Methods for calculation the spatial distribution of the territory membership to the urbanized based on the hybrid neural network

IF 1.4 Q3 GEOGRAPHY
Bogdan Gats, Yaroslav Vyklyuk, A. Horbovyy
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引用次数: 0

Abstract

This paper presents the method for calculation of the spatial distribution of the territory membership to the urbanized (TMU)—the value that puts the degree of suitability for development (tourism development) in accordance with each node of the grid of the study area within (0, 1). The method is based on a hybrid neural network training. It allows us to assess all kinds of territories for tourist infrastructure development capabilities with relation to attractiveness and provide results visualization on the geographical information system (GIS) maps. The developed algorithm of picking and conversion of geospatial data from GIS for knowledge base generation uses attractors coordinates (vectors of roads, a city center, a railway station) and random points of the explored area, makes possible distance calculation between them (on the road to the attractor and Manhattan distance) and following conversion to the ASCII file, that allows unifying input parameters of the set of models for forecasting development of tourist infrastructure objects. The paper studied typical tourist towns of the Ukrainian Carpathians.
基于混合神经网络的城市版图隶属度空间分布计算方法
本文提出了一种计算领土城市化隶属度(TMU)空间分布的方法,即将研究区域网格中每个节点的发展适宜度(旅游发展)置于(0,1)范围内的值。该方法基于混合神经网络训练。它使我们能够评估与吸引力相关的各种旅游基础设施开发能力,并在地理信息系统(GIS)地图上提供结果可视化。开发的从GIS中提取和转换地理空间数据用于知识库生成的算法使用吸引点坐标(道路,城市中心,火车站的向量)和探索区域的随机点,使它们之间的距离计算(在道路上到吸引点和曼哈顿距离)成为可能,并将其转换为ASCII文件,从而允许统一输入参数的模型集用于预测旅游基础设施对象的发展。本文以乌克兰喀尔巴阡山脉的典型旅游城镇为研究对象。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
2.00
自引率
16.70%
发文量
16
审稿时长
12 weeks
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