The Dianping Merchant and Rating Database is a structured database constructed based on publicly available data from Dianping, China's leading lifestyle service platform. This database comprehensively covers merchant information across multiple cities nationwide, spanning various consumption sectors such as dining, shopping, and leisure entertainment. It includes 28 fields encompassing basic merchant information, geographic coordinates, classification systems, operational status, user reviews, consumption level, and service facilities. With broad temporal coverage and high spatial resolution, this dataset holds significant value for academic research and commercial analysis, providing a robust data foundation for researchers to explore urban commercial spatial structures, consumer behavior preferences, and service industry quality assessments.
Data Features:
- Spatial Hierarchy Integrity: Provides four-level spatial identifiers ("province–city–district–business district") supplemented by precise latitude and longitude coordinates, supporting multi-scale geospatial analysis. Suitable for studies on urban commercial agglomeration, regional consumption capacity evaluation, and identification of offline service radiation ranges.
- Systematic Classification System: Built on a three-tier classification coding system ("major category–medium category–minor category"), covering dimensions from macro-industry categories to micro-business types. Enables cross-industry comparisons and analysis of segmented market competition patterns, featuring a clear classification structure and standardized coding.
- Multidimensional Merchant Information: Beyond basic details (name, address, phone number), includes fields such as brand affiliation, review count, average consumption, store rating, business hours, and service facilities. Comprehensively characterizes merchant operational features and service quality, combining static attributes with dynamic performance metrics.
- High Data Reliability: Data sourced from Dianping's public pages, ensuring consistency through unified collection and cleaning procedures.
Potential Application Scenarios:
- Academic Research: Applicable to urban geography (commercial spatial structure evolution), consumer economics (price sensitivity vs. rating correlation), and management science (service quality and user review mechanisms). High-precision geocoding supports GIS spatial analysis (e.g., hotspot identification and spatial autoregression).
- Commercial Services: Assists enterprises in site selection, competitor monitoring, and brand influence evaluation. Identifies high-growth potential merchants based on review volume and rating metrics, providing data support for investment decisions and market entry strategies.
- Policy Optimization: Offers evidence for public policies such as urban commercial planning, consumption stimulus policy evaluation, and SME support. Identifies service industry weak spots by analyzing spatial distribution and performance across merchant types, optimizing public service resource allocation.
The Dianping Merchant and Rating Data systematically integrates core merchant information from the Dianping platform in a multidimensional, high-precision manner, encompassing spatial, categorical, and operational performance dimensions. Its well-designed fields, clear classification system, and reliable data quality make it a rare foundational data resource for academic research, commercial analysis, and policy formulation.
Time Range
2024 Edition
Field Display
Sample Data
Relevant Literature
- LI Bing, GUO Dongmei, LIU Siqin, 2019, "City Size, Population Structure, and Non-tradable Diversity—Big Data Analysis Based on Dianping.com," Economic Research 1(7):45-58 [In Chinese].
- LU Xianghua, FENG Yue, 2009, "The Value of Online Word-of-Mouth—An Empirical Study Based on Restaurant Reviews," Management World 1(1):126-132 [In Chinese].
Data Update Frequency
Annual Updates