Next Article in Journal
Hierarchical Instance Recognition of Individual Roadside Trees in Environmentally Complex Urban Areas from UAV Laser Scanning Point Clouds
Previous Article in Journal
Spatio-Temporal Relationship between Land Cover and Land Surface Temperature in Urban Areas: A Case Study in Geneva and Paris
Previous Article in Special Issue
Index for the Consistent Measurement of Spatial Heterogeneity for Large-Scale Land Cover Datasets
Open AccessTechnical Note

PolySimp: A Tool for Polygon Simplification Based on the Underlying Scaling Hierarchy

by Ding Ma 1,2, Zhigang Zhao 1,2,*, Ye Zheng 1, Renzhong Guo 1,2 and Wei Zhu 1
1
Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
2
Key Laboratory of Urban Land Resources Monitoring and Simulation, MNR, Shenzhen 518060, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(10), 594; https://doi.org/10.3390/ijgi9100594
Received: 1 September 2020 / Revised: 9 October 2020 / Accepted: 9 October 2020 / Published: 10 October 2020
(This article belongs to the Special Issue Geographic Complexity: Concepts, Theories, and Practices)
Map generalization is a process of reducing the contents of a map or data to properly show a geographic feature(s) at a smaller extent. Over the past few years, the fractal way of thinking has emerged as a new paradigm for map generalization. A geographic feature can be deemed as a fractal given the perspective of scaling, as its rough, irregular, and unsmooth shape inherently holds a striking scaling hierarchy of far more small elements than large ones. The pattern of far more small things than large ones is a de facto heavy tailed distribution. In this paper, we apply the scaling hierarchy for map generalization to polygonal features. To do this, we firstly revisit the scaling hierarchy of a classic fractal: the Koch Snowflake. We then review previous work that used the Douglas–Peuker algorithm, which identifies characteristic points on a line to derive three types of measures that are long-tailed distributed: the baseline length (d), the perpendicular distance to the baseline (x), and the area formed by x and d (area). More importantly, we extend the usage of the three measures to other most popular cartographical generalization methods; i.e., the bend simplify method, Visvalingam–Whyatt method, and hierarchical decomposition method, each of which decomposes any polygon into a set of bends, triangles, or convex hulls as basic geometric units for simplification. The different levels of details of the polygon can then be derived by recursively selecting the head part of geometric units and omitting the tail part using head/tail breaks, which is a new classification scheme for data with a heavy-tailed distribution. Since there are currently few tools with which to readily conduct the polygon simplification from such a fractal perspective, we have developed PolySimp, a tool that integrates the mentioned four algorithms for polygon simplification based on its underlying scaling hierarchy. The British coastline was selected to demonstrate the tool’s usefulness. The developed tool can be expected to showcase the applicability of fractal way of thinking and contribute to the development of map generalization. View Full-Text
Keywords: cartographical generalization; scaling of polygonal features; fractal analysis; head/tail breaks cartographical generalization; scaling of polygonal features; fractal analysis; head/tail breaks
Show Figures

Figure 1

MDPI and ACS Style

Ma, D.; Zhao, Z.; Zheng, Y.; Guo, R.; Zhu, W. PolySimp: A Tool for Polygon Simplification Based on the Underlying Scaling Hierarchy. ISPRS Int. J. Geo-Inf. 2020, 9, 594.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop