ISPRS International Journal of Geo-Information

Editorial

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7 pages, 209 KiB

Open AccessEditorial

Current Trends and Challenges in Location-Based Services

by Haosheng Huang and Georg Gartner

ISPRS Int. J. Geo-Inf. 2018, 7(6), 199; https://doi.org/10.3390/ijgi7060199 - 28 May 2018

Cited by 37 | Viewed by 10554

Abstract

Location-based services (LBS) are a growing area of research. This editorial paper introduces the key research areas within the scientific field of LBS, which consist of positioning, modelling, communication, applications, evaluation, analysis of LBS data, and privacy and ethical issues. After that, 18 [...] Read more.

Location-based services (LBS) are a growing area of research. This editorial paper introduces the key research areas within the scientific field of LBS, which consist of positioning, modelling, communication, applications, evaluation, analysis of LBS data, and privacy and ethical issues. After that, 18 original papers are presented, which provide a general picture of recent research activities on LBS, especially related to the research areas of positioning, modelling, applications, and LBS data analysis. This Special Issue together with other recent events and publications concerning LBS show that the scientific field of LBS is rapidly evolving, and that LBS applications have become smarter and more ubiquitous in many aspects of our daily life. Full article

(This article belongs to the Special Issue Location-Based Services)

Research

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7118 KiB

Open AccessArticle

Ranking the City: The Role of Location-Based Social Media Check-Ins in Collective Human Mobility Prediction

by Omid Reza Abbasi, Ali Asghar Alesheikh and Mohammad Sharif

ISPRS Int. J. Geo-Inf. 2017, 6(5), 136; https://doi.org/10.3390/ijgi6050136 - 28 Apr 2017

Cited by 19 | Viewed by 6291

Abstract

Technological advances have led to an increasing development of data sources. Since the introduction of social networks, numerous studies on the relationships between users and their behaviors have been conducted. In this context, trip behavior is an interesting topic that can be explored [...] Read more.

Technological advances have led to an increasing development of data sources. Since the introduction of social networks, numerous studies on the relationships between users and their behaviors have been conducted. In this context, trip behavior is an interesting topic that can be explored via Location-Based Social Networks (LBSN). Due to the wide availability of various spatial data sources, the long-standing field of collective human mobility prediction has been revived and new models have been introduced. Recently, a parameterized model of predicting human mobility in cities, known as rank-based model, has been introduced. The model predicts the flow from an origin toward a destination using “rank” concept. However, the notion of rank has not yet been well explored. In this study, we investigate the potential of LBSN data alongside the rank concept in predicting human mobility patterns in Manhattan, New York City. For this purpose, we propose three scenarios, including: rank-distance, the number of venues between origin and destination, and a check-in weighted venue schema to compute the ranks. When trip distribution patterns are considered as a whole, applying a check-in weighting schema results in patterns that are approximately 10 percent more similar to the ground truth data. From the accuracy perspective, as the predicted numbers of trips are closer to real number of trips, the trip distribution is also enhanced by about 50 percent. Full article

(This article belongs to the Special Issue Location-Based Services)

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2496 KiB

Open AccessArticle

Salience Indicators for Landmark Extraction at Large Spatial Scales Based on Spatial Analysis Methods

by Min Weng, Qin Xiong and Mengjun Kang

ISPRS Int. J. Geo-Inf. 2017, 6(3), 72; https://doi.org/10.3390/ijgi6030072 - 4 Mar 2017

Cited by 10 | Viewed by 6023

Abstract

Urban landmarks are frequently used in way-finding and representations of spatial knowledge. However, assessing the salience of urban landmarks is difficult. Moreover, no method exists to rapidly extract urban landmarks from basic geographic information databases. The goal of this paper is to solve [...] Read more.

Urban landmarks are frequently used in way-finding and representations of spatial knowledge. However, assessing the salience of urban landmarks is difficult. Moreover, no method exists to rapidly extract urban landmarks from basic geographic information databases. The goal of this paper is to solve these problems from the dual aspects of spatial knowledge representation and public spatial cognition rules. A clear and systematic definition for multiple-scale urban landmarks is proposed, together with a category reference for extracting small- and medium-scale urban landmarks and a model for the large-scale automatic extraction of urban landmarks. In this large-scale automatic urban landmark extraction model, the salience is expressed by two weighted parameters: the check-in totals and local accessibility. The extraction threshold is set according to a predefined number of landmarks to be extracted. Experiments show that the extraction results match the reference data well. Full article

(This article belongs to the Special Issue Location-Based Services)

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Open AccessArticle

Towards a Landmark-Based Pedestrian Navigation Service Using OSM Data

by Adam Rousell and Alexander Zipf

ISPRS Int. J. Geo-Inf. 2017, 6(3), 64; https://doi.org/10.3390/ijgi6030064 - 25 Feb 2017

Cited by 33 | Viewed by 11708

Abstract

With the advent of location-aware smartphones, the desire for pedestrian-based navigation services has increased. Unlike car-based services where instructions generally are comprised of distance and road names, pedestrian instructions should instead focus on the delivery of landmarks to aid in navigation. OpenStreetMap (OSM) [...] Read more.

With the advent of location-aware smartphones, the desire for pedestrian-based navigation services has increased. Unlike car-based services where instructions generally are comprised of distance and road names, pedestrian instructions should instead focus on the delivery of landmarks to aid in navigation. OpenStreetMap (OSM) contains a vast amount of geospatial information that can be tapped into for identifying these landmark features. This paper presents a prototype navigation service that extracts landmarks suitable for navigation instructions from the OSM dataset based on several metrics. This is coupled with a short comparison of landmark availability within OSM, differences in routes between locations with different levels of OSM completeness and a short evaluation of the suitability of the landmarks provided by the prototype. Landmark extraction is performed on a server-side service, with the instructions being delivered to a pedestrian navigation application running on an Android mobile device. Full article

(This article belongs to the Special Issue Location-Based Services)

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Open AccessArticle

An Exploratory Study Investigating Gender Effects on Using 3D Maps for Spatial Orientation in Wayfinding

by Hua Liao and Weihua Dong

ISPRS Int. J. Geo-Inf. 2017, 6(3), 60; https://doi.org/10.3390/ijgi6030060 - 24 Feb 2017

Cited by 41 | Viewed by 9161

Abstract

3D representations in applications that provide self-localization and orientation in wayfinding have become increasingly popular in recent years because of technical advances in the field. However, human factors have been largely ignored while designing 3D representations in support of pedestrian navigation. This exploratory [...] Read more.

3D representations in applications that provide self-localization and orientation in wayfinding have become increasingly popular in recent years because of technical advances in the field. However, human factors have been largely ignored while designing 3D representations in support of pedestrian navigation. This exploratory study aims to explore gender effects on using 3D maps for spatial orientation. We designed a 3D map that combines salient 3D landmarks and 2D layouts, and evaluated gender differences in their performance during direction-pointing tasks by administrating an eye tracking experiment. The results indicate there was no significant overall gender difference on performance and visual attention. However, we observed that males using the 3D map paid more attention to landmarks in the environment and performed better than when using the conventional 2D map, whereas female performance did not show any significant difference between the two types of map usage. We also observed contrary gender differences in visual attention on landmarks between the 3D and 2D maps. While males fixated longer on landmarks than females when using the 3D map, females paid more visual attention to landmarks than males when using the 2D map. In addition, verbal protocols showed that males had more confidence while make decisions. These empirical results can be helpful in the design of map-based wayfinding enhancement tools. Full article

(This article belongs to the Special Issue Location-Based Services)

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Open AccessArticle

A Line Graph-Based Continuous Range Query Method for Moving Objects in Networks

by Hengcai Zhang, Feng Lu and Jie Chen

ISPRS Int. J. Geo-Inf. 2016, 5(12), 246; https://doi.org/10.3390/ijgi5120246 - 19 Dec 2016

Cited by 5 | Viewed by 4464

Abstract

The rapid growth of location-based services has motivated the development of continuous range queries in networks. Existing query algorithms usually adopt an expansion tree to reuse the previous query results to get better efficiency. However, the high maintenance costs of the traditional expansion [...] Read more.

The rapid growth of location-based services has motivated the development of continuous range queries in networks. Existing query algorithms usually adopt an expansion tree to reuse the previous query results to get better efficiency. However, the high maintenance costs of the traditional expansion tree lead to a sharp efficiency decrease. In this paper, we propose a line graph-based continuous range (LGCR) query algorithm for moving objects in networks, which is characterized by a novel graph-based expansion tree (GET) structure used to monitor queries in an incremental manner. In particular, GET is developed based on the line graph model of networks and simultaneously supports offline pre-computation to better adapt our proposed algorithm to different sizes of networks. To improve performance, we create a series of related data structures, such as bridgeable edges and distance edges. Correspondingly, we develop several algorithms, including initialization, insertion of objects, filter and refinement and location update, to incrementally re-evaluate continuous range queries. Finally, we implement the GET and related algorithms in the native graph database Neo4J. We conduct experiments using real-world networks and simulated moving objects and compare the proposed LGCR with the existing classical algorithm to verify its effectiveness and demonstrate its greater efficiency. Full article

(This article belongs to the Special Issue Location-Based Services)

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Open AccessArticle

Interest Aware Location-Based Recommender System Using Geo-Tagged Social Media

by Basma AlBanna, Mahmoud Sakr, Sherin Moussa and Ibrahim Moawad

ISPRS Int. J. Geo-Inf. 2016, 5(12), 245; https://doi.org/10.3390/ijgi5120245 - 19 Dec 2016

Cited by 19 | Viewed by 6116

Abstract

Advances in location acquisition and mobile technologies led to the addition of the location dimension to Social Networks (SNs) and to the emergence of a newer class called Location-Based Social Networks (LBSNs). While LBSNs are richer in their model and functions than SNs, [...] Read more.

Advances in location acquisition and mobile technologies led to the addition of the location dimension to Social Networks (SNs) and to the emergence of a newer class called Location-Based Social Networks (LBSNs). While LBSNs are richer in their model and functions than SNs, they fail so far to attract as many users as SNs. On the other hand, SNs have large amounts of geo-tagged media that are under-utilized. In this paper, we propose an Interest-Aware Location-Based Recommender system (IALBR), which combines the advantages of both LBSNs and SNs, in order to provide interest-aware location-based recommendations. This recommender system is proposed as an extension to LBSNs. It is novel in: (1) utilizing the geo-content in both LBSNs and SNs; (2) ranking the recommendations based on a novel scoring method that maps to the user interests. It also works for passive users who are not active content contributors to the LBSN. This feature is critical to increase the number of LBSN users. Moreover, it helps with reducing the cold start problem, which is a common problem facing the new users of recommender systems who get random unsatisfying recommendations. This is due to the lack of user interest awareness, which is reliant on user history in most of the recommenders. We evaluated our system with a large-scale real dataset collected from foursquare with respect to precision, recall and the f-measure. We also compared the results with a ground truth system using metrics like the normalized discounted cumulative gain and the mean absolute error. The results confirm that the proposed IALBR generates more efficient recommendations than baselines in terms of interest awareness. Full article

(This article belongs to the Special Issue Location-Based Services)

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Open AccessArticle

An Improved WiFi/PDR Integrated System Using an Adaptive and Robust Filter for Indoor Localization

by Zengke Li, Chunyan Liu, Jingxiang Gao and Xin Li

ISPRS Int. J. Geo-Inf. 2016, 5(12), 224; https://doi.org/10.3390/ijgi5120224 - 30 Nov 2016

Cited by 33 | Viewed by 5775

Abstract

Location-based services (LBS) are services offered through a mobile device that take into account a device’s geographical location. To provide position information for these services, location is a key process. GNSS (Global Navigation Satellite System) can provide sub-meter accuracy in open-sky areas using [...] Read more.

Location-based services (LBS) are services offered through a mobile device that take into account a device’s geographical location. To provide position information for these services, location is a key process. GNSS (Global Navigation Satellite System) can provide sub-meter accuracy in open-sky areas using satellite signals. However, for indoor and dense urban environments, the accuracy deteriorates significantly because of weak signals and dense multipaths. The situation becomes worse in indoor environments where the GNSS signals are unreliable or totally blocked. To improve the accuracy of indoor positioning for location-based services, an improved WiFi/Pedestrian Dead Reckoning (PDR) integrated positioning and navigation system using an adaptive and robust filter is presented. The adaptive filter is based on scenario and motion state recognition and the robust filter is based on the Mahalanobis distance. They are combined and used in the WiFi/PDR integrated system to weaken the effect of gross errors on the dynamic and observation models. To validate their performance in the WiFi/PDR integrated system, a real indoor localization experiment is conducted. The results indicate that the adaptive filter is better able to adapt to the circumstances of the dynamic model by adjusting the covariance of the process noise and the robust Kalman filter is able to mitigate the harmful effect of gross errors from the WiFi positioning. Full article

(This article belongs to the Special Issue Location-Based Services)

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Open AccessArticle

Hidden Naive Bayes Indoor Fingerprinting Localization Based on Best-Discriminating AP Selection

by Chunjing Song, Jian Wang and Guan Yuan

ISPRS Int. J. Geo-Inf. 2016, 5(10), 189; https://doi.org/10.3390/ijgi5100189 - 10 Oct 2016

Cited by 24 | Viewed by 5923

Abstract

Indoor fingerprinting localization approaches estimate the location of a mobile object by matching observations of received signal strengths (RSS) from access points (APs) with fingerprint records. In real WLAN environments, there are more and more APs available, with interference between them, which increases [...] Read more.

Indoor fingerprinting localization approaches estimate the location of a mobile object by matching observations of received signal strengths (RSS) from access points (APs) with fingerprint records. In real WLAN environments, there are more and more APs available, with interference between them, which increases the localization difficulty and computational consumption. To cope with this, a novel AP selection method, LocalReliefF-C( a novel method based on ReliefF and correlation coefficient), is proposed. Firstly, on each reference location, the positioning capability of APs is ranked by calculating classification weights. Then, redundant APs are removed via computing the correlations between APs. Finally, the set of best-discriminating APs of each reference location is obtained, which will be used as the input features when the location is estimated. Furthermore, an effective clustering method is adopted to group locations into clusters according to the common subsets of the best-discriminating APs of these locations. In the online stage, firstly, the sequence of RSS observations is collected to calculate the set of the best-discriminating APs on the given location, which is subsequently used to compare with cluster keys in order to determine the target cluster. Then, hidden naive Bayes (HNB) is introduced to estimate the target location, which depicts the real WLAN environment more accurately and takes into account the mutual interaction of the APs. The experiments are conducted in the School of Environmental Science and Spatial Informatics at the China University of Mining and Technology. The results validate the effectiveness of the proposed methods on improving localization accuracy and reducing the computational consumption. Full article

(This article belongs to the Special Issue Location-Based Services)

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Open AccessArticle

Top-k Spatial Preference Queries in Directed Road Networks

by Muhammad Attique, Hyung-Ju Cho, Rize Jin and Tae-Sun Chung

ISPRS Int. J. Geo-Inf. 2016, 5(10), 170; https://doi.org/10.3390/ijgi5100170 - 23 Sep 2016

Cited by 9 | Viewed by 4789

Abstract

Top-k spatial preference queries rank objects based on the score of feature objects in their spatial neighborhood. Top-k preference queries are crucial for a wide range of location based services such as hotel browsing and apartment searching. In recent years, a [...] Read more.

Top-k spatial preference queries rank objects based on the score of feature objects in their spatial neighborhood. Top-k preference queries are crucial for a wide range of location based services such as hotel browsing and apartment searching. In recent years, a lot of research has been conducted on processing of top-k spatial preference queries in Euclidean space. While few algorithms study top-k preference queries in road networks, they all focus on undirected road networks. In this paper, we investigate the problem of processing the top-k spatial preference queries in directed road networks where each road segment has a particular orientation. Computation of data object scores requires examining the scores of each feature object in its spatial neighborhood. This may cause the computational delay, thus resulting in a high query processing time. In this paper, we address this problem by proposing a pruning and grouping of feature objects to reduce the number of feature objects. Furthermore, we present an efficient algorithm called TOPS that can process top-k spatial preference queries in directed road networks. Experimental results indicate that our algorithm significantly reduces the query processing time compared to period solution for a wide range of problem settings. Full article

(This article belongs to the Special Issue Location-Based Services)

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Open AccessArticle

Method for Determining Appropriate Clustering Criteria of Location-Sensing Data

by Youngmin Lee, Pil Kwon, Kiyun Yu and Woojin Park

ISPRS Int. J. Geo-Inf. 2016, 5(9), 151; https://doi.org/10.3390/ijgi5090151 - 25 Aug 2016

Cited by 8 | Viewed by 5252

Abstract

Large quantities of location-sensing data are generated from location-based social network services. These data are provided as point properties with location coordinates acquired from a global positioning system or Wi-Fi signal. To show the point data on multi-scale map services, the data should [...] Read more.

Large quantities of location-sensing data are generated from location-based social network services. These data are provided as point properties with location coordinates acquired from a global positioning system or Wi-Fi signal. To show the point data on multi-scale map services, the data should be represented by clusters following a grid-based clustering method, in which an appropriate grid size should be determined. Currently, there are no criteria for determining the proper grid size, and the modifiable areal unit problem has been formulated for the purpose of addressing this issue. The method proposed in this paper is applies a hexagonal grid to geotagged Twitter point data, considering the grid size in terms of both quantity and quality to minimize the limitations associated with the modifiable areal unit problem. Quantitatively, we reduced the original Twitter point data by an appropriate amount using Töpfer’s radical law. Qualitatively, we maintained the original distribution characteristics using Moran’s I. Finally, we determined the appropriate sizes of clusters from zoom levels 9–13 by analyzing the distribution of data on the graphs. Based on the visualized clustering results, we confirm that the original distribution pattern is effectively maintained using the proposed method. Full article

(This article belongs to the Special Issue Location-Based Services)

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Open AccessArticle

Road Map Inference: A Segmentation and Grouping Framework

by Jia Qiu and Ruisheng Wang

ISPRS Int. J. Geo-Inf. 2016, 5(8), 130; https://doi.org/10.3390/ijgi5080130 - 23 Jul 2016

Cited by 16 | Viewed by 6891

Abstract

We propose a new segmentation and grouping framework for road map inference from GPS traces. We first present a progressive Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm with an orientation constraint to partition the whole point set of the traces into [...] Read more.

We propose a new segmentation and grouping framework for road map inference from GPS traces. We first present a progressive Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm with an orientation constraint to partition the whole point set of the traces into clusters that represent road segments. A new point cluster grouping algorithm, according to the topological relationship and spatial proximity of the point clusters to recover the road network, is then developed. After generating the point clusters, the robust Locally-Weighted Scatterplot Smooth (Lowess) method is used to extract their centerlines. We then propose to build the topological relationship of the centerlines by a Hidden Markov Model (HMM)-based map matching algorithm; and to assess whether the spatial proximity between point clusters by assuming the distances from the points to the centerline comply with a Gaussian distribution. Finally, the point clusters are grouped according to their topological relationship and spatial proximity to form strokes for recovering the road map. Experimental results show that our algorithm is robust to noise and varied sampling rates. The generated road maps show high geometric accuracy. Full article

(This article belongs to the Special Issue Location-Based Services)

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Open AccessArticle

Modeling and Querying Moving Objects with Social Relationships

by Hengcai Zhang, Feng Lu and Jianqiu Xu

ISPRS Int. J. Geo-Inf. 2016, 5(7), 121; https://doi.org/10.3390/ijgi5070121 - 15 Jul 2016

Cited by 7 | Viewed by 4899

Abstract

Current moving-object database (MOD) systems focus on management of movement data, but pay less attention to modelling social relationships between moving objects and spatial-temporal trajectories in an integrated manner. This paper combines moving-object database and social network systems and presents a novel data [...] Read more.

Current moving-object database (MOD) systems focus on management of movement data, but pay less attention to modelling social relationships between moving objects and spatial-temporal trajectories in an integrated manner. This paper combines moving-object database and social network systems and presents a novel data model called Geo-Social-Moving (GSM) that enables the unified management of trajectories, underlying geographical space and social relationships for mass moving objects. A bulk of user-defined data types and corresponding operators are also proposed to facilitate geo-social queries on moving objects. An implementation framework for the GSM model is proposed, and a prototype system based on native Neo4J is then developed with two real-world data sets from the location-based social network systems. Compared with solutions based on traditional extended relational database management systems characterized by time-consuming table join operations, the proposed GSM model characterized by graph traversal is argued to be more powerful in representing mass moving objects with social relationships, and more efficient and stable for geo-social querying. Full article

(This article belongs to the Special Issue Location-Based Services)

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Open AccessArticle

Detecting Themed Streets Using a Location Based Service Application

by Byeongsuk Ji, Youngmin Lee, Kiyun Yu and Pil Kwon

ISPRS Int. J. Geo-Inf. 2016, 5(7), 111; https://doi.org/10.3390/ijgi5070111 - 12 Jul 2016

Cited by 3 | Viewed by 4799

Abstract

Various themed streets have recently been developed by local governments in order to stimulate local economies and to establish the identity of the corresponding places. However, the motivations behind the development of some of these themed street projects has been based on profit, [...] Read more.

Various themed streets have recently been developed by local governments in order to stimulate local economies and to establish the identity of the corresponding places. However, the motivations behind the development of some of these themed street projects has been based on profit, without full considerations of people’s perceptions of their local areas, resulting in marginal effects on the local economies concerned. In response to this issue, this study proposed a themed street clustering method to detect the themed streets of a specific region, focusing on the commercial themed street, which is more prevalent than other types of themed streets using location based service data. This study especially uses “the street segment” as a basic unit for analysis. The Sillim and Gangnam areas of Seoul, South Korea were chosen for the evaluation of the adequacy of the proposed method. By comparing trade areas that were sourced from a market analysis report by a reliable agent with the themed streets detected in this study, the experiment results showed high proficiency of the proposed method. Full article

(This article belongs to the Special Issue Location-Based Services)

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Open AccessArticle

Heading Estimation with Real-time Compensation Based on Kalman Filter Algorithm for an Indoor Positioning System

by Xin Li, Jian Wang and Chunyan Liu

ISPRS Int. J. Geo-Inf. 2016, 5(6), 98; https://doi.org/10.3390/ijgi5060098 - 20 Jun 2016

Cited by 11 | Viewed by 6155

Abstract

The problem of heading drift error using only low cost Micro-Electro-Mechanical (MEMS) Inertial-Measurement-Unit (IMU) has not been well solved. In this paper, a heading estimation method with real-time compensation based on Kalman filter has been proposed, abbreviated as KHD. For the KHD method, [...] Read more.

The problem of heading drift error using only low cost Micro-Electro-Mechanical (MEMS) Inertial-Measurement-Unit (IMU) has not been well solved. In this paper, a heading estimation method with real-time compensation based on Kalman filter has been proposed, abbreviated as KHD. For the KHD method, a unified heading error model is established for various predictable errors in magnetic compass for pedestrian navigation, and an effective method for solving the model parameters is proposed in the indoor environment with regular structure. In addition, error model parameters are solved by Kalman filtering algorithm with building geometry information in order to achieve real-time heading compensation. The experimental results show that the KHD method can not only effectively correct the original heading information, but also effectively inhibit the accumulation effect of positioning errors. The performance observed in a field experiment performed on the fourth floor of the School of Environmental Science and Spatial Informatics (SESSI) building on the China University of Mining and Technology (CUMT) campus confirms that apply KHD method to PDR(Pedestrian Dead Reckoning) algorithm can reliably achieve meter-level positioning using a low cost MEMS IMU only. Full article

(This article belongs to the Special Issue Location-Based Services)

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Open AccessArticle

A Knowledge-Based Step Length Estimation Method Based on Fuzzy Logic and Multi-Sensor Fusion Algorithms for a Pedestrian Dead Reckoning System

by Ying-Chih Lai, Chin-Chia Chang, Chia-Ming Tsai, Shih-Ching Huang and Kai-Wei Chiang

ISPRS Int. J. Geo-Inf. 2016, 5(5), 70; https://doi.org/10.3390/ijgi5050070 - 17 May 2016

Cited by 15 | Viewed by 7161

Abstract

The demand for pedestrian navigation has increased along with the rapid progress in mobile and wearable devices. This study develops an accurate and usable Step Length Estimation (SLE) method for a Pedestrian Dead Reckoning (PDR) system with features including a wide range of [...] Read more.

The demand for pedestrian navigation has increased along with the rapid progress in mobile and wearable devices. This study develops an accurate and usable Step Length Estimation (SLE) method for a Pedestrian Dead Reckoning (PDR) system with features including a wide range of step lengths, a self-contained system, and real-time computing, based on the multi-sensor fusion and Fuzzy Logic (FL) algorithms. The wide-range SLE developed in this study was achieved by using a knowledge-based method to model the walking patterns of the user. The input variables of the FL are step strength and frequency, and the output is the estimated step length. Moreover, a waist-mounted sensor module has been developed using low-cost inertial sensors. Since low-cost sensors suffer from various errors, a calibration procedure has been utilized to improve accuracy. The proposed PDR scheme in this study demonstrates its ability to be implemented on waist-mounted devices in real time and is suitable for the indoor and outdoor environments considered in this study without the need for map information or any pre-installed infrastructure. The experiment results show that the maximum distance error was within 1.2% of 116.51 m in an indoor environment and was 1.78% of 385.2 m in an outdoor environment. Full article

(This article belongs to the Special Issue Location-Based Services)

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Open AccessArticle

A Multi-Element Approach to Location Inference of Twitter: A Case for Emergency Response

by Farhad Laylavi, Abbas Rajabifard and Mohsen Kalantari

ISPRS Int. J. Geo-Inf. 2016, 5(5), 56; https://doi.org/10.3390/ijgi5050056 - 28 Apr 2016

Cited by 61 | Viewed by 9858

Abstract

Since its inception, Twitter has played a major role in real-world events—especially in the aftermath of disasters and catastrophic incidents, and has been increasingly becoming the first point of contact for users wishing to provide or seek information about such situations. The use [...] Read more.

Since its inception, Twitter has played a major role in real-world events—especially in the aftermath of disasters and catastrophic incidents, and has been increasingly becoming the first point of contact for users wishing to provide or seek information about such situations. The use of Twitter in emergency response and disaster management opens up avenues of research concerning different aspects of Twitter data quality, usefulness and credibility. A real challenge that has attracted substantial attention in the Twitter research community exists in the location inference of twitter data. Considering that less than 2% of tweets are geotagged, finding location inference methods that can go beyond the geotagging capability is undoubtedly the priority research area. This is especially true in terms of emergency response, where spatial aspects of information play an important role. This paper introduces a multi-elemental location inference method that puts the geotagging aside and tries to predict the location of tweets by exploiting the other inherently attached data elements. In this regard, textual content, users’ profile location and place labelling, as the main location-related elements, are taken into account. Location-name classes in three granularity levels are defined and employed to look up the location references from the location-associated elements. The inferred location of the finest granular level is assigned to a tweet, based on a novel location assignment rule. The location assigned by the location inference process is considered to be the inferred location of a tweet, and is compared with the geotagged coordinates as the ground truth of the study. The results show that this method is able to successfully infer the location of 87% of the tweets at the average distance error of 12.2 km and the median distance error of 4.5 km, which is a significant improvement compared with that of the current methods that can predict the location with much larger distance errors or at a city-level resolution at best. Full article

(This article belongs to the Special Issue Location-Based Services)

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Open AccessArticle

A Novel Dynamic Physical Storage Model for Vehicle Navigation Maps

by Shaohua Wang, Ershun Zhong, Kai Li, Guanfu Song and Wenwen Cai

ISPRS Int. J. Geo-Inf. 2016, 5(4), 53; https://doi.org/10.3390/ijgi5040053 - 22 Apr 2016

Cited by 3 | Viewed by 5684

Abstract

The physical storage model is one of the key technologies for vehicle navigation maps used in a navigation system. However, the performance of most traditional storage models is limited in dynamic navigation due to the static storage format they use. In this paper, [...] Read more.

The physical storage model is one of the key technologies for vehicle navigation maps used in a navigation system. However, the performance of most traditional storage models is limited in dynamic navigation due to the static storage format they use. In this paper, we proposed a new physical storage model, China Navigation Data Format (CNDF), which helped access and update the navigation data. The CNDF model used the reach-based hierarchy method to build a road hierarchal network, which enhanced the efficiency of data compression. It also adopted the Linear Link Coding method, in which the start position was combined with the end position as the identification code for multi-level links, and each link traced up-level links consistently without recording the array of identifications. The navigation map of East China (including Beijing, Tianjin, Shandong, Hebei, and Jiangsu) at 1:10,000, generated using the CNDF model, and the real time traffic information in Beijing were combined to test the performance of a navigation system using an embedded navigation device. Results showed that it cost less than 1 second each time to refresh the navigation map, and the accuracy of the hierarchal shortest-path algorithm was 99.9%. Our work implied that the CNDF model is efficient in vehicle navigation applications. Full article

(This article belongs to the Special Issue Location-Based Services)

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Open AccessArticle

Integrated WiFi/PDR/Smartphone Using an Adaptive System Noise Extended Kalman Filter Algorithm for Indoor Localization

by Xin Li, Jian Wang, Chunyan Liu, Liwen Zhang and Zhengkui Li

ISPRS Int. J. Geo-Inf. 2016, 5(2), 8; https://doi.org/10.3390/ijgi5020008 - 1 Feb 2016

Cited by 75 | Viewed by 7642

Abstract

Wireless signal strength is susceptible to the phenomena of interference, jumping, and instability, which often appear in the positioning results based on Wi-Fi field strength fingerprint database technology for indoor positioning. Therefore, a Wi-Fi and PDR (pedestrian dead reckoning) real-time fusion scheme is [...] Read more.

Wireless signal strength is susceptible to the phenomena of interference, jumping, and instability, which often appear in the positioning results based on Wi-Fi field strength fingerprint database technology for indoor positioning. Therefore, a Wi-Fi and PDR (pedestrian dead reckoning) real-time fusion scheme is proposed in this paper to perform fusing calculation by adaptively determining the dynamic noise of a filtering system according to pedestrian movement (straight or turning), which can effectively restrain the jumping or accumulation phenomena of wireless positioning and the PDR error accumulation problem. Wi-Fi fingerprint matching typically requires a quite high computational burden: To reduce the computational complexity of this step, the affinity propagation clustering algorithm is adopted to cluster the fingerprint database and integrate the information of the position domain and signal domain of respective points. An experiment performed in a fourth-floor corridor at the School of Environment and Spatial Informatics, China University of Mining and Technology, shows that the traverse points of the clustered positioning system decrease by 65%–80%, which greatly improves the time efficiency. In terms of positioning accuracy, the average error is 4.09 m through the Wi-Fi positioning method. However, the positioning error can be reduced to 2.32 m after integration of the PDR algorithm with the adaptive noise extended Kalman filter (EKF). Full article

(This article belongs to the Special Issue Location-Based Services)

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