A Javascript GIS Platform Based on Invocable Geospatial Web Services

Provenance, a metadata component referring to the origin and the processes undertaken to obtain a specific geographic digital feature or product, is crucial to evaluate the quality of spatial information and help in reproducing and replicating geospatial processes. However, the heterogeneity and complexity of the geospatial processes, which can potentially modify part or the complete content of datasets, make evident the necessity for describing geospatial provenance at dataset, feature and attribute levels. This paper presents the application of W3C PROV, which is a generic specification to express provenance records, for representing geospatial data provenance at these different levels. In particular, W3C PROV is applied to feature models, where geospatial phenomena are represented as individual features described with spatial (point, lines, polygons, etc.) and non-spatial (names, measures, etc.) attributes.This paper first analyses the potential for representing geospatial provenance in a distributed environment at the three levels of granularity using ISO 19115 and W3C PROV models. Next, an approach for applying the generic W3C PROV provenance model to the geospatial environment is presented. As a proof of concept, we provide an application of W3C PROV to describe geospatial provenance at the feature and attribute levels. The use case presented consists of a conflation of the U.S. Geological Survey dataset with the National Geospatial-Intelligence Agency dataset. Finally, an example of how to capture the provenance resulting from workflows and chain executions with PROV is also presented. The application uses a web processing service, which enables geospatial processing in a distributed system and allows to capture the provenance information based on the W3C PROV ontology at the feature and attribute levels.

In recent years, there has been growing interest in Location Based Service (LBS). However, the existing LBS has some limitations in the management of dynamic location-dependent contents and interoperability between the different platforms and various domains. We have focused on how LBS applications obtain integrated, dynamic, and sensitive contents on the different domains. We approach the Semantic Web technologies to resolve these issues. In this paper, we design the architecture to combine the Semantic Web technologies with the LBS and implement the prototype to describe the Semantic LBS.

The widespread use of Internet of Things (IoT) technology has promoted location‐based service (LBS) applications. Users can enjoy various conveniences brought by LBS by providing location information to LBS. However, it also brings potential privacy threats to location information. Location data that contains private information is often transmitted among IoT networks in LBS, and such privacy information should be protected. In order to solve the problem of location privacy leakage in LBS, a location privacy protection scheme based on k‐anonymity is proposed in this paper, in which the Geohash coding model and Voronoi graph are used as grid division principles. We adopt the client‐server‐to‐user (CS2U) model to protect the user’s location data on the client side and the server side, respectively. On the client side, the Geohash algorithm is proposed, which converts the user’s location coordinates into a Geohash code of the corresponding length. On the server side, the Geohash code generated by the user is inserted into the prefix tree, the prefix tree is used to find the nearest neighbors according to the characteristics of the coded similar prefixes, and the Voronoi diagram is used to divide the area units to complete the pruning. Then, using the Geohash coding model and the Voronoi diagram grid division principle, the G‐V anonymity algorithm is proposed to find k neighbors in an anonymous area so that the user’s location data meets the k‐anonymity requirement in the area unit, thereby achieving anonymity protection of location privacy. Theoretical analysis and experimental results show that our method is effective in terms of privacy and data quality while reducing the time of data anonymity.

Geospatially Enabled Scientific Workflows offer a promising toolset to help researchers in the earth observation domain with many aspects of the scientific process. One such aspect is that of access to distributed earth observation data and computing resources. Earth observation research often utilizes large datasets requiring extensive CPU and memory resources in their processing. These resource intensive processes can be chained; the sequence of processes (and their provenance) makes up a scientific workflow. Despite the exponential growth in capacity of desktop computers, their resources are often insufficient for the scientific workflow processing tasks at hand. By integrating distributed computing capabilities into a geospatially enabled scientific workflow environment, it is possible to provide researchers with a mechanism to overcome the limitations of the desktop computer. Most of the effort on extending scientific workflows with distributed computing capabilities has focused on the web services approach, as exemplified by the OGC's Web Processing Service and by GRID computing. The approach to leveraging distributed computing resources described in this article uses instead remote objects via RPyC and the dynamic properties of the Python programming language. The Vistrails environment has been extended to allow for geospatial processing through the EO4Vistrails package (http://code.google.com/p/eo4vistrails/). In order to allow these geospatial processes to be seamlessly executed on distributed resources such as cloud computing nodes, the Vistrails environment has been extended with both multi‐tasking capabilities and distributed processing capabilities. The multi‐tasking capabilities are required in order to allow Vistrails to run side‐by‐side processes, a capability it does not currently have. The distributed processing capabilities are achieved through the use of remote objects and mobile code through RPyC.

Smart devices--including speakers, cameras, TVs, and sensors--are increasingly used to enhance comfort, efficiency, security, and entertainment in and outside the home. These devices collect significant audio, video, and environmental data, and that data may be shared with device owners, manufacturers, and/or third-parties. Social computing researchers have devoted significant attention to the privacy risks posed by these devices, although the focus has largely been on primary users who have access and control of devices and data. More recently, researchers have begun examining the privacy risks to incidental users --those who do not own or manage devices but still have their data collected due to their presence in a smart environment. In this paper, we present findings from 25 interviews with US-based adults who have encountered and/or used smart devices as incidental users in rental properties, as part of their work, at friends' and family's houses, and in shared living environments. We explore their reactions to these devices across different contexts, as well as their expectations for how devices should be disclosed and managed by primary users. We found a prevailing sense of resignation across our participants, as well behavioral and environmental modifications in response to smart devices. At the same time, participants were disinterested in gaining access and control to other people's devices, and they were hesitant or uncomfortable with the idea of initiating communication with device owners regarding their concern. Many wanted clearer and more prominent disclosures about device and data management that shifted responsibility from the incidental user to the device owner. Based on our findings, we argue for researchers to focus more closely on sociotechnical approaches to help incidental users navigate privacy risks in smart environments. Such approaches must account for interpersonal factors like trust, power dynamics, and social norms in addition to the many technical solutions that have been introduced in recent years.

Abstract. Geospatial data visualization systems have been predominantly through applications that are installed and run in a desktop environment. Over the last decade, with the advent of web technologies and its adoption by Geospatial community, the server-client model for data handling, data rendering and visualization respectively has been the most prevalent approach in Web-GIS. While the client devices have become functionally more powerful over the recent years, the above model has largely ignored it and is still in a mode of serverdominant computing paradigm. In this paper, an attempt has been made to develop and demonstrate LSIViewer – a simple, easy-to-use and robust online geospatial data visualisation system for the user’s own data that harness the client’s capabilities for data rendering and user-interactive styling, with a reduced load on the server. The developed system can support multiple geospatial vector formats and can be integrated with other web-based systems like WMS, WFS, etc. The technology stack used to build this system is Node.js on the server side and HTML5 Canvas and JavaScript on the client side. Various tests run on a range of vector datasets, upto 35 MB, showed that the time taken to render the vector data using LSIViewer is comparable to a desktop GIS application, QGIS, over an identical system.

With the development of mobile internet and smart devices, location-based services (LBS) have developed rapidly and attracted more and more users. The availability of a large amount of user interaction data makes it possible to provide more personalized and accurate recommendation services. However, in mobile scenarios, multiple influencing factors such as the diversity of user preferences, the variability of user behavior, and the dynamics of spatiotemporal contexts bring great challenges to recommendation services. To accurately capture the preferences of mobile users in dynamic contexts, we propose an Inherent and Contextual Preference-aware Attention Network (ICPAN) for online recommendation in location-based services. Our ICPAN consists of an inherent-preference mining module with self-attention layers, a contextual-preference perception module with improved IR\(^2\)-tree-based index structures, and an online recommendation module. The inherent-preference and contextual-preference models are trained based on global historical behavior data and instantly selected context-sensitive data, respectively. And then the online recommendation module uses attention aggregation to couple the two preference representations to generate the final recommendation result. Extensive experiments are conducted on three real datasets, and the experimental results show that the proposed ICPAN outperforms existing state-of-the-art methods.KeywordsLocation-based serviceRecommender systemSelf-attentionIR\(^2\)-tree

This study presents the development of a Web-based Geographic Information System (Web GIS) aimed at mapping historical tourism sites in Sumenep Regency. The system integrates Location-Based Services (LBS) to enhance the delivery of spatial information and support tourism promotion. A prototype-based methodology was used in designing and building the application, which includes planning, requirements analysis, system design, data collection, and testing. The system architecture was modeled using UML diagrams, including use case, activity, and entity-relationship diagrams to ensure functionality and data integrity. Spatial and attribute data were collected through literature reviews and transformed into an interactive web platform that allows users to search for and visualize historical tourism locations. The system supports administrative features for data input and updates, alongside public-facing features such as maps and visitor statistics. Testing was conducted using black-box methods to verify the system’s performance and reliability. The final output is a fully functional Web GIS platform that enhances public access to tourism information and assists local authorities in managing tourism data effectively. This research contributes to the advancement of digital tourism infrastructure and promotes cultural heritage visibility in the region.

In recent times, social and commercial interests in location-based services (LBS) are significantly increasing due to the rise in smart devices and technologies. The global navigation satellite systems (GNSS) have long been employed for LBS to navigate and determine accurate and reliable location information in outdoor environments. However, the GNSS signals are too weak to penetrate buildings and unable to provide reliable indoor LBS. Hence, GNSS’s incompetence in the indoor environment invites extensive research and development of an indoor positioning system (IPS). Various technologies and techniques have been studied for IPS development. This paper provides an overview of the available smartphone-based indoor localization solutions that rely on radio frequency technologies. As fingerprinting localization is mostly accepted for IPS development owing to its good localization accuracy, we discuss fingerprinting localization in detail. In particular, our analysis is more focused on practical IPS that are realized using a smartphone and Wi-Fi/Bluetooth Low Energy (BLE) as a signal source. Furthermore, we elaborate on the challenges of practical IPS, the available solutions and comprehensive performance comparison, and present some future trends in IPS development.

Probability-based prediction query algorithm

Traveling is a favorite leisure activity. People always take notes, photos, or videos to remember while traveling. But few of them make travel records completely and continuously. One possible reason is that it is difficult to record, in meticulous detail, traveling; another one is that tourists lack proper tools to facilitate collecting and organizing traveling records. The popularity of smart mobile devices provides a convenient approach for organizing travel records. In this paper, a traveling memory (TM), based upon location-based services and social network sites is introduced. It adopts check-in service in Facebook to let travelers easily collect travel information, and then upload to the TM application. With this application, travelers can select, organize, and share their traveling information. Upon completion of system development, the system usability scale approach was adopted to evaluate system usability. The result shows that travelers have favorable responses to the system.

The widespread penetration of smart mobile devices has facilitated rapid growth of mobile location-based services (LBS), which provide users with a variety of benefits and are attractive from a marketing perspective. However, mobile-payment (M-Payment) adoption by users has been below expectations. For better understanding of drivers and inhibitors of the willingness to M-Pay for mobile LBS, this study contributes by conceptual modeling and empirical assessment of user willingness to M-Pay. To test the proposed conceptual research model, data from 122 valid responses were analyzed by employing the Partial Least Squares (PLS) technique. The findings show that Perceived Risk is the main inhibitor of user willingness to M-Pay for LBS and that the magnitude of this inhibitor’s negative impact is at least twice the magnitude of any driver’s positive impact.

This paper reports on the early-stage development of an analytics framework to support the semantic integration of dynamic surveillance data across multiple scales to inform decision making for malaria eradication. We propose using the Semantic Web of Things (SWoT), a combination of Internet of Things (IoT) and semantic web technologies, to support the evolution and integration of dynamic malaria data sources and improve interoperability between different datasets generated through relevant IoT assets (e.g. computers, sensors, persons, and other smart objects and devices).

Background and Purpose: In recent years, the use of smart mobile devices due to easy access, portability, and also simpler user interface, compared to desktop computers has been met with unparalleled acceptance by Iranian users, managers, and sellers of goods. That's why application development for smart devices in Internet businesses has become essential. On the other hand, the location-awareness of such programs will make it smarter and provide targeted advertising and services to users.Methods: This study provides a model that first handles and categorizes information from some service centers and quickly exchanges data between smart media (such as a mobile, tablet) and data centers based upon online map services (Google Maps). Second, based on the components of location-based services (LBS), in order to provide effective advertising to users, the components of selecting the service center and advertising and navigation of the user to it were put on the agenda based on a loose coupling strategy.Findings: In order to have the features of recommendation, three criteria of desirability, namely the introduction of the final option based on the shortest route in the transportation network, or based on user scores or based on scores allocated by the relevant service unit class were considered. Finally, the Location Finder application was implemented under the Android operating system using the new technologies of the mobile and web-based spatial information system through the proposed model.Discussion and Conclusion: The results of the questionnaire were statistically analyzed using the frequency of the respondents and significantly showed the users' satisfaction with the program, with 55 percent rating it as a great choice for quality, and its compliance with the set goals so that it could fill part of the gap caused by the advertising software.

In this paper, we have addressed the problem of data and location privacy in smart devices over vehicular cloud computing (VCC). We proposed a framework to identify and register the smart IoT GPS devices over VCC service and allow the users to monitor their devices in real time. The proposed framework divides into three parts: First, data anonymization of users' information over VCC, by masking the original data of the user and replaced with fake data. The proposed technique will remove the user identity and other linkers to identify the users. Second, proposed a technique using asymmetric cryptography (RSA) technique, the proposed technique provides location privacy of users' trajectories before requesting point of interest (POI) from location-based services (LBS). Third, secure communication between users and the VCC, based on Token-based authentication by authenticating the trusted users while requesting a location from the VCC service. The proposed framework shows the efficiency and reliability of responding to user trajectories from different sources of IoT GPS devices and datasets.