天津市滨海新区线下零售门店选址分析数据

选址模型通过天津市滨海新区宏观数据、友商数据、商业氛围等20+维度，使用机器学习算法和H3算法将指定区域切割为若干六边形，在每个六边形中选出最佳开店位置。同时还可以对已开设的老店进行评估，判断门店位置是否合理，科学的辅助线下零售门店选址，有助于拥有实体店的企业实现智能门店选址。1、数据采集：整理并清洗天津市滨海新区门店特征数据集（经纬度和对应的特征（宏观经济、商业氛围等））
2、建立模型：
（1）将清洗的数据进行EDA（电子设计自动化）、特征工程；
（2）选取候选点，通过h3（Uber）算法将区域细分为越来越小的六边形，形成候选点，并生成特征数据输入到机器学习模型；
（3）选择LightGBM算法并训练开店选址评分的机器学习模型；
3、数据应用：输出候选点的对应的评分，选取Top_N候选点作为推荐点，搜索Top_N推荐点周边的POI信息，输出推荐位置。

The site selection model utilizes over 20 dimensions including macroeconomic data of Tianjin Binhai New Area, competitor data, and business atmosphere, cuts the specified area into multiple hexagons via machine learning algorithms and the H3 algorithm, and selects the optimal store opening location in each hexagon. Additionally, it can evaluate existing physical stores to judge whether their locations are reasonable, scientifically assisting offline retail store site selection and helping enterprises with physical stores realize intelligent store site selection.
1. Data Collection: Organize and clean the store feature dataset of Tianjin Binhai New Area (including longitude, latitude and corresponding features such as macroeconomic conditions, business atmosphere, etc.)
2. Model Establishment:
(1) Conduct EDA (Electronic Design Automation) and feature engineering on the cleaned data;
(2) Select candidate points: subdivide the target area into increasingly smaller hexagons via the H3 (Uber) algorithm to generate candidate points, and generate feature data as input for the machine learning model;
(3) Select the LightGBM algorithm to train a machine learning model for store location selection scoring;
3. Data Application: Output the corresponding scores of the candidate points, select the Top_N candidate points as recommended locations, retrieve POI information around the Top_N recommended locations, and output the recommended positions.