Urban" is something that intuitively feels very well defined; however, when it comes time to express this idea on a map, things get complicated.Definitions of "urban" vary globally, and as such there is not universal understanding of what makes a given place "urban".The common approach to spatially defining urban extents is through remotely sensed imagery.The alternative approach presented in this paper uses the percent of population in urban areas, which is a common macroeconomic (country-level) variable with the definition for urban generally defined by each country's statistical office, along with temporally-aligned highresolution population data to spatially define urban extents for each country.Because the percent urban number is defined by the same producer as other urban/rural defined statistical data, such as household characteristics or birth rate, understanding the spatial aspect of urban from the same perspective is ideal for high resolution spatial modeling of these other phenomena.
This paper demonstrates the use of fuzzy clustering to characterize Volunteered Geographic Information (VGI).We argue that classifying small areas based on variables related to the amount, type, and currency of VGI can provide a more nuanced understanding of the content.We present a classification of 2011 UK Census Output Areas in Leicestershire (UK) based on content of OpenStreetMap, using a fuzzy c-means clustering algorithm, and we compare the resulting classification with a 'standard' socio-economic geodemographic classification.
Methods to gather information from the public about place range from ethnographic approaches such as free listing to automatic extraction from user-generated content. We compared aspects of place (location, locale and sense of place) contained in free lists elicited from participants recruited on site with tags from georeferenced Flickr images. Using manual annotation we assigned content as toponyms (location), landscape elements (locale) and feelings (sense of place). Flickr tags contained more toponyms than free-listing data, but almost no information relating to feelings. Landscape elements were prominent in both data sets, with those captured by free lists and Flickr being cognitively more salient than those only captured by free listing, suggesting they represent basic levels. In Flickr, landscape elements consisted of basic levels in different languages (e.g. mountain, Berg), while free lists contained landscape elements both at the basic and subordinate level (e.g. Arvenwalder, arolla pine forests). We conclude that both methods yielded information about locale, with Flickr contributing basic levels and free lists also more detailed information, but that Flickr provided little information about sense of place compared to in situ free-listing elicitation with participants.
GIScience 2016 Short Paper Proceedings Toward A Location Based Service for Assessing and Recommending Landscape Views S. Soleimani 1 , M. R. Malek 1 , G. Sinha 2 Geodesy & Geomatics Department, K.N.Toosi University of Technology, Tehran, Iran Email: {ssoleimani; mrmalek}@kntu.ac.ir Department of Geography, Ohio University, Athens, Ohio, 45701, USA Email: sinhag@ohio.edu Abstract The rationale and initial design considerations are presented for a location based service (LBS) that will offer people personalized recommendations for aesthetically desirable places. This will be based on people’s personal characteristics and environmental factors known to affect landscape perception. A prototype user-interface has been designed for eliciting users’ perceptions of landscape elements and colors they perceive from photographs of landscape views. In the long-term, a VGI repository database will be maintained to make personalized location specific recommendations about suitable landscape views for user-specified locations. A prototype fuzzy logic recommendation system comprising 72 rules has also been designed to model how personal factors (gender, age, health status) and perceived colors likely determine the aesthetic suitability of landscape views. An initial trial revealed some limitations and stressed the need for further testing of the feasibility of fuzzy logic as the reasoning framework for this LBS for recommending suitable landscape views. 1. Introduction As urbanity imposes psychological demands that become excessive, people seek stress- reducing (especially natural) environments where they can recreate and relax. The investigation of the ecological and visual aspects of physical landscapes is critical for understanding and addressing many of the psychosomatic disorders of urban dwellers. In this paper, we focus on visual perception of people’s everyday environments. Human-beings engage in a wide variety of important decisions in their everyday lives, based on visual properties of the geographic environment and their geographical knowledge. Examples include coping with environmental visual pollution, finding one’s way in spatially extended areas, and finding a landscape view appropriate to one’s emotional status. Color, shape, composition, and configuration of objects are important components of visual stimuli that should be studied to understand our perceptions of and reactions to physical landscapes in urban environments. In this short paper, we present the rationale and initial design considerations for a prototype Location Based Service (LBS) for landscape view assessment. This LBS will ultimately offer people personalized recommendations for places (near their chosen location(s)) that are most likely to appeal to them aesthetically. The LBS is designed to make such personalized recommendations based on available knowledge of how people’s personal characteristics affects landscape perception. A prototype user-interface has been designed for eliciting users’ perceptions about the types of landscape elements and the colors they perceive in images showing landscape views. An important goal of this project is to maintain a large database of people’s perception of landscape views, based on volunteered geographic information (VGI) generated from people tagging images uploaded by them and/or others.
This research considers the relationship between spatial cognition and situation awareness, or "spatial awareness" in a refugee camp.Specifically, we present some of the first research on spatial awareness using empirical data collected in the Za'atari Syrian refugee camp in Jordan.Results showed clear spatial awareness differences between male and female in terms of camp infrastructure completeness.This result is likely based on the underlying cultural dynamics of the refugee population.We outline areas for future refugee spatial awareness research such as community asset mapping.A better understanding of how refugees maintain spatial awareness in camp settings can inform GIScience research aimed at (a) identifying new methodologies and educational pathways for supporting spatial awareness in long-term displacement situations, (b) refugee camp design, and (c) space/time representations to ultimately improve the lives of people that are forced to leave their homes and countries due to natural disasters or armed conflicts.
We developed 30 m resolution grids of the US population in 1990, 2000, and 2010 using a multi-year compatible dasymetric model.These grids are designed to assess population change across the conterminous US at street-level spatial resolution.The model and its novel, computationally efficient implementation in R are described.The grids are available online for interactive exploration and data download using especially developed GeoWeb application SocScape (http://sil.uc.edu/webapps/socscape_usa/).
GIScience 2016 Short Paper Proceedings Deriving Hospital Catchment Areas from Mobile Phone Data Bernd Resch 1,2 , Azmat Arif 1 , Gautier Krings 3 , Guillaume Vankeerberghen 3 , Marc Buekenhout 4 University of Salzburg, Department of Geoinformatics – Z_GIS, Schillerstrasse 30, 5020 Salzburg, Austria Email: bernd.resch@sbg.ac.at, azmat.arif@stud.sbg.ac.at Harvard University, Center for Geographical Analysis, 1737 Cambridge Street, Cambridge, MA 02138, USA Real Impact Analytics, 5 Place du Champ de Mars, 1050 Brussels, Belgium Email: {gautier.krings; guillaume.vankeerberghen}@realimpactanalytics.com Email: mbuekenhout@msn.com Abstract Delineating catchment areas of medical facilities is essential for estimating the quality of a health-care system and to maximise the efficiency of health service provision. One critical shortcoming of previous approaches are manifested in their comprehensive assumptions about a hospital’s patients by using census data or gravity models. In contrast, our approach uses anonymised mobile and landline phone data to derive hospital catchment areas. Our goal is not to assess the quality of the health care system, but to identify the geographic areas, in which people actually use a hospital. Thus, our results reveal new insights into the catchment areas of hospitals by minimising assumptions about demographic factors. 1. Introduction and Related Work Adequate provision of health services is a central priority of health professionals and policy makers worldwide. More, the efficiency of health care systems, i.e., the provision of best possible service with minimum resources, is critical to public providers (Fransen et al. 2015). These requirements have led to a number of studies to analyse catchment areas and service quality of medical facilities. Even though geospatial analysis methods have existed for decades, there is still a general lack of studies that have mapped and examined health service catchments in practice, not only from a theoretical viewpoint (Schuurman et al. 2006). Previous approaches for delineating medical catchment areas comprise statistical population-to-provider ratios, gravitational models, travel cost estimation, analysis of the physical distance between hospitals, census-based patient-origin analysis, commuter-based approaches of modelling spatial accessibility (Fransen et al. 2015; Wang and Wheeler 2015), or the two-step floating catchment area method based on the physician-to-population ratio (Luo and Wang 2003). The major drawback of these approaches is that they make far- reaching assumptions about a hospital’s patients by applying census data, travel times or gravity models. Moreover, they do not take heterogeneous activity and mobility patterns into account or only derive them from static census data. Thus, the approach proposed in this paper uses anonymised mobile and landline phone data to delineate hospital catchment areas. Like this, we aim to identify the geographic areas, in which people use a hospital instead of assessing the quality of the health care system per se. Therefore, we analyse calls to and from hospitals in Trinidad and Tobago. This goes beyond just using patient records in that we are able to draw conclusions from a wider range of communication with a hospital (enquiries, arrangement of appointments, follow-up care, visitors, etc.), beyond patients’ hospital stays.
This paper applies a local analysis to model and predict hedonic house price in Hanoi, Vietnam.It applies a locally compensated geographically weighted ridge regression to data survey data collected to support the Status Quality Trade Off theory proposed by Phe and Wakely (2000).This has an inherently local flavour is therefore suitable for local statistical approaches such as GWR (Brunsdon et al., 1996).The locally compensated ridge regression accounts for the observed local collinearity.The results provide a spatially nuanced model of status poles associated with areas of desirable housing.Some key areas for future work are suggested.
GIScience 2016 Short Paper Proceedings Modelling Vague Shape Dynamic Phenomena from Sensor Network data using a Decentralized Fuzzy Rule-Based Approach Roger Cesarie Ntankouo Njila 1 , Mir Abolfazl Mostafavi 1 , Jean Brodeur 1 Centre de Recherche en Geomatique, 2314 Pavillon Casault; Universite Laval, Quebec, Canada, G1K 7P4 Email: roger-cesarie.ntankouo-njila.1@ulaval.ca, Mir-Abolfazl.Mostafavi@scg.ulaval.ca, recherchegeosemantic@videotron.ca Abstract Modelling dynamic phenomena of vague shape from sensor data is still a challenging problem for many applications. In this paper, we propose a decentralized fuzzy rule-based approach based on fuzzy object model to build a more realistic spatiotemporal representation for such phenomena. This approach has been successfully implemented in a simulation case of bushfire monitoring, showing advantages for spatial decision making in a disaster management context. Keywords: sensors, sensor data, fuzzy objects, disaster management, spatial decision support 1. Introduction Extracting geospatial information from geosensor data can help to better understand a complex phenomenon for real time decision making process (Sadeq et al. 2013). Several approaches are used for the extraction of geospatial information from sensor data. Many of these approaches are developed based on the assumption that monitored phenomena are of crisp shape with well- defined boundaries. However, many dynamic phenomena have vague spatial boundaries, and their accurate detection and extraction from sensor data is a challenging problem. In this paper we propose a decentralized fuzzy rule-based approach to address this problem. In the proposed method, sensors detect vague shape phenomena using a fuzzy logic reasoning approach and collaborate with their neighboring sensors to infer vague spatial extent of the phenomena and its dynamics. We adopt Crisp-Fuzzy objects (Pauly and Schneider 2008) as a more realistic model for large scale and vague shape dynamic phenomena. This paper is organized as follows. After a brief background presented in section 2, section 3 describes the proposed approach implemented in section 4 for a bushfire simulation case showing it applicability for real time spatial decision process in disaster management. Section 5 presents conclusions and future works. 2. Background Spatial computing can be undertaken in a sensor system following a centralized (all data are sent to process center), a decentralized (located at sensor site or other site) or a hybrid approach (Chong and Kumar 2003). Crisp vector objects are extracted in the existing approaches using statistics or filters (Chintalapudi and Govindan 2003) or qualitative reasoning (Guan and Duckham 2009). Real-world phenomena are inherently uncertain (Carniel et al. 2015). Crisp-Fuzzy objects model (Pauly and Schneider 2008) is an interesting candidate for the representation of such phenomenon. In this model the geometry of object is composed of a kernel and a conjecture part, the kernel part belongs definitely and always to the vague object but one can’t say with certainty whether conjecture part considered as the broad boundary belongs to the vague object.
We gladly use automated technology (e.g., smart devices) to extend our hard working minds.But what if such technology turns into mind crutches we cannot do without?Understanding how varying levels of automation in mobile maps might impact navigation performance and spatial knowledge acquisition will provide important insights for the ongoing debate on the potentially detrimental effects of using navigation systems on human spatial cognition.We need to identify the right balance between system automation (support) and user autonomy (self-reliance).Preliminary results of a pilot study performed within a novel empirical framework indicate that it is possible to increase user autonomy and spatial knowledge acquisition without negatively impacting navigation performance and usefulness of the system.
Geospatial data stored in databases and other formats can be accessed through Web Feature Service (WFS).However, it is not convenient to access data in multiple WFS servers since WFS protocol is geared towards single server.In this paper, we propose an algorithm to query and synthesize distributed WFS data through a RDF query interface, where users can specify data requests to multiple WFS servers using a single RDF query.The algorithm translates each RDF query written in SPARQL-like syntax to multiple WFS get-feature requests, and then convert the WFS results to answers to the original query.A lightweight Web-based prototype is implemented based on this approach.
GIScience 2016 Short Paper Proceedings Context-sensitive spatiotemporal simulation model for movement S. Dodge Department of Geography, Environment, and Society, University of Minnesota Email: sdodge@umn.edu Abstract This paper presents a context-sensitive spatiotemporal model to simulate movement trajectories. The model incorporates both the correlated random walk and time-geography theories to generate a more realistic trajectory of an agent within its environment. 1. Introduction Movement is an essential form of temporal change that is an integral characteristic of dynamic entities (e.g. humans, animals, vehicles, diseases). It is the focus of research in a range of application domains such as transportation, movement ecology, environmental studies, and human health. Movement models help us to better understand the characteristics of movement, enable us to simulate movement and predict its patterns (Dodge 2016). Examples of existing movement models include the random walk and its variations (Codling et al. 2008, Technitis et al. 2015), time-geography (Miller 2005, Song and Miller 2014), and Brownian Bridge (Horne et al. 2007) models. These models either generate trajectories using a set of geometric movement parameters (turn angle, distance), or they identify a visitation probability surface for an agent considering its speed and time budget. Existing models often disregard the characteristics of the environment or the context within which the movement takes place. Simulation of movement in relation to its embedding context is an essential problem that is applied to generate trajectories to fill gaps in low-resolution tracking datasets, or to examine behavioral responses of moving agents to environmental changes. This paper introduces a context-sensitive spatiotemporal simulation model based on a correlated random walk with external biases and is controlled by time-geography constraints of the moving agent. The novelty of the model is that at each step the simulation is driven by behavior and the contextual factors (i.e. environment, geography) that influence the local movement of the agent. As a case study, this research uses GPS observations of a tiger to parameterize the model and to simulate the tiger’s movement between actual GPS observations. 2. Movement Simulation The overall goal is to generate a trajectory (a sequence of spatiotemporal points) from a start location and time !(# $ , & $ , ' $ ) to an end location and time )(# * , & * , ' * ). The simulation uses a correlated random walk from ! with an external bias to move towards ) (i.e. global constraints). The local movement at each step is driven by agent’s behavior and contextual factors. The model specifications are: (1) the maximum movement speed is determined by behavior (e.g. patrolling, hunting, foraging, biking), (2) the global movement path and speed are controlled by the actual time-budget to reach the end-point, and (3) the path is influenced by agent’s local choices based on context (environmental drivers and spatial constraints, -e.g. general movement direction, slope preferences, trail network). The simulation algorithm runs on regular time intervals defined by the user, named step time , to ensure the global movement occurs within the time-budget ('+ = ' * − ' $ ). The maximum speed of the agent (/ 012 ) is determined based on expert knowledge or derived
GIScience 2016 Short Paper Proceedings Scalability in Participatory Planning: A comparison of online PPGIS methods with face-to-face meetings Piotr Jankowski 1,2 , Michal Czepkiewicz 2 , Marek Mlodkowski 2 Michal Wojcicki 2 Zbigniew Zwolinski 2 San Diego State University, Department of Geography San Diego, CA 92182-4493 Email: pjankows@mail.sdsu.edu Institute of Geoecology and Geoinformation Adam Mickiewicz University in Poznan, Poznan, Poland 1. Introduction A traditional approach to participatory planning involves face-to-face engagement of interested public in the tradition of town-hall meetings. A limitation of this approach has been its inability to scale public participation out to involve more people and up to involve participants from a wider geographical area (Nyerges and Aguirre 2011, Innes and Booher 2004). Although online Public Participation GIS (PPGIS) methods are not a panacea for scaling public participation, they offer a potential for collecting views and preferences of some residents who typically do not participate in open planning processes. The questions of who participates and whose views are represented have been at the heart of theorizing about PPGIS (Schlossberg and Shufford 2005, Sieber 2006), and were highlighted as core research questions in a recent review (Brown and Kytta, 2014). This paper contributes to the literature by comparing online PPGIS and face-to-face methods in two aspects of scalability: number of participants (scaling out) and spatial extent (scaling up). Additionally, the demographic representation across the methods is also assessed. The evaluation is based on an empirical study involving four participatory planning processes, which took place between May 2014 and July 2015 in the City of Poznan (pop. 554 thousand), Poland. The processes were focused on local land use plan for a centrally located, multi-functional area in the City of Poznan, including a park, recreational facilities, allotment gardens, single- and multi- family housing, and a public school. Two of the four processes were traditional town-hall meetings (May 2014 and June 2015) whereas the other two (October 2014 and July 2015) employed online PPGIS applications. The dichotomy between the two modes of participatory planning (same-place/same-time and distributed) affords a unique opportunity to compare demographic and spatial scalability of face-to-face meetings with online PPGIS methods. 2. Methods Both of traditional, town-hall style meetings were organized by the municipal planning office in Poznan. The purpose of the first meeting (May 2014) was to familiarize participants with the planning procedure and to facilitate a discussion, during which participants had an opportunity to voice their concerns, opinions, and expectations regarding the planning process. During the
This paper presents a new multi-frequency segmentation method for movement trajectories.The method is presented using a case study of a Turkey Vulture data set.The approach show promising results to automatically extract behavioral modes from movement trajectories at multiple temporal frequencies.
Monte Carlo simulation is a popular numerical experimentation technique used in a range of scientific fields to obtain the statistics of unknown random output variables.Though Monte Carlo simulation is a powerful technique for the probabilistic understanding of many processes, it can only be applied if it is possible to infer the probability distributions describing the required input variables.This is particularly challenging when the input probability distributions are related to population counts unknown at desired spatial resolutions.To overcome this challenge, we propose a framework that uses a dasymetric model to infer the probability distributions needed for a specific class of Monte Carlo simulations dependent on population counts.
Place can be understood, and represented, not only as an attribute of a location but also as the emotional attachments that characterize a relationship between an individual and a location.We demonstrate the development and application of this principle in place-based geographic information systems (GIS) through the use of georeferenced ecological momentary assessment (EMA), an approach for gathering real-time, in-situ data on individuals' mood states, behaviors, and social interactions via brief surveys delivered on a mobile phone.As a case study, we focus on the representation and analysis of the effect of activity space exposure to vacant housing on perceived safety among a sample of 139 urban adolescents enrolled in a longitudinal, georeferenced EMA study.
We propose a crowd sensing system to capture certain dynamics of public participation in a city.Crowd sensing systems (CSS) attempt to capture the opinions of local publics from webresources.We define our CSS using a spatially-situated social network graph where users along with different variables, such as time, location, social interaction, service usage, and human activities can be studied and used to identify experts or influential citizens who are relevant to municipal affairs.
This paper presents a comparative study of existing topographic multi-scale maps, regarding relations between display scale and level of abstraction (LoA) of the map content.The general trends in zoom levels distribution across scale and the original patterns in transitions between LoAs are especially highlighted.
GIScience 2016 Short Paper Proceedings Location Optimization of Fire Stations: Trade-off between Accessibility and Service Coverage J. Yao 1 , X. Zhang 2 Urban Big Data Centre, University of Glasgow, 7 Lilybank Gardens, Glasgow, G12 8RZ, UK Email: Jing.Yao@glasgow.ac.uk Department of Geographical Information Science, Hohai University, 1 Xikang Road, Nanjing, 210098, China Email: Xiaoxiang@hhu.edu.cn Abstract Fire and rescue service is one of the fundamental public services provided by government in order to protect people, properties and environment from fires and other disasters, and thus promote a safe living environment. Efficient deployment of fire stations is necessary and essential if timely response to the emergencies is to be achieved. Spatial optimization approaches have been long employed in public facility location studies. In particular, coverage-based models, such as the location set covering problem (LSCP) and the maximum coverage location problem (MCLP), have been widely adopted to achieve complete or maximum coverage of service demand. This paper extends the LSCP by accounting for both partial coverage and access to the demand areas. The proposed model is applied to the optimization of fire station locations in Nanjing, China. The results can be used to assist future fire station location planning and rescue resource deployment. 1. Introduction Fire caused by humans or nature can pose hazard to people, properties and environment, and lead to psychological damage, physical injuries (even death) and significant economic losses. Fire prevention and protection is necessary and essential for a safe living environment. The associated fire and rescue service therefore needs to be properly deployed to ensure efficient fire safety management. A fundamental concern in this regard is the spatial configuration of fire stations as it is critical to timely response to emergency calls. Given the inherent spatial nature, fire station location problems have been well studied using geographical information system (GIS)-coupled location modelling (Chevalier et al. 2012; Aktas et al. 2013; Murray 2013). In particular, LSCP (Toregas et al. 1971), MCLP (Church and ReVelle 1974) and their extensions have long been employed to evaluate the locational efficiency of existing fire stations as well as seek sites for new fire stations (Chevalier et al. 2012; Murray 2013). Common goals of locating fire stations include maximizing the access to provided services, covering as much demand as possible and minimizing total costs of service provision, usually subject to available resources. In practice, two or more objectives are often considered to capture different aspects in relation to fire service delivery. The aim of this paper is to seek best locations of fire stations with spatial optimization approaches, particularly considering accessibility and service coverage. The proposed model is applied in an empirical study in Nanjing, China, to assist future fire station location planning and rescue resource deployment.
