Statistics for Spatial Data.

The theory of affordances

The availability of user check-in data in large volume from the rapid growing location based social networks (LBSNs) enables many important location-aware services to users. Point-of-interest (POI) recommendation is one of such services, which is to recommend places where users have not visited before. Several techniques have been recently proposed for the recommendation service. However, no existing work has considered the temporal information for POI recommendations in LBSNs. We believe that time plays an important role in POI recommendations because most users tend to visit different places at different time in a day, \eg visiting a restaurant at noon and visiting a bar at night. In this paper, we define a new problem, namely, the time-aware POI recommendation, to recommend POIs for a given user at a specified time in a day. To solve the problem, we develop a collaborative recommendation model that is able to incorporate temporal information. Moreover, based on the observation that users tend to visit nearby POIs, we further enhance the recommendation model by considering geographical information. Our experimental results on two real-world datasets show that the proposed approach outperforms the state-of-the-art POI recommendation methods substantially.

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Time-aware point-of-interest recommendation

The emergence of big data brings new opportunities for us to understand our socioeconomic environments. We use the term social sensing for such individual-level big geospatial data and the associat...

Social Sensing: A New Approach to Understanding Our Socioeconomic Environments

Recent advances in localization techniques have fundamentally enhanced social networking services, allowing users to share their locations and location-related contents, such as geo-tagged photos and notes. We refer to these social networks as location-based social networks (LBSNs). Location data bridges the gap between the physical and digital worlds and enables a deeper understanding of users' preferences and behavior. This addition of vast geo-spatial datasets has stimulated research into novel recommender systems that seek to facilitate users' travels and social interactions. In this paper, we offer a systematic review of this research, summarizing the contributions of individual efforts and exploring their relations. We discuss the new properties and challenges that location brings to recommender systems for LBSNs. We present a comprehensive survey analyzing 1) the data source used, 2) the methodology employed to generate a recommendation, and 3) the objective of the recommendation. We propose three taxonomies that partition the recommender systems according to the properties listed above. First, we categorize the recommender systems by the objective of the recommendation, which can include locations, users, activities, or social media. Second, we categorize the recommender systems by the methodologies employed, including content-based, link analysis-based, and collaborative filtering-based methodologies. Third, we categorize the systems by the data sources used, including user profiles, user online histories, and user location histories. For each category, we summarize the goals and contributions of each system and highlight the representative research effort. Further, we provide comparative analysis of the recommender systems within each category. Finally, we discuss the available data-sets and the popular methods used to evaluate the performance of recommender systems. Finally, we point out promising research topics for future work. This article presents a panorama of the recommender systems in location-based social networks with a balanced depth, facilitating research into this important research theme.

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Recommendations in location-based social networks: a survey

Feature types play a crucial role in understanding and analyzing geographic information. Usually, these types are defined, standardized, and controlled by domain experts and cover geographic features on the mesoscale level, e.g., populated places, forests, or lakes. While feature types also underlie most Location-Based Services (LBS), assigning a consistent typing schema for Points Of Interest (POI) across different data sets is challenging. In case of Volunteered Geographic Information (VGI), types are assigned as tags by a heterogeneous community with different backgrounds and applications in mind. Consequently, VGI research is shifting away from data completeness and positional accuracy as quality measures towards attribute accuracy. As tags can be assigned by everybody and have no formal or stable definition, we propose to study category tags via indirect observations. We extract user check-ins from massive real-world data crawled from Location-based Social Networks to understand the temporal dimension of Points Of Interest. While users may assign different category tags to places, we argue that their temporal characteristics, e.g., opening times, will show distinguishable patterns.

What you are is when you are: the temporal dimension of feature types in location-based social networks

With the increasing success and commercial integration of Volunteered Geographic Information (VGI), the focus shifts away from coverage to data quality and homogeneity. Within the last years, several studies have been published analyzing the positional accuracy of features, completeness of specific attributes, or the topological consistency of line and polygon features. However, most of these studies do not take geographic feature types into account. This is for two reasons. First, and in contrast to street networks, choosing a reference set is difficult. Second, we lack the measures to quantify the degree of feature type miscategorization. In this work, we present a methodology to analyze the spatial-semantic interaction of point features in Volunteered Geographic Information. Feature types in VGI can be considered special in both, the way they are formed and the way they are applied. Given that they reflect community agreement more accurately than top-down approaches, we argue that they should be used as the primary basis for assessing spatial-semantic interaction. We present a case study on a spatial and semantic subset of OpenStreetMap, and introduce a novel semantic similarity measure based on the change history of OpenStreetMap elements. Our results set the stage for systems that assist VGI contributors in suggesting the types of new features, cleaning up existing data, and integrating data from different sources.

Analyzing the spatial-semantic interaction of points of interest in volunteered geographic information

The term Volunteered Geographic Information (VGI) describes various layperson-based, geo-collaboration projects to collect, maintain, and visualize information. VGI has been successfully utilized in scenarios such as emergency response and is also increasingly integrated into commercial products. Based on an analysis of existing projects and research, we propose to extend the idea of VGI by introducing Volunteered Geographic Services (VGS). Instead of contributing information, volunteers can request or offer microservices to their local community. We provide a flexible server framework that handles service requests and offers. We also implement a smartphone application developed using Google's Android platform. The server and mobile client are realized following the Linked Data paradigm and using Semantic Web technologies. In this paper, we discuss the idea behind VGS, motivate it using two scenarios, and explain the technical realization.

Volunteered geographic services: developing a linked data driven location-based service

Sketch maps are drawn from memories and they are in general schematized and distorted. However, the schematizations and distortions are not random. They are a consequence during the cognitive process of perceiving, memorizing, and producing spatial layout. This paper describes an empirical study to investigate the impact of distortions on similarity perception. The study is designed as a human-subjects experiment of similarity ranking with two scenarios. Subjects were presented with 45 sketch maps and one reference map in each scenario; they were asked to rank the sketch maps according to their similarities with the reference map. The results of the experiment are used to develop a cognitively motivated alignment strategy for computer-based comparison of sketch maps and metric maps.

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An Empirical Study on Relevant Aspects for Sketch Map Alignment

Semantic similarity measurement has been an active research area in GIScience and the Semantic Web for many years. However, implementations of these measures were largely missing, not publicly available, or tailored to specific application needs. To foster the application of similarity reasoning in information retrieval, ontology engineering, and spatial decision support, we implemented the SIM-DL semantic similarity server as well as a plug-in for the popular Protege ontology editor. While SIM-DL has been successfully applied to several application areas, the implemented similarity theory was largely structural, could not handle concept and instance similarity within the same framework, and was based on a Protege version and DIG interface that have been re-engineered over the last years. This paper introduces a new version, called SIM-DLA, engineered from scratch to addresses these shortcomings. It is based on our new similarity theory, can handle inter-instance and inter-concept similarity using the same functions and alignments, and is available for the new Protege version 4.1.

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Christoph Mülligann

Papers

What you are is when you are: the temporal dimension of feature types in location-based social networks

Analyzing the spatial-semantic interaction of points of interest in volunteered geographic information

Volunteered geographic services: developing a linked data driven location-based service

An Empirical Study on Relevant Aspects for Sketch Map Alignment

Introducing the new SIM-DLA semantic similarity measurement plug-in for the Protégé ontology editor