This research analyzes the spatial pattern of large-scale stores based on space syntax theory and explores the correlation
between the variations in syntax accessibility and the spatial pattern of large-scale stores. This research develops a
framework of spatial topology analysis based on the space syntax theory, which includes the following modifications: the
trail to break the traditional long axial line network of space syntax and apply this partitioned network in the topological
analysis; the trail to analyze the bus route network; By taking both the syntax accessibility of road and bus network into
consideration, we produce the scopes of urban syntax centers of city level, local level and sub local level respectively. In
the analysis of the retail distribution pattern, the city level, local level and sub local level urban retail centers are
suggested respectively according to the spatial distributions of the quantity and scale of the retail stores. The spatial
distribution pattern of each retail format is studied as spatial correlations between the retail locations and the urban space
syntax centers based on a case study in Wuhan, China. The Space Syntax can be a useful tool to explain the allocation
logic of urban retail space in large cities. We suggest to apply the partitioned transportation network instead of the
traditional long axial line network.
With mobile terrestrial laser scanning, laser point clouds of large urban areas can be acquainted rapidly during normal
speed driving. Classification of the laser points is beneficial to the city reconstruction from laser point cloud, but a
manual classification process can be rather time-consuming due to the huge amount of laser points. Although the pulse
return is often used to automate classification, it is only possible to distinguish limited types such as vegetation and
ground. In this paper we present a new method which classifies mobile terrestrial laser point clouds using only
coordinate information. First, a point of a whole urban scene is segmented, and geometric properties of each segment are
computed. Then semantic constraints for several object types are derived from observation and knowledge. These
constraints concern not only geometric properties of the semantic objects, but also regulate the topological and
hierarchical relations between objects. A search tree is formulated from the semantic constraints and applied to the laser
segments for interpretation. 2D map can provide the approximate locations of the buildings and roads as well as the
roads' dominant directions, so it is integrated to reduce the search space. The applicability of this method is demonstrated
with a Lynx data of the city Enschede and a Streetmapper data of the city Esslingen. Four object types: ground, road,
building façade, and traffic symbols, are classified in these data sets.
KEYWORDS: Geographic information systems, Spatial analysis, Databases, Data modeling, Buildings, Roads, Data acquisition, Systems modeling, Computing systems, Information technology
Along with the development of Digital City and its practical applications, various urban geographic information systems
(UGIS) has contributed enormously to the government's information services and decision-making process. However,
the data redundancy has become a practical issue, which makes planners difficult to derive required data effectively from
a large amount of data from UGIS databases. Based on comparison of the planning support system (PSS) with other
UGIS, requirements of planning support based on GIS is discussed. Aiming at providing effective data and methods for
urban planning, the paper explored GIS-based approaches for planning support in which spatial analysis played an
important role and put forward a technological model to analyze urban problems in a dynamic environment to provide
ideas and hints for further development of urban planning theories and practice.
In recent years, terrestrial laser scanner (TLS) has become a popular data acquisition tool for producing irregularly-spaced
point clouds as well as airborne laser scanning (ALS). Automated detection of structures (roof and ground etc.) based on
the point cloud analysis of buildings has become increasingly important. One of the most demanding tasks in TLS is the
filtering of the ground and roofs in TLS point clouds. This paper proposes a method for detecting buildings' structures
from an irregularly-spaced point-cloud. This method is consisted of segmentation and classification. As the previously
developed the segmentation methods can not be applied to it directly, it has to perform twice pre-filtration so as to proceed
to further calculation for segmentation and classification. More importantly the algorithm is extensible and future work
will further strengthen the algorithm.
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