节庆活动对田园综合体客流与消费弹性预测模型数据

该数据适用于田园综合体运营方、文旅策划公司、农业旅游开发企业，在规划节庆活动、制定运营与定价策略时使用。适用条件为田园综合体具备基础场地设施（如活动场地、餐饮配套）与数据记录能力，需科学预判活动效果；适用范围覆盖全国各类以农业体验、乡村度假为核心的田园综合体。
可解决三大核心问题：一是预测不同节庆活动（如丰收节、亲子节）的日均客流，辅助提前调配人力（如服务人员）与物资（如停车位、食材）；二是精准测算客流与消费的弹性关系，明确客流增长对消费收入的拉动幅度，为门票定价、农产品促销活动提供依据；三是评估活动投入产出比，帮助筛选高性价比活动类型，降低盲目举办活动的风险。1.数据采集：数据来源于系统
2.数据处理：数据清洗：去除重复记录（如同一活动多次上报的重复数据）、错误数据（如客流为负、消费金额异常偏高）及关键字段缺失（如无历史基准数据）的无效记录；
标准化处理：对“宣传投入”“活动时长”等数值型数据，采用“最小值-最大值”法转换为0-1标准化数据（如将5-15万元的宣传投入，统一转换为0-1区间数值），确保数据量级一致。
3.算法加工：采用“线性加权+实际增长率测算”的简化模型，核心步骤如下：
因子权重设定：结合行业经验与历史数据相关性，为核心影响因子赋予固定权重，确保计算逻辑简洁易懂：
活动时长（0.2）：时长适中（5-7天）权重更高（过长易导致客流分散）。
宣传投入（0.3）：投入越高，客流拉动效应越强；
交通站点数（0.2）：站点越多，客群可达性越好；
周边人口密度（0.3）：人口越密集，潜在客群基数越大；
客流预测公式：
预测节庆日均客流=历史无节庆日均客流×[1+（活动时长权重×0.2+宣传投入权重×0.3+交通站点数×0.2+人口密度权重×0.3）]；
消费弹性系数计算：消费弹性系数=节庆期间消费增长率/节庆期间客流增长率；
其中：
节庆期间客流增长率=（实际节庆日均客流-历史无节庆日均客流）/历史无节庆日均客流×100%；
节庆期间消费增长率=（节庆客群均消-历史无节庆客群均消）/历史无节庆客群均消×100%；
预测准确度评分计算：
预测准确度评分=（1-|预测节庆日均客流-实际节庆日均客流|/实际节庆日均客流）×10
（直接换算为1-10分制，取整数评分，简化计算逻辑）；
4.数据分类分级：依据“消费弹性系数”与“预测准确度评分（1-10分）”，将运营建议等级清晰划分为优、中、差三类：
优：当消费弹性系数≥0.3且预测准确度评分≥8分，说明活动对消费拉动强、预测精准度高，建议优先举办；
中：当消费弹性系数处于0.2（含）-0.3（不含）区间且预测准确度评分处于6（含）-7（含）区间（双区间需同时满足），代表活动有一定消费拉动效应，但预测精准度待提升，需优化促销策略（如农产品满减、套票优惠等）；
差：若消费弹性系数＜0.2或预测准确度评分＜6分（满足任一条件即可），意味着活动对消费拉动弱，或预测可信度低，不建议举办。

This dataset is designed for pastoral complex operators, cultural and tourism planning companies, and agricultural tourism development enterprises when planning festival activities and formulating operation and pricing strategies.
Its applicable conditions require that the pastoral complex has basic venue facilities (such as event venues, catering supporting facilities) and data recording capabilities, and needs to scientifically predict event effects; its applicable scope covers all types of pastoral complexes across China with agricultural experience and rural vacation as their core.

This dataset addresses three core issues:
1. Predict the daily average passenger flow of different festival activities (e.g., Harvest Festival, Parent-child Festival), and assist in the advance allocation of labor (e.g., service staff) and materials (e.g., parking spaces, food ingredients);
2. Accurately calculate the elastic relationship between passenger flow and consumption, clarify the driving effect of passenger flow growth on consumption revenue, and provide a basis for ticket pricing and agricultural product promotion activities;
3. Evaluate the input-output ratio of activities, help screen cost-effective activity types, and reduce the risk of blindly holding events.

1. Data Collection: The dataset is sourced from internal systems.
2. Data Processing:
a. Data Cleaning: Remove invalid records including duplicate records (e.g., duplicate data reported multiple times for the same activity), erroneous data (e.g., negative passenger flow, abnormally high consumption amounts), and records with missing key fields (e.g., no historical baseline data);
b. Standardization Processing: For numerical data such as "promotion investment" and "activity duration", use the min-max normalization method to convert them into 0-1 standardized data (e.g., uniformly convert promotion investment ranging from 50,000 to 150,000 yuan into values in the 0-1 interval) to ensure consistent data magnitude.
3. Algorithm Processing: Adopt a simplified model of "linear weighting + actual growth rate calculation", with the core steps as follows:
Factor Weight Setting: Combine industry experience and historical data correlation to assign fixed weights to core influencing factors, ensuring the calculation logic is concise and easy to understand:
- Activity duration (0.2): Moderate duration (5-7 days) has a higher weight (overlong duration easily leads to scattered passenger flow);
- Promotion investment (0.3): The higher the investment, the stronger the passenger flow driving effect;
- Number of transportation stops (0.2): The more stops, the better the accessibility of passenger groups;
- Surrounding population density (0.3): The denser the population, the larger the potential passenger group base;
Passenger Flow Forecasting Formula:
Predicted daily average passenger flow of festivals = Historical daily average passenger flow without festivals × [1 + (activity duration weight × 0.2 + promotion investment weight × 0.3 + number of transportation stops × 0.2 + population density weight × 0.3)]
Consumption Elasticity Coefficient Calculation:
Consumption elasticity coefficient = Consumption growth rate during festivals / Passenger flow growth rate during festivals
Where:
Passenger flow growth rate during festivals = (Actual daily average passenger flow during festivals - Historical daily average passenger flow without festivals) / Historical daily average passenger flow without festivals × 100%
Consumption growth rate during festivals = (Average consumption per passenger during festivals - Historical average consumption per passenger without festivals) / Historical average consumption per passenger without festivals × 100%
Prediction Accuracy Score Calculation:
Prediction accuracy score = (1 - |Predicted daily average passenger flow of festivals - Actual daily average passenger flow during festivals| / Actual daily average passenger flow during festivals) × 10
(Directly converted to a 1-10 scale with integer scores to simplify the calculation logic)
4. Data Classification and Grading: Based on the "consumption elasticity coefficient" and "prediction accuracy score (1-10 points)", the operation recommendation levels are clearly divided into three categories: excellent, medium, and poor:
- Excellent: When the consumption elasticity coefficient ≥ 0.3 and the prediction accuracy score ≥ 8 points, it indicates that the activity has a strong consumption driving effect and high prediction accuracy, and it is recommended to hold it first;
- Medium: When the consumption elasticity coefficient is in the range of 0.2 (inclusive) to 0.3 (exclusive) and the prediction accuracy score is in the range of 6 (inclusive) to 7 (inclusive) (both intervals must be satisfied at the same time), it means that the activity has a certain consumption driving effect, but the prediction accuracy needs to be improved, and the promotion strategy needs to be optimized (e.g., agricultural product full-reduction discounts, package ticket discounts, etc.);
- Poor: If the consumption elasticity coefficient < 0.2 or the prediction accuracy score < 6 points (either condition is satisfied), it means that the activity has a weak consumption driving effect or low prediction credibility, and it is not recommended to hold it.

搜集汇总

背景与挑战

背景概述

该数据集专为田园综合体运营方设计，用于预测节庆活动期间的日均客流和消费弹性关系，辅助资源调配和定价策略。它采用线性加权模型结合历史数据，计算客流预测和消费弹性系数，并根据结果将运营建议分为优、中、差等级，以科学评估活动投入产出比，降低运营风险。