GreenspacePerception (Github) code repository: analysis underlying the publication "How well do NDVI and OpenStreetMap data capture people’s visual perceptions of urban greenspace?"

These notebooks allow to collect spatial data, specifically NDVI, OpenStreetMap and Google Street View metadata, and conduct the quantitative analysis to research how well NDVI and OpenStreetMap data capture what people visually perceive as being urban greenspaces. These notebooks were used in complementation by data collection through a crowdsourcing questionnaire to collect people's perceptions of places presented in Google Street View imagery. The quantitative analysis was followed up by a qualitative analysis, of which the codebook is added to this repository as well as an .xlsx file.This code was developed for three cities in Europe: Barcelona, Rotterdam, and Gothenburg, but can be adapted to fit other geographical contexts.

本套Jupyter笔记本可用于采集空间数据，具体涵盖归一化差分植被指数（Normalized Difference Vegetation Index，NDVI）、开放街道地图（OpenStreetMap）及谷歌街景（Google Street View）元数据，并可开展定量分析，以探究NDVI与开放街道地图数据在准确反映人们视觉感知中的城市绿地方面的表现。本套笔记本还配合众包问卷调查开展数据采集工作，用于收集人们对谷歌街景影像中呈现场景的感知评价。定量分析完成后，本研究还开展了定性分析，本次定性分析所用的编码手册（codebook）及一份.xlsx文件已一并上传至本代码仓库。本套代码最初针对欧洲三座城市——巴塞罗那、鹿特丹与哥德堡开发，但可被适配以适用于其他地理研究场景。