Location-aware Technologies Research Articles

Development of a Dimming Lighting Control System Using General Illumination and Location-Awareness Technology

As part of ongoing research into saving lighting energy, studies on lighting control integrating location-awareness technologies have recently been increasing, but these have led to the indoor illuminance imbalance problem by controlling only the lighting adjacent to the occupant. Therefore, the purpose of this study is to develop a dimming lighting control system using general illumination and location-awareness technology by integrating general illumination and dimming technology with lighting control technology that is based on location-recognition, and to verify the effectiveness of this system. This study built a full-scale test bed to evaluate the performance of the developed technology, and derived the energy reduction rate and indoor light environment improvement rate to evaluate the performance of dimming lighting control (Case 1), dimming lighting control using location-awareness technology (Case 2), and dimming lighting control using general illumination and location-awareness technology (Case 3). The conclusions are as follows. (1) Case 3 reduces lighting energy by 47.9–64.2% as compared to Case 1, and Case 3 reduces lighting energy compared to Case 2 when there are three occupants. (2) Case 3 improves indoor light environment comfort by increasing the uniformity by 17.8–49% compared to Case 2. These results confirm the effectiveness of Case 3 proposed in this study.

Dis-placing place-making: how African-American and immigrant youth realize their rights to the city

ABSTRACTNotions of place-making assume that individuals and groups of people have legitimate ‘rights to the city.’ This paper unsettles these notions to incorporate the politically and legally tenuous relationships African-American and Immigrant youth have to their cities. We describe a community-based digital STEAM curriculum called Mobile City Science that invited youth to engage in place-making efforts using mobile and location-aware technologies. The design study relied on a contradiction that is fundamental to youth place-making in an era of white nationalism: for African-American and Immigrant youth to engage power structures in community development processes, they had to engage in a series of dis-placements that removed them from embodied experiences and in-location perceptions of their communities. Self-censoring, witnessing, historicizing, and re-veiwing were all examples of dis-placements youth enacted to speak truth to power with digital and mobile tools.

A NEW HIERARCHICAL CLUSTERING APPROACH FOR SPARSE MOBILE PHONE TRAJECTORIES

Abstract. Understanding the pattern of human activities benefits both the living service providing for the public and the policy-making for urban planners. The development of location-aware technology enables us to acquire large volume individual trajectories with different spatial and temporal resolution, such as GPS trajectories, mobile phone positioning data, social media check-in data, Wifi, and Bluetooth. However, the highest population penetrated mobile phone positioning trajectories are hard to infer human activity pattern directly, because of the sparsity in both space and time. This article presents a hierarchical clustering approach by using the move and stay sequences inferred from spare mobile phone trajectories to uncover the hidden human activity pattern. Personal stays at some places and following moves are first extracted from mobile phone trajectories, considering the spatial uncertainty of position. The similarity of trajectories is measured with a new indicator defined by the area of a spatial-temporal polygon bound with normalized trajectories. Finally, a hierarchical clustering method is developed to group trajectories with similar stay-move chains from the bottom to the top. The obtained clusters are analyzed to identify human activity patterns. An experiment with mobile phone users’ one-day trajectories in Shenzhen, China was conducted to test the performance of the proposed clustering approach. The results indicate all used trajectories are classified into 10 clusters representing typical daily activity patterns from the simple home-staying mode to complex home-working-social activity daily cycles. This study not only unravels the hidden activity patterns behind massive sparse trajectories but also deepens our understanding of the interaction of human activity and urban space.

Key Technologies of Wireless Sensor Networks: A Review

Wireless sensor network is an emerging technology developing rapidly in the last ten years. Two key technologies of WSN, location-awareness and data fusion are mainly reviewed in this paper. Firstly, the main methods, technical difficulties and future trends of location aware technology are introduced in detail. In addition, the data fusion level and its main algorithms along with advantages and disadvantages are expounded, and the future development of the data fusion technology is prospected.

Quantifying multi-dimensional attributes of human activities at various geographic scales based on smartphone tracking

BackgroundAdvancement in location-aware technologies, and information and communication technology in the past decades has furthered our knowledge of the interaction between human activities and the built environment. An increasing number of studies have collected data regarding individual activities to better understand how the environment shapes human behavior. Despite this growing interest, some challenges exist in collecting and processing individual’s activity data, e.g., capturing people’s precise environmental contexts and analyzing data at multiple spatial scales.MethodsIn this study, we propose and implement an innovative system that integrates smartphone-based step tracking with an app and the sequential tile scan techniques to collect and process activity data. We apply the OpenStreetMap tile system to aggregate positioning points at various scales. We also propose duration, step and probability surfaces to quantify the multi-dimensional attributes of activities.ResultsResults show that, by running the app in the background, smartphones can measure multi-dimensional attributes of human activities, including space, duration, step, and location uncertainty at various spatial scales. By coordinating Global Positioning System (GPS) sensor with accelerometer sensor, this app can save battery which otherwise would be drained by GPS sensor quickly. Based on a test dataset, we were able to detect the recreational center and sports center as the space where the user was most active, among other places visited.ConclusionThe methods provide techniques to address key issues in analyzing human activity data. The system can support future studies on behavioral and health consequences related to individual’s environmental exposure.

Surechigai sociality: location-aware technology on the Yamanote line

Every day on Tokyo's Yamanote line, over a million people come face to face. For some, this can be a source of anxiety and discomfort, because they are expected to obey the social etiquette of maintaining one's distance from strangers, despite the train cars being filled beyond capacity. To make the train ride bearable, passengers have adopted a wide range of practices aimed at creating and maintaining personal space, and these practices increasingly involve the use of technology. This article focuses on surechigai tsūshin, a location-aware technology installed in Nintendo's portable gaming devices, which allows users to engage in quasi-anonymous encounters with digital strangers while ‘passing by’ on the street or on the train. Such technologies and encounters show how train cars serve as spaces where estrangement is negotiated and mediated in Japan today. Surechigai tsūshin offers a means to control how one comes to know others, acting as a digitized form of civil inattention. The sociality of surechigai is maintained as smooth ‘passing by’ among strangers who are virtually and actually co-present, familiar and distant faces on the train. The volume of human traffic on the Yamanote line compounds the social effects of surechigai tsūshin, showing how a variety of technologies articulate with one another to create distinct forms of sociality. In this sense, the significance of the Yamanote line rests not only in its characteristic ‘intimate alienation’ (Fujii 1999), but also in the sociocultural negotiation of technologies that mediate experience on the urban railway.

A Hybrid Approach Combining the Multi-Temporal Scale Spatio-Temporal Network with the Continuous Triangular Model for Exploring Dynamic Interactions in Movement Data: A Case Study of Football

Benefiting from recent advantages in location-aware technologies, movement data are becoming ubiquitous. Hence, numerous research topics with respect to movement data have been undertaken. Yet, the research of dynamic interactions in movement data is still in its infancy. In this paper, we propose a hybrid approach combining the multi-temporal scale spatio-temporal network (MTSSTN) and the continuous triangular model (CTM) for exploring dynamic interactions in movement data. The approach mainly includes four steps: first, the relative trajectory calculus (RTC) is used to derive three types of interaction patterns; second, for each interaction pattern, a corresponding MTSSTN is generated; third, for each MTSSTN, the interaction intensity measures and three centrality measures (i.e., degree, betweenness and closeness) are calculated; finally, the results are visualized at multiple temporal scales using the CTM and analyzed based on the generated CTM diagrams. Based on the proposed approach, three distinctive aims can be achieved for each interaction pattern at multiple temporal scales: (1) exploring the interaction intensities between any two individuals; (2) exploring the interaction intensities among multiple individuals, and (3) exploring the importance of each individual and identifying the most important individuals. The movement data obtained from a real football match are used as a case study to validate the effectiveness of the proposed approach. The results demonstrate that the proposed approach is useful in exploring dynamic interactions in football movement data and discovering insightful information.

Poređenje korišćenja različitih vrsta podataka o saobraćaju u analizi najboljeg puta primenom geografskog informacionog sistema

Geographic information systems (GIS) represent a sophisticated tool in transportation network analysis. It is very important how transportation network is modelled regarding traffic data. Usually, fixed travel speed data is used, but due to advancements in information and communication technologies (ICTs) and location-aware technologies (LATs), it is now possible to collect large amounts of mobility and activity data in real-time. The aim of the paper is to make a comparison between different types of best route analysis regarding different travel speed information: fixed travel speed, historical traffic, and live traffic. This finding would not be important only for best route analysis but also for other types of network analyses. First, we distinguish between different kinds of travel speed information, then we present our practical work of creating three different types of network models according to different types of traffic speed data, and finally we perform best route analysis along the above models and compare the results.

Spatial technology and social media in remote sensing: challenges and opportunities [point of view

The convergence of remote sensing technologies with social media, coupled with advances in other location-aware technologies such as WiFi and smartphones, is moving us on a fast track to a situation in which we can readily know, at any time, where everybody and everything are located on the surface of the Earth, and to exploit the power of social media in different contexts. Remote sensing technology involves the use of systems and algorithms to record information about the surface of the Earth from a remote location [1] . Although reliable as a data source, remote sensing data may not always be available. However, these data can be complemented by other sources of data, such as geographic information systems (GIS) and social media [2] , in order to address time-critical applications. For instance, relating publicly available social media information with remote sensing or GIS data can lead to a more efficient management of emergency response (which refers to applications in which real/near-real-time response is needed, such as natural disasters). Social media are now playing a more relevant role in our daily lives and provide a unique opportunity to gain valuable insight on information flow and social networking within the society. As a result, the integration of social media data with other consolidated technologies such as remote sensing or GIS is of great importance.

Post-processing GPS-tracks in reconstructing travelled routes in a GIS-environment: network subset selection and attribute adjustment

ABSTRACTThe exact distance and routes travelled on an individual level are essential variables in determining the effectiveness of ‘soft’ transport demand management strategies. The ability to track individuals in great spatial detail by means of Location Aware Technologies such as GPS has opened avenues for gathering these data with great precision. Route reconstruction with positional data is typically done by a process referred to as map-matching; however, despite the large number of real-time map-matching algorithms developed, few studies have developed a post-processing map-matching algorithm in a Geographic Information Systems (GIS)-environment. This paper presents two GIS-based map-matching methods that predominantly use a digital road network with speed and directionality attributes for route reconstruction of raw GPS-trajectories. The methodologies were tested for a data set in which actual routes travelled were known. Both explored procedures, the ‘Connected Subset’ assignment procedure based on network subset selection and the ‘Impedance Reduction’ assignment procedure based on attribute adjustment, provide accurate results. In addition, both procedures effectively deal with commonly GPS-induced problems such as measurement gaps and positional drift.

Modelling Electronic Sharing (E-Sharing) Behaviour: A Perspective on Location-aware Technology

Modelling Electronic Sharing (E-Sharing) Behaviour: A Perspective on Location-aware Technology

Pokémon GO: Mobile media play, place-making, and the digital wayfarer

This special commentary for Mobile Media & Communication seeks to put these divisive debates in context. Through the lens of Pokémon GO, we can understand and critically interpret a variety of issues involved in the politics and practice of playful mobile media. These issues move across debates around location-aware technologies in constructions of privacy (Coldewey, 2016; Cunningham, 2016), risk and surveillance (Machkovech, 2016; Mishra, 2016) to the role of mobile media in commodifying (Evangelho, 2016) and expanding the social, cultural, and creative dimensions of play (Isbister, 2016; Mäyrä, 2012). As the mobile media and game theorists in this commentary highlight, the game sits at the nexus of several technological and cultural trajectories: the playful turn; the ubiquity of location-based and haptic mobile media (and apps and games); innovative game design; the effects of digital mapping technologies; the intertwining of performative media games and art; our individual and collective memories of playworlds and transmedia universes; the increasing importance of issues concerning privacy and risk in public spaces; the ongoing augmentation of place and space; and the politics embedded in this hybrid experience of the lifeworld.

Big Data for Healthy Cities: Using Location-Aware Technologies, Open Data and 3D Urban Models to Design Healthier Built Environments

Big Data for Healthy Cities: Using Location-Aware Technologies, Open Data and 3D Urban Models to Design Healthier Built Environments

Understanding Spatiotemporal Patterns of Human Convergence and Divergence Using Mobile Phone Location Data

Investigating human mobility patterns can help researchers and agencies understand the driving forces of human movement, with potential benefits for urban planning and traffic management. Recent advances in location-aware technologies have provided many new data sources (e.g., mobile phone and social media data) for studying human space-time behavioral regularity. Although existing studies have utilized these new datasets to characterize human mobility patterns from various aspects, such as predicting human mobility and monitoring urban dynamics, few studies have focused on human convergence and divergence patterns within a city. This study aims to explore human spatial convergence and divergence and their evolutions over time using large-scale mobile phone location data. Using a dataset from Shenzhen, China, we developed a method to identify spatiotemporal patterns of human convergence and divergence. Eight distinct patterns were extracted, and the spatial distributions of these patterns are discussed in the context of urban functional regions. Thus, this study investigates urban human convergence and divergence patterns and their relationships with the urban functional environment, which is helpful for urban policy development, urban planning and traffic management.

Study on movable light-shelf system with location-awareness technology for lighting energy saving

Various ongoing studies regard light-shelves as one solution to the recent increase in lighting energy consumption. However, in previous light-shelf systems, the direction of incoming light was determined by external conditions, thereby limiting the efficiency of lighting energy saving. The purpose of the present study was to develop a movable light-shelf system with location-awareness technology and verify its performance. In this study, a full-scale testbed was established in order to test the proposed movable light-shelf system with location awareness as well as to verify its energy saving potential. The results were analysed and compared with the performances of previous fixed (Case 1) and movable (Case 2) light-shelf systems without location-awareness technology. The obtained results were as follows. (1) The proposed light-shelf system can respond to external conditions and to the location of the occupant by means of the control axis of the light-shelf module angle through modulation between the control axis of the angle of the previous light-shelf and the reflector of the light-shelf. (2) The proposed light-shelf system provides 90.0% and 86.6%/91.0% energy savings in comparison to Case 1 and Case 2, respectively.

Location-aware device communication design: exploration and exploitation on energy

Cooperation among devices is an important part of green communication. As one of the core techniques of 5G technology, device-to-device (D2D) technology is able to improve spectrum efficiency and take advantage of the neighborhood information. While most of the existing work focuses on D2D pairs, clustering technology can bring benefits, especially in energy efficiency. In this article we focus on the benefits of energy saving when location-aware devices cooperate. More specifically, the green communication of location-aware devices is discussed from two aspects: the exploration of energy and the exploitation of energy. The exploration of energy refers to harvesting energy from transmitted signals, providing a dependable energy source. The exploitation of energy, on the other hand, refers to an efficient tactic to use the accumulated energy based on location-aware clustering technology. Combining the two technologies, communication among location-aware devices is coordinated to be energy efficient and highly sustainable.

Another Tale of Two Cities: Understanding Human Activity Space Using Actively Tracked Cellphone Location Data

Activity space is an important concept in geography. Recent advancements of location-aware technologies have generated many useful spatiotemporal data sets for studying human activity space for large populations. In this article, we use two actively tracked cellphone location data sets that cover a weekday to characterize people's use of space in Shanghai and Shenzhen, China. We introduce three mobility indicators (daily activity range, number of activity anchor points, and frequency of movements) to represent the major determinants of individual activity space. By applying association rules in data mining, we analyze how these indicators of an individual's activity space can be combined with each other to gain insights of mobility patterns in these two cities. We further examine spatiotemporal variations of aggregate mobility patterns in these two cities. Our results reveal some distinctive characteristics of human activity space in these two cities: (1) A high percentage of people in Shenzhen have a relatively short daily activity range, whereas people in Shanghai exhibit a variety of daily activity ranges; (2) people with more than one activity anchor point tend to travel further but less frequently in Shanghai than in Shenzhen; (3) Shenzhen shows a significant north–south contrast of activity space that reflects its urban structure; and (4) travel distance in both cities is shorter around noon than in regular work hours, and a large percentage of movements around noon are associated with individual home locations. This study indicates the benefits of analyzing actively tracked cellphone location data for gaining insights of human activity space in different cities.

A Density-Based Spatial Flow Cluster Detection Method

GIScience 2016 Short Paper Proceedings A Density-Based Spatial Flow Cluster Detection Method Ran Tao 1 , Jean-Claude Thill 1 Dept. of Geography & Earth Sciences, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC Email:{ rtao2; jfthill}@uncc.edu Abstract Understanding the patterns and dynamics of spatial origin-destination flow data has been a long- standing goal of spatial scientists. In this paper we introduce a density-based cluster detection method tailored for disaggregated spatial flow data. The basic idea is to first measure flow density considering both endpoint coordinates and flow lengths, and combine it with state-of-art density-based clustering methods. We experiment with a carefully designed synthetic dataset. The results prove that our method can effectively extract flow clusters from various situations encompassing varied flow densities, lengths, hierarchies and, at the same time, avoid issues of Modifiable Areal Unit Problem (MAUP) of flows endpoints, loss of spatial information, and false positive errors on short flows. 1. Introduction Spatial flows, also known as spatial interactions (SI) between georeferenced places, have been an enduring study object in a wide range of research fields. With the widespread adoption of location-aware technologies and the global diffusion of geographic information systems (GIS), spatial interaction data have been enriched in several respects including volume, type, availability, ubiquity, and spatiotemporal granularity (Yan and Thill 2009; Guo et al. 2012). While it brings unprecedented opportunities to improve our understanding of SI processes and thus enriching SI theories, it also brings the analytical challenge of developing more data-drive approaches tailored for SI data (Yan and Thill 2009). As a common data mining technique, cluster detection has proved useful in exploratory analysis of large sets of spatial flows. One approach measures the spatial relationships among origins and destinations, respectively, before combining them, as the basis for clustering flows. Here, spatial relationships can be contiguity or proximity of origin or destination regions (Guo 2009; Zhu and Guo 2014). However these methods are sensitive to uneven distribution and ad hoc zoning definition of flow endpoints; besides they are prone to false positive errors on short- distance interactions. Another type of methods use flow geometry to bundle nearby ones (Cui et al. 2008). While the results usually have desirable visual clarity, these methods compromise through loss of valuable spatial information. In this paper we introduce a new method that not only can extract spatial flow clusters from various situations including varying flow densities, lengths, hierarchies, but also avoids problems like MAUP, false positive errors, and loss of information. 2. Methodology Of various clustering methods, we choose to design our flow clustering method in the density- based tradition because of its capability to discover clusters of arbitrary shape and to filter out noise. Moreover, density-based methods like OPTICS (Ankerst et al. 1999) can effectively reveal hierarchical structures in the data since its byproduct, the reachability plot, is convertible to a dendrogram (Sander et al. 2003; Campello et al. 2013). Hereafter, we first introduce the proximity metric tailored to spatial flows; then we explain the clustering method step by step.

Developing a Workflow to Identify Inconsistencies in Volunteered Geographic Information: A Phenological Case Study.

Recent improvements in online information communication and mobile location-aware technologies have led to the production of large volumes of volunteered geographic information. Widespread, large-scale efforts by volunteers to collect data can inform and drive scientific advances in diverse fields, including ecology and climatology. Traditional workflows to check the quality of such volunteered information can be costly and time consuming as they heavily rely on human interventions. However, identifying factors that can influence data quality, such as inconsistency, is crucial when these data are used in modeling and decision-making frameworks. Recently developed workflows use simple statistical approaches that assume that the majority of the information is consistent. However, this assumption is not generalizable, and ignores underlying geographic and environmental contextual variability that may explain apparent inconsistencies. Here we describe an automated workflow to check inconsistency based on the availability of contextual environmental information for sampling locations. The workflow consists of three steps: (1) dimensionality reduction to facilitate further analysis and interpretation of results, (2) model-based clustering to group observations according to their contextual conditions, and (3) identification of inconsistent observations within each cluster. The workflow was applied to volunteered observations of flowering in common and cloned lilac plants (Syringa vulgaris and Syringa x chinensis) in the United States for the period 1980 to 2013. About 97% of the observations for both common and cloned lilacs were flagged as consistent, indicating that volunteers provided reliable information for this case study. Relative to the original dataset, the exclusion of inconsistent observations changed the apparent rate of change in lilac bloom dates by two days per decade, indicating the importance of inconsistency checking as a key step in data quality assessment for volunteered geographic information. Initiatives that leverage volunteered geographic information can adapt this workflow to improve the quality of their datasets and the robustness of their scientific analyses.

Mining spatiotemporal patterns of urban dwellers from taxi trajectory data

With the widespread adoption of locationaware technology, obtaining long-sequence, massive and high-accuracy spatiotemporal trajectory data of individuals has become increasingly popular in various geographic studies. Trajectory data of taxis, one of the most widely used inner-city travel modes, contain rich information about both road network traffic and travel behavior of passengers. Such data can be used to study the microscopic activity patterns of individuals as well as the macro system of urban spatial structures. This paper focuses on trajectories obtained from GPS-enabled taxis and their applications for mining urban commuting patterns. A novel approach is proposed to discover spatiotemporal patterns of household travel from the taxi trajectory dataset with a large number of point locations. The approach involves three critical steps: spatial clustering of taxi origin-destination (OD) based on urban traffic grids to discover potentially meaningful places, identifying threshold values from statistics of the OD clusters to extract urban jobs-housing structures, and visualization of analytic results to understand the spatial distribution and temporal trends of the revealed urban structures and implied household commuting behavior. A case study with a taxi trajectory dataset in Shanghai, China is presented to demonstrate and evaluate the proposed method.