Title
Finding Optimal Sequences for Area Aggregation—A⋆ vs. Integer Linear Programming
Abstract
AbstractTo provide users with maps of different scales and to allow them to zoom in and out without losing context, automatic methods for map generalization are needed. We approach this problem for land-cover maps. Given two land-cover maps at two different scales, we want to find a sequence of small incremental changes that gradually transforms one map into the other. We assume that the two input maps consist of polygons, each of which belongs to a given land-cover type. Every polygon on the smaller-scale map is the union of a set of adjacent polygons on the larger-scale map.In each step of the computed sequence, the smallest area is merged with one of its neighbors. We do not select that neighbor according to a prescribed rule but compute the whole sequence of pairwise merges at once, based on global optimization. We have proved that this problem is NP-hard. We formalize this optimization problem as that of finding a shortest path in a (very large) graph. We present the A⋆ algorithm and integer linear programming to solve this optimization problem. To avoid long computing times, we allow the two methods to return non-optimal results. In addition, we present a greedy algorithm as a benchmark. We tested the three methods with a dataset of the official German topographic database ATKIS. Our main result is that A⋆ finds optimal aggregation sequences for more instances than the other two methods within a given time frame.
Year
DOI
Venue
2020
10.1145/3409290
ACM Transactions on Spatial Algorithms and Systems
Keywords
DocType
Volume
Continuous map generalization, land-cover area, type change, compactness, shortest path
Journal
7
Issue
ISSN
Citations 
1
2374-0353
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Dongliang Peng1116.41
Alexander Wolff222222.66
Jan-Henrik Haunert317919.32