Exploring spatio-temporal patterns of OpenStreetMap (OSM) contributions in heterogeneous urban areas

Abstract: The potential of intrinsic parameters to estimate geospatial data quality on Voluntary Geographic Information (VGI) platforms is a recurrent theme in Cartography. The spatial-temporal distribution in these platforms is very heterogeneous, depending on several factors such as input availability, number, and motivation of volunteers, especially in developing countries. The most recent approaches have been aiming to detail temporal patterns as an additional measure of quality in VGI. This research proposes a methodology to identify and analyze the behavior of the contribution parameters over time (2007-2022) of the OSM platform and differentiates the influences that affect its growth. Part of the Metropolitan region of Curitiba was the study area, subdivided into 1 x 1 km cells. The cumulative growth of contributions was calculated and later adjusted using a Logistic Regression. The obtained parameters made it possible to identify abruptly growing cells caused by external data import, mass contributions, or collective mapping activities. In addition, heterogeneity in the growth of the data available in OSM over time was evident. Furthermore, the proposed methodology promoted the investigation of a new indicator of intrinsic quality based on modelling the spatiotemporal evolution of OSM feature insertions.

摘要：在地图学（Cartography）领域，利用内在参数评估志愿地理信息（Voluntary Geographic Information, VGI）平台的地理空间数据质量，是长期受到关注的研究议题。此类平台的时空分布呈现极强的异质性，其差异受数据输入可得性、志愿者规模与参与动机等多重因素影响，在发展中国家这一特征尤为突出。当前最新的研究方向已转向细化时空分布模式，将其作为VGI质量评估的补充指标。本研究提出一套方法论体系，用于识别并分析开放街道地图（OpenStreetMap, OSM）平台2007至2022年间贡献参数随时间的变化特征，并厘清影响其增长的各类驱动因素。研究区域选取库里蒂巴都会区的部分范围，将其划分为1×1千米的规则网格单元。首先计算贡献量的累积增长曲线，随后通过逻辑回归（Logistic Regression）模型进行拟合修正。基于推导得到的参数，可精准识别出因外部数据导入、批量贡献或集体制图活动而出现爆发式增长的网格单元。此外，研究结果清晰验证了OSM平台可用数据的增长随时间呈现出显著的异质性特征。最后，本研究提出的方法论，为基于OSM要素插入的时空演化建模的新型内在质量指标研究提供了可行路径。