Hongchao Fan

Quality assessment for building footprints data on OpenStreetMap

In the past two years, several applications of generating three-dimensional (3D) buildings from OpenStreetMap (OSM) have been made available, for instance, OSM-3D, OSM2World, OSM Building, etc. In these projects, 3D buildings are reconstructed using the buildings’ footprints and information about their attributes, which are documented as tags in OSM. Therefore, the quality of 3D buildings relies strongly on the quality of the building footprints data in OSM. This article is dedicated to a quality assessment of building footprints data in OSM for the German city of Munich, which is one of the most developed cities in OSM. The data are evaluated in terms of completeness, semantic accuracy, position accuracy, and shape accuracy by using building footprints in ATKIS (German Authority Topographic–Cartographic Information System) as reference data. The process contains three steps: finding correspondence between OSM and ATKIS data, calculating parameters of the four quality criteria, and statistical analysis. The results show that OSM footprint data in Munich have a high completeness and semantic accuracy. There is an offset of about four meters on average in terms of position accuracy. With respect to shape, OSM building footprints have a high similarity to those in ATKIS data. However, some architectural details are missing; hence, the OSM footprints can be regarded as a simplified version of those in ATKIS data.

Road-based travel recommendation using geo-tagged images

Abstract Geotagged photos on social media like Flickr explicitly indicate the trajectories of tourists. They can be employed to reveal the tourists’ preference on landmarks and routings of tourism. Most of existing works on routing searches are based on the trajectories of GPS-enabled devices’ users. From a distinct point of view, we attempt to propose a novel approach in which the basic unit of routing is separate road segment instead of GPS trajectory segment. In this paper, we build a recommendation system that provides users with the most popular landmarks as well as the best travel routings between the landmarks. By using Flickr geotaggged photos, the top ranking travel destinations in a city can be identified and then the best travel routes between the popular travel destinations are recommended. We apply a spatial clustering method to identify the main travel landmarks and subsequently rank these landmarks. Using machine learning method, we calculate the tourism popularity of the road in terms of relevant parameters, e.g., the number of users and the number of Point-of-Interests. These popularity assessments are integrated into the routing recommendation system. The routing recommendation system takes into consideration both the popularity assessment and the length of the road. The best route recommended to the user minimizes the distance while including maximal tourism popularity. Experiments were conducted in two different scenarios. The empirical results show that the recommendation system is able to provide the user good travel planning including both top ranking landmarks and suitable routings in a city. Besides, the system offers user-generated semantic information for the recommended routes.

A three-step approach of simplifying 3D buildings modeled by CityGML

CityGML (City Geography Markup Language), the OGC (Open Open Geospatial Consortium) standard on three-dimensional (3D) city modeling, is widely used in an increasing number of applications, because it models a city with rich geometrical and semantic information. The underlying building model differentiates four consecutive levels of detail (LoDs). Nowadays, most city buildings are reconstructed in LoD3, while few landmarks in LoD4. For visualization or other purposes, buildings in LoD2 or LoD1 need to be derived from LoD3 models. But CityGML does not indicate methods for the automatic derivation of the different LoDs. This article presents an approach for deriving LoD2 buildings from LoD3 models which are essentially the exterior shells of buildings without opening objects. This approach treats different semantic components of a building separately with the aim to preserve the characteristics of ground plan, roof, and wall structures as far as possible. The process is composed of three steps: simplifying wall elements, generalizing roof structures, and then reconstructing the 3D building by intersecting the wall and roof polygons. The first step simplifies ground plan with wall elements projected onto the ground. A new algorithm is developed to handle not only simple structures like parallel and rectangle shapes but also complicated structures such as non-parallel, non-rectangular shapes and long narrow angles. The algorithm for generalizing roof structure is based on the same principles; however, the calculation has to be conducted in 3D space. Moreover, the simplified polygons of roof structure are further merged and typified depending on the spatial relations between two neighboring polygons. In the third step, generalized 3D buildings are reconstructed by increasing walls in height and intersecting with roof structures. The approach has been implemented and tested on a number of 3D buildings. The experiments have verified that the 3D building can be efficiently generalized, while the characteristics of wall and roof structure can be well preserved after the simplification.

Generalization of 3D Buildings Modelled by CityGML

City GML (City Geography Markup Language) not only represents the shape and graphical appearance of 3D buildings but specifically addresses the object semantics and the thematic properties, taxonomies and aggregations. The generalization algorithm presented in this paper takes this advantage of CityGML. That means that our approach considers the semantic information associated with geometrical objects of buildings to be generalized. Experiments show that the approach can reduce about 90% of the storage space of 3D buildings while keeping the information amounts as far as possible.

Analyzing Human Activities Through Volunteered Geographic Information: Using Flickr to Analyze Spatial and Temporal Pattern of Tourist Accommodation

Volunteered Geographic Information (VGI) provides valuable information to analyze human activities in space and time. In this chapter, we use Flickr photos as an example to explore the possibilities of VGI to analyze spatiotemporal patterns of tourists’ accommodation in Vienna, Austria as study site. Kernel density estimations and spatial scan statistics are used to explore the distribution of photos, while seasonality is considered additionally. The results show seasonal tendency of tourists for accommodation. It has been discovered that Flickr photos have, in general, the capability to improve tourism-related researches. In particular, they are useful to investigate spatiotemporal human activities, which open new possibilities for further location and event based analysis.

A polygon-based approach for matching OpenStreetMap road networks with regional transit authority data

ABSTRACT Matching road networks is an essential step for data enrichment and data quality assessment, among other processes. Conventionally, road networks from two datasets are matched using a line-based approach that checks for the similarity of properties of line segments. In this article, a polygon-based approach is proposed to match the OpenStreetMap road network with authority data. The algorithm first extracts urban blocks that are central elements of urban planning and are represented by polygons surrounded by their surrounding streets, and it then assigns road lines to edges of urban blocks by checking their topologies. In the matching process, polygons of urban blocks are matched in the first step by checking for overlapping areas. In the second step, edges of a matched urban block pair are further matched with each other. Road lines that are assigned to the same matched pair of urban block edges are then matched with each other. The computational cost is substantially reduced because the proposed approach matches polygons instead of road lines, and thus, the process of matching is accelerated. Experiments on Heidelberg and Shanghai datasets show that the proposed approach achieves good and robust matching results, with a precision higher than 96% and a F1-score better than 90%.

Identifying the city center using human travel flows generated from location-based social networking data:

Since cities have become more complex and some large cities are likely to be polycentric, a better understanding of cities requires a clear topology that reveals how city centers are spatially distributed and interacted with. The identification of a city center that aims to find the accurate location of the city center or delineate the city center with a precise boundary becomes vital. This work attempts to achieve this by using a new type of movement data generated from location-based social networks, whereby three different methods are deployed for clustering and compared regarding identification of city centers and delineation of their boundaries. Experiments show that city centers with precise boundaries can be identified by using the proposed approach with location-based social network data. Furthermore, the results show that the three methods for clustering have different advantages and disadvantages during the process of city center identification, and thus seem to be suitable for cities with different urban structures.

Polygon-based approach for extracting multilane roads from OpenStreetMap urban road networks

This study proposes a novel approach for extracting multilane roads from urban road networks in OpenStreetMap (OSM) data sets as functional high-level roads, thereby allowing comparative analyses to determine the differences between this functional hierarchy and other hierarchies. OSM road networks have high levels of detail and complex structures, but they also have large numbers of duplicated lines for the same road features, which leads to difficulties and low efficiency when extracting multilane roads using conventional methods based on the analysis and operations of line segments. To overcome these deficiencies, a polygon-based method is proposed that is based on shape analysis and Gestalt theory, which treats polygons surrounded by roads as operating elements. First, shape descriptors are calculated for each polygon in networks and are used for classification. Second, candidate multilane polygons are classified as seeds based on all the polygons used as shape descriptors by a support vector machine. Finally, based on the seed polygons, a region-growing method is proposed that connects and fills the multilane features according to Gestalt theory. An experiment using OSM data from different urban networks verified the validity of the proposed method. The method achieved good and effective extraction performance, regardless of the complexity and duplication of data sets. Thus, a comparative analysis with high-level roads extracted based on road type attributes and structural analysis was performed to demonstrate the differences between the constructed road levels and other hierarchies.

A contextualized and personalized model to predict user interest using location-based social networks

Abstract The accurate determination of user interest in terms of geographic information is essential to numerous mobile applications, such as recommender systems and mobile advertising. User interest is greatly influenced by the usage context and varies across individuals; therefore, a user interest model should incorporate these individual needs and propensities. In this paper, we present an approach to model user interest in a contextualized and personalized manner based on location-based social networks. Multinomial logistic regression is employed to quantify the relationship between user interest and usage context at both the aggregate and individual levels. The proposed approach is tested in a real-world application using Foursquare check-ins issued between February and June 2014 in the three major cities of Chicago, Los Angeles and New York. Results demonstrate the capability of the contextualization process for capturing contextual influences on user interest, and that such influences can be observed at a fine-grained scale at the individual level through the personalization process. The proposed approach therefore enables contextualized and personalized estimation of user interest, thereby contributing useful information to follow-up mobile applications.

Event Identification from Georeferenced Images

Geotagged images (e.g., Flickr) could indicate some social events (e.g., festival, parade, protest, sports, etc.) with spatial, temporal and semantic information. Previous researches relied much on tag frequency, thus some images which do not have clearly tag indicating the occurrence of social event would be missing in this case. One potential way to address this problem or enhance the event identification is to make more use of the spatial and temporal information. In this chapter, we take into consideration the underlying spatio-temporal pattern of social events. Particularly, the influence of urban land use and road on the occurrence of event is considered. Specifically, with a spatio-temporal cluster detection method, we firstly detected spatio-temporal clusters composed of geotagged images. Among these detected S-T clusters, we furthermore attempted to identify social events in terms of a classification model. Specifically, land use and road were considered to generate new kinds of spatial characteristics used as dependent variables incorporated into the classification model. In addition to this, user characteristics (i.e., the number of images and the number of users), spatial and temporal range of images, and the heterogeneity of temporal distribution of images were considered as the other dependent variables for the classification model. Consequently, with a binary logistic regression (BLR) method, we estimated the categories (i.e., ‘event’ or ‘non-event’ one) of the S-T clusters (cases). Experimental results demonstrated the good performance of the method with a total accuracy of 71 %. With the variable selection process of the BLR method, empirical result also indicates that (1) some characteristics (e.g., the distance to the road and the heterogeneity of temporal distribution of images) do not have considerable influence on the occurrence of ‘event’; and (2) compared to the other urban land categories (i.e., residential and recreational land), commercial land has a relatively high influence on the occurrence of ‘event’.