安徽省网格化机会挖掘数据

将安徽省所有街道（镇）称作网格，共计1680个网格。创建每个网格的宏观经济、商业氛围、厨电行业、住宅区等维度的数据集。创建并训练机器学习、深度学习算法模型。输出每个网格是否应该开店，开多少家店，并输出安徽省所有网格对开店机会的预测评分；实地操盘手可根据预测评分高低，进行网格化工作策略制定、资源分配优化及策略纠偏，同行业也可通过网格化机会挖掘算法预测开店机会。1、数据收集：整理并清洗安徽省所有网格（1680街道/镇）的前一年份第三产业GDP、洗衣店、集成灶、烟酒专卖店、房产中介、竞品、宠物诊所、商场、住宅区、区县_乡村总数量、格均社消额、格均土地面积等12个维度的所有数据。
2、模型建立：针对有店的网格数据，建立RF（随机森林）、LGBM（梯度提升决策树）、MLP（人工神经网络）模型并进行集成学习，训练出12个维度与店之间的得分模型：网格得分=前一年份第三产业GDP*7.5%+洗衣店*13.9%+集成灶*6.6%+烟酒专卖店*6.2%+房产中介*1.7%+竞品*30.1%+宠物诊所*13.7%+商场*7.0%+住宅区*2.4%+区县_乡村总数量*3.8%+格均社消额*2.8%+格均土地面积*4.3%
3、通过训练好的模型对全部网格进行预测评分并输出无店网格的开店机会及空间：
网格得分<51，则无开店机会；50<网格得分<108，则有1个开店机会；107<网格得分<164，则有2个开店机会；网格得分>163，则有多个开店机会。

All streets and towns in Anhui Province are uniformly defined as grids, with a total of 1680 grids. A dataset is constructed for each grid, covering dimensions such as macroeconomics, business environment, kitchen appliance industry, residential areas, and others. Machine learning and deep learning algorithm models are developed and trained. The outputs include the suitability of each grid for opening stores, the optimal number of stores to establish, and the predicted store opening opportunity scores for all grids in Anhui Province. On-site operators can formulate grid-based work strategies, optimize resource allocation and adjust operational strategies based on the predicted scores; peers in the same industry can also utilize the grid-based opportunity mining algorithm to predict store opening opportunities.
1. Data Collection: Collect and clean data across 12 dimensions for all 1680 grids (streets/towns) in Anhui Province from the previous year, including: tertiary industry GDP, laundromats, integrated stoves, tobacco and alcohol specialty stores, real estate agencies, competing products, pet clinics, shopping malls, residential areas, total number of districts/counties and rural areas, average grid social consumer goods retail sales, and average grid land area.
2. Model Establishment: For grid data with existing stores, establish RF (Random Forest), LGBM (Light Gradient Boosting Machine), and MLP (Multi-Layer Perceptron, an Artificial Neural Network) models and perform ensemble learning, to train a scoring model that links the 12 dimensions and store counts: Grid Score = 7.5% × Previous Year Tertiary Industry GDP + 13.9% × Laundromats + 6.6% × Integrated Stoves + 6.2% × Tobacco and Alcohol Specialty Stores + 1.7% × Real Estate Agencies + 30.1% × Competing Products + 13.7% × Pet Clinics + 7.0% × Shopping Malls + 2.4% × Residential Areas + 3.8% × Total Number of Districts/Counties and Rural Areas + 2.8% × Average Grid Social Consumer Goods Retail Sales + 4.3% × Average Grid Land Area.
3. Use the trained model to conduct predictive scoring for all grids and output store opening opportunities and potential space for grids with no existing stores:
- If Grid Score < 51: No store opening opportunity;
- If 50 < Grid Score < 108: 1 store opening opportunity;
- If 107 < Grid Score < 164: 2 store opening opportunities;
- If Grid Score > 163: Multiple store opening opportunities.

搜集汇总

数据集介绍

特点

该数据集提供了安徽省1680个网格的详细经济和社会数据，用于训练机器学习模型预测开店机会。数据集每年更新，包含12个关键维度的信息，如第三产业GDP、商业设施数量等，支持网格化工作策略的制定和资源分配优化。