Global road geospatial data on postal code level, optimized for Machine learning tasks | Sample

This dataset contains the actual data about roads in the 1 k, radius from the postal code area aggregated on the postal code level from the OpenStreetMap: the length of motorways, the length of railways, the length of primary roads etc.

This data product has been optimized specifically for machine learning and prediction tasks, such as credit scoring, when you know the borrower's address. Or to forecast demand in a specific location. It includes pre-calculated machine learning features that are ready to use.

🔎 Simple Drag & Drop Streamlit App to find relevant data listings & fields for your ML task: https://upgini.com/upgini-widget

本数据集包含以邮政编码区域为聚合粒度、取自OpenStreetMap（开放街道地图）的、覆盖各邮政编码区域周边1公里半径范围内的道路实测数据，涵盖高速公路长度、铁路长度、主干道长度等内容。
本数据产品专为机器学习与预测任务优化设计，例如已知借款人地址时的信用评分任务，或是特定区域的需求预测任务。其内置预计算完成、可直接投入使用的机器学习特征。
🔎 简易拖拽式Streamlit应用，可帮您查找适配机器学习任务的相关数据条目与字段：https://upgini.com/upgini-widget