The Dazhong Dianping Merchant and Rating Database is a structured database constructed from publicly available data sourced from Dazhong Dianping, a leading domestic lifestyle service platform. This database comprehensively covers merchant information across multiple cities nationwide, spanning various consumption sectors such as catering, shopping, and leisure entertainment. It encompasses 28 fields, including basic merchant information, geographic coordinates, classification systems, operational status, user reviews, consumption levels, 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 Completeness: Provides four-tier spatial identifiers ("Province—City—Administrative 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 coverage areas.
• Systematic Classification Framework: Built on a three-tier classification coding system ("Major Category—Medium Category—Minor Category"), covering dimensions from macro-industry types to micro-business formats. Supports cross-industry comparisons and analyses of niche market competition patterns, featuring clear classification structures and standardized coding.
• Multi-dimensional Merchant Information: Beyond basic names, addresses, and contact details, includes fields such as brand affiliation, review count, average expenditure, shop rating, operating hours, and service facilities. Fully characterizes merchant operational features and service quality, integrating both static attributes and dynamic performance indicators.
• Strong Data Reliability: Data originates from Dazhong Dianping’s public pages, ensuring consistency through unified collection and cleaning processes.

Potential Application Scenarios:

• Academic Research: Applicable to fields such as urban geography (evolution of commercial spatial structures), consumption economics (relationship between price sensitivity and ratings), and management science (service quality and user review mechanisms). High-precision geocoding supports GIS spatial analysis, including hotspot identification and spatial autocorrelation analysis.
• Commercial Services: Assists enterprises in site selection decisions, 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 an evidence base for public policies like urban commercial planning, consumption stimulus policy evaluation, and small business support. Analyzes spatial distribution and performance of diverse merchant types to identify underserved service areas and optimize public resource allocation.

   The Dazhong Dianping Merchant and Rating Data integrates core platform merchant data systematically, multi-dimensionally, and with high precision, encompassing spatial, categorical, and operational performance dimensions. Its well-designed fields, clear classification framework, and reliable data quality make it a rare underlying data resource for academic research, commercial analysis, and policy formulation.

Temporal Coverage

2012–2025, with annual editions (updatable as needed)

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 'Dazhong Dianping'," Economic Research Journal No. 1.
• Lu Xianghua, Feng Yue, 2009: "The Value of Online Word-of-Mouth—An Empirical Study Based on Restaurant Reviews," Management World No. 1.

Data Update Frequency

Annual updates