University campuses consists of many buildings within a large area managed by a single organization. Like 3D city modeling, a 3D model of campuses can be utilized to provide a better foundation for planning, navigation and management of buildings. This study approaches 3D modeling of the UTM campus by utilizing data from aerial photos and site observations. The 3D models of buildings were drawn from building footprints in SketchUp and converted to CityGML using FME software. The CityGML models were imported into a geodatabase using 3DCityDB and visualized in Cesium. The resulting 3D model of buildings was in CityGML format level of detail 2, consisting of ground, wall and roof surfaces. The 3D models were positioned with real-world coordinates using the geolocation function in SketchUp. The non-spatial attributes of the 3D models were also stored in a database managed by PostgreSQL. While the methodology demonstrated in this study was found to be able to create LoD2 building models. However, issues of accuracy arose in terms of building details and positioning. Therefore, higher accuracy data, such as point cloud data, should produce higher LoD models and accurate positioning.

Smart city is a connection of physical and social infrastructure together with the information technology to leverage the collective intelligence of the city. Cities will build huge data centres. These data are collected from sensors, social media, and legacy data sources. In order to be smart, cities needs data analysis to identify infrastructure that needs to be improved, city planning and predictive analysis for citizen safety and security. However, no matter how much smart city focus on the updated technology, data do not organize themselves in a database. Such tasks require a sophisticated database structure to produce informative data output. Furthermore, increasing number of smart cities and generated data from smart cities contributes to current phenomenon called big data. These large and complex data collections would be difficult to process using regular database management tools or traditional data processing applications. There are multiple challenges for big data, including visualization, mining, analysis, capture, storage, search, and sharing. Efficient data analysis mechanisms are necessary to search and extract valuable patterns and knowledge through the big data of smart cities. In this paper, we present a technique of three-dimensional data analytics using dendrogram clustering approach. Data will be organized using this technique and several output and analyses are carried out to proof the efficiency of the structure for three – dimensional data analytics in smart city.

Keywords: Geomarketing, Market Segmentation, Geo-Clustering, 3D Spatial Database, 3D GIS, Information Retrieval Geomarketing is a discipline which uses geographic information in the process of planning and implementation of marketing activities. It can be used in any aspect of the marketing such as price, promotion or geo targeting. The analysis of geomarketing data use a huge data pool such as location residential areas, topography, it also analyzes demographic information such as age, genre, annual income and lifestyle. This information can help users to develop successful promotional campaigns in order to achieve marketing goals. One of the common activities in geomarketing is market segmentation. The segmentation clusters the data into several groups based on its geographic criteria. To refine the search operation during analysis, we proposed an approach to cluster the data using a clustering algorithm. However, with the huge data pool, overlap among clusters may happen and leads to inefficient analysis. Moreover, geomarketing is usually active in urban areas and requires clusters to be organized in a three-dimensional (3D) way (i.e. multi-level shop lots, residential apartments). This is a constraint with the current Geographic Information System (GIS) framework. To avoid this issue, we proposed a combination of market segmentation based on geographic criteria and clustering algorithm for 3D geomarketing data management. The proposed approach is capable in minimizing the overlap region during market segmentation. In this paper, geomarketing in urban area is used as a case study. Based on the case study, several locations of customers and stores in 3D are used in the test. The experiments demonstrated in this paper substantiated that the proposed approach is capable of minimizing overlapping segmentation and reducing repetitive data entries. The structure is also tested for retrieving the spatial records from the database. For marketing purposes, certain radius of point is used to analyzing marketing targets. Based on the presented tests in this paper, we strongly believe that the structure is capable in handling and managing huge pool of geomarketing data. For future outlook, this paper also discusses the possibilities of expanding the structure.

The advantages of three dimensional (3D) city models can be seen in various applications including photogrammetry, urban and regional planning, computer games, etc.. They expand the visualization and analysis capabilities of Geographic Information Systems on cities, and they can be developed using web standards. However, these 3D city models consume much more storage compared to two dimensional (2D) spatial data. They involve extra geometrical and topological information together with semantic data. Without a proper spatial data clustering method and its corresponding spatial data access method, retrieving portions of and especially searching these 3D city models, will not be done optimally. Even though current developments are based on an open data model allotted by the Open Geospatial Consortium (OGC) called CityGML, its XML-based structure makes it challenging to cluster the 3D urban objects. In this research, we propose an opponent data constellation technique of space-filling curves (3D Hilbert curves) for 3D city model data representation. Unlike previous methods, that try to project 3D or n-dimensional data down to 2D or 3D using Principal Component Analysis (PCA) or Hilbert mappings, in this research, we extend the Hilbert space-filling curve to one higher dimension for 3D city model data implementations. The query performance was tested using a CityGML dataset of 1,000 building blocks and the results are presented in this paper. The advantages of implementing space-filling curves in 3D city modeling will improve data retrieval time by means of optimized 3D adjacency, nearest neighbor information and 3D indexing. The Hilbert mapping, which maps a subinterval of the [0, 1] interval to the corresponding portion of the d-dimensional Hilbert’s curve, preserves the Lebesgue measure and is Lipschitz continuous. Depending on the applications, several alternatives are possible in order to cluster spatial data together in the third dimension compared to its clustering in 2D.

In spatial science, the relationship between spatial objects is considered to be a vital element. Currently, 3D objects are often used for visual aids, improving human insight, spatial observations, and spatial planning. This scenario involves 3D geometrical data handling without the need for topological information. Nevertheless, in the near future, users will shift to more complex queries corresponding to the existing 2D spatial approaches. Therefore, having 3D spatial objects without having these relationships or topology is impractical for 3D spatial analysis queries. In this paper, we present a new method for creating topological information that we call the Compact Abstract Cell Complexes (CACC) data structure for 3D spatial objects. The idea is to express in the most compact way the topology of a model in 3D (or more generally in nD) without requiring the topological space to be discrete or geometric. This is achieved by storing all the atomic cycles through the models (null combinatorial homotopy classes). The main idea here is to store the atomic paths through the models as an ant experiences topology: each time the ant perceives a previous trace of pheromone, it knows it has completed a cycle. The main advantage of this combinatorial topological data structure over abstract simplicial complexes is that the storage size of the abstract cell cycles required to represent the geometric topology of a model is far lower than that for any of the existing topological data structures (including abstract simplicial cell cycles) required to represent the geometric decomposition of the same model into abstract simplicial cells. We provide a thorough comparative analysis of the storage sizes for the different topological data structures to sustain this.