Analysis of Irregular Spatial Data with Machine Learning: Classification of Building Patterns with a Graph Convolutional Neural Network (Short Paper)

Machine learning methods such as Convolutional Neural Network (CNN) are becoming an integral part of scientific research in many disciplines, the analysis of spatial data often failed to these powerful methods because of its irregularity. By using the graph Fourier transform and convolution theorem, we try to convert the convolution operation into a point-wise product in Fourier domain and build a learning architecture of graph CNN for the classification of building patterns. Experiments showed that this method has achieved outstanding results in identifying regular and irregular patterns, and has significantly improved in comparing with other methods

THE VISUALIZATION AND ANALYSIS OF URBAN FACILITY POIS USING NETWORK KERNEL DENSITY ESTIMATION CONSTRAINED BY MULTI-FACTORS

The urban facility, one of the most important service providers is usuallyrepresented by sets of points in GIS applications using POI (Point of Interest) modelassociated with certain human social activities. The knowledge about distributionintensity and pattern of facility POIs is of great significance in spatial analysis,including urban planning, business location choosing and social recommendations.Kernel Density Estimation (KDE), an efficient spatial statistics tool for facilitatingthe processes above, plays an important role in spatial density evaluation, becauseKDE method considers the decay impact of services and allows the enrichment ofthe information from a very simple input scatter plot to a smooth output densitysurface. However, the traditional KDE is mainly based on the Euclidean distance,ignoring the fact that in urban street network the service function of POI is carriedout over a network-constrained structure, rather than in a Euclidean continuousspace. Aiming at this question, this study proposes a computational method of KDEon a network and adopts a new visualization method by using 3-D “wall” surface.Some real conditional factors are also taken into account in this study, such astraffic capacity, road direction and facility difference. In practical works theproposed method is implemented in real POI data in Shenzhen city, China to depictthe distribution characteristic of services under impacts of multi-factors

Formalization and automatic interpretation of map requirements

The map requirements (constraints) can be interpreted by computer programs using their basic embedded functionalities. There are a huge number of constraints available to define the objective of various generalization outputs. Some of the constraints contain high-level knowledge which is not easy to interpret. This needs a huge amount of efforts to implement those constraints. The fact that many constraints have something in common makes the implementation per constraint a waste of resource. The paper proposes to decompose the constraints into more basic units, so as to interpret those constraints more flexible and reuse the already developed functionality as much as possible

A Method for Road Map Construction Based on Trajectory Segmentation and Layer Fusion Using Vehicle Track Line

Traditional methods treat track points (lines) equally to extract road data, which ignores the spatial distribution disparity and restricts its application. Therefore, this paper proposes a new approach for map construction based on trajectory segmentation and layer fusion from vehicle tracks. First, track line subset is selected through the segmentation filtering method based on speed profile. Second, three road map layers are constructed by the Delaunay triangulation through adding different constraints according to the feature of track line subset. Third, buffer method is used to integrate multiple road layers into a single road map. An experiment using taxi GPS traces in Beijing is verified the novel method. The experimental results show that our method can extract road geometry and traffic semantic data considering the heterogeneity of trajectory, and the accuracy of result is improved compared with the two existing methods

The Extraction of Road Boundary from Crowdsourcing Trajectory Using Constrained Delaunay Triangulation

Extraction of road boundary accurately from crowdsourcing trajectory lines is still a hard work.Therefore,this study presented a new approach to use vehicle trajectory lines to extract road boundary.Firstly, constructing constrained Delaunay triangulation within interpolated track lines to calculate road boundary descriptors using triangle edge length and Voronoi cell.Road boundary recognition model was established by integrating the two boundary descriptors.Then,based on seed polygons,a regional growing method was proposed to extract road boundary. Finally, taxi GPS traces in Beijing were used to verify the validity of the novel method, and the results also showed that our method was suitable for GPS traces with disparity density,complex road structure and different time interval

Metaphor Representation and Analysis of Non-Spatial Data in Map-Like Visualizations

Metaphors are rhetorical devices in linguistics that facilitate the understanding of an unfamiliar concept based on a familiar concept. Map representations are usually referred to as the second language of geo-science studies, and the metaphor method could be applied to maps to visualize non-spatial data via spatial element symbols. This study performs a cross-domain application of the map representation method through a map-like visualization. The procedure first designs the map layout with the aid of the Gosper curve. Under the guidance of the Gosper curve, the leaf data items without spatial attributes are arranged on the space plane. Through the bottom-up regional integration, one can complete the construction of the map framework. Then, the cartographic method is used to complete map-like renderings that reflect different data features through diverse visualizations. The map representation advantages, such as overview sensing and multi-scale representation, are also reflected in the map-like visualization and used to identify the characteristics of non-spatial data. Additionally, the electronic map provides a series of interactive convenience features for map observation and analysis. Using the help of map-like visualizations, one can perform a series of analyses of non-spatial data in a new form. To verify the proposed method, the authors conducted map-making experiments and data analyses using real data