Empirical approach to advance the generalisation of multi-scale maps

This dataset is described in the article Empirical approach to advance the generalisation of multi-scale maps; summarized below: Since the advent of the web revolution, maps have undergone profound changes in their usage and design. Pan-scalar map design, in particular, emphasises interaction, enabling users to navigate diverse representations seamlessly across different scales.This study addresses gaps in understanding pan-scalar map specificity. Through empirical experimentation, a multi-scale map exemplar is constructed, focusing on map generalisation quality to reduce disorientation and facilitate information retrieval during exploration. Utilising a combination of generalisation strategies, including automatic and semi-automatic processes, this map serves as a foundational step in formalising new rules of map generalisation in a pan-scalar mapping context. Then, we initiate discussions on the lessons learnt from this exemplar construction. In particular, we discuss what constitutes good generalisation for pan-scalar maps and outline potential approaches for achieving it. It is composed of four folder : All_Data contains all the data before generalisation. MS_Hydro contains the shapefile of generalised hydrographic element for each zoom level. MS_Road contains the shapefile of generalised urban road elements for each zoom level. Styles contains the style file to apply to create our map.本数据集对应论文《推进多尺度地图泛化的实证研究（Empirical approach to advance the generalisation of multi-scale maps）》，内容摘要如下： 自Web变革浪潮兴起以来，地图的使用场景与设计范式均发生了深刻变革。其中，全尺度地图（pan-scalar map）设计尤为注重交互体验，可支持用户在不同比例尺间无缝流转，浏览多样化的地图表达形式。本研究填补了当前对全尺度地图特异性认知的空白。通过实证实验，我们构建了一款多尺度地图示例，核心聚焦于地图泛化（map generalisation）质量，旨在降低用户探索过程中的方位迷失感，提升信息检索效率。本地图集成自动与半自动等多种泛化策略，为全尺度地图场景下地图泛化新规则的规范化奠定了基础。随后，我们围绕该示例构建过程中的经验教训展开研讨，重点探讨全尺度地图的优质泛化标准，并梳理了实现该标准的可行路径。 本数据集包含四个文件夹： - All_Data：存储所有泛化前的原始数据。 - MS_Hydro：存储各缩放层级下已泛化水文要素的矢量文件（shapefile）。 - MS_Road：存储各缩放层级下已泛化城市道路要素的矢量文件（shapefile）。 - Styles：存储用于生成目标地图的样式文件。