban planning. The objective of this study was to present a new strategy based on the Shannon’s entropy and Pearson chi-square statistic to investigate the spatial and temporal variations of the SUHII. In this study, Landsat TM, ETM+, OLI and TIRS images, MODIS products, meteorological data, topographic and population maps of the Babol city, Iran, from 1985 to 2017, and air temperature data recorded by ground recorder devices in 2017 were used. First, Single-Channel algorithm was used to estimate land surface temperature (LST), and the maximum likelihood classifier was employed to classify Landsat images. Then, based on LST maps, surface urban heat island ratio index was employed to calculate the SUHII. Further, several statistical methods, such as the degree-of-freedom, degree-of-sprawl and degree-of-goodness, were used to analyse the SUHII variation along different geographic directions and in various time periods. Finally, correlation between various parameters such as air temperature, SUHII, population variation and degree-of-goodness index values were investigated. The results indicated that the SUHII value increased by 24% in Babol over different time periods. The correlation coefficient yielded 0.82 between the values of the difference between the mean air temperature of the urban and suburbs and the SUHII values for the geographic directions. Furthermore, the correlation coefficient between the population variation and the degree-of-goodness index values reached 0.8. The results suggested that the SUHII variation of Babol city had a high degree-of-freedom, high degree-of-sprawl and negative degree-of-goodness.     

Exposing the urban continuum: implications and cross-comparison from an interdisciplinary perspective

ABSTRACT

There is an increasing availability of geospatial data describing patterns of human settlement and population such as various global remote-sensing based built-up land layers, fine-grained census-based population estimates, and publicly available cadastral and building footprint data. This development constitutes new integrative modeling opportunities to characterize the continuum of urban, peri-urban, and rural settlements and populations. However, little research has been done regarding the agreement between such data products in measuring human presence which is measured by different proxy variables (i.e. presence of built-up structures derived from different remote sensors, census-derived population counts, or cadastral land parcels). In this work, we quantitatively evaluate and cross-compare the ability of such data to model the urban continuum, using a unique, integrated validation database of cadastral and building footprint data, U.S. census data, and three different versions of the Global Human Settlement Layer (GHSL) derived from remotely sensed data. We identify advantages and shortcomings of these data types across different geographic settings in the U.S., which will inform future data users on implications of data accuracy and suitability for a given application, even in data-poor regions of the world.     

对新型智慧城市三维模型“计算思维”的思考

新型智慧城市地理信息公共服务平台及三维模型建设,大多应用BIM、VR、AR,以及数字航空摄影测量、高分辨率卫星遥感、无人机摄影测量系统等技术,然而,这些感官成果缺少计算思维。在大数据时代的今天,人们对地理信息成果的要求不仅能"直观看到",还要具备基于计算思维满足智能需求的计算功能。应用倾斜摄影实景三维技术构建城市地理信息底层因满足了这一需求将备受关注。     

基于地理国情监测成果的通用专题服务平台设计

为了提高服务能力和保障水平,对地理国情监测成果资源进行整合,使其在经济社会发展、民生需求中发挥整体效应。针对目前公众地理信息服务和用户需求,本文构想一个通用的智能专题地图开放服务平台,开创交互式地图,实现一键式制作专业地图,面向企业机构和个人用户提供在线地图与地理信息服务,从而辅助业务决策,打造企业或个人专属地图应用。依托信息丰富的地理国情监测成果数据,实现交互式地图应用,面向机关、企业机构和个人用户提供免费、便捷的在线地理信息服务。通过快速准确的数据匹配,展现地理位置和专题数据的空间关系。让各种用户数据、专题数据内容在地理空间上图文并茂地展示,从而起到辅助业务决策、分析和解决实际问题的作用。本文构想的平台能够加强地理国情监测成果应用推广,满足国民经济和社会信息化建设对测绘工作的需求,推动我国测绘行业成果应用。