Starbucks - Global Brand Presence POIs
收藏Snowflake2024-03-07 更新2024-05-01 收录
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资源简介:
This dataset provides comprehensive information on all Starbucks locations worldwide, including coffee shops, cafes, distribution centers, food court storefronts, and more.
Dataplor offers over 12,000 global brands through its constantly updated, human-reviewed, worldwide datasets.
Available Attributes:
Unique dataplor ID: A unique identifier for each record in the dataset.
Business Name: The name Starbucks-branded location.
Main/Sub/Business Categories: Primary and secondary business categories (e.g., "dining","cafe").
Chain ID/Name: Chain identifier (if applicable) and the associated chain name (e.g., "Starbucks").
Home Address: Full address of the location, including street number, street name, city, state/province, postal code, and country.
Neighborhood: Neighborhood where the location is situated.
City, State, Postal Code, Country: City, state/province, postal code, and country of the location.
Latitude/Longitude: Geographic coordinates of the location.
Open/Closed Status: Indicates if the location is currently open or closed.
Confidence Scoring: Dataplor's confidence score in the accuracy of the data for this location.
First Opened: Date the location first opened for business.
Popularity Indices: An index representing the relative popularity of the location based on various factors.
本数据集收录了全球所有星巴克门店的全面信息,涵盖咖啡店、咖啡馆、配送中心、美食广场门店等各类业态。
Dataplor通过其持续更新且经人工审核的全球数据集,提供超过12000个全球品牌的相关数据。
可用数据字段:
Dataplor专属ID:本数据集中每条记录的唯一标识符。
商户名称:星巴克品牌门店的官方名称。
主/次业务类别:门店的一级、二级业务分类(例如“餐饮”“咖啡馆”)。
连锁品牌ID/名称:连锁门店的唯一标识(如适用)及所属连锁品牌名称(例如“星巴克”)。
完整地址:门店的完整通讯地址,包含街道门牌号、街道名称、城市、州/省、邮政编码及国家信息。
所在街区:门店所处的街区区域。
城市、州/省、邮政编码及国家:门店所属的城市、州/省、邮政编码与国家信息。
经纬度:门店的地理坐标信息。
营业状态:标识门店当前处于营业或歇业状态。
数据置信度评分:Dataplor对该门店数据准确性的置信评分。
首次开业日期:门店首次对外营业的日期。
热度指数:基于多维度因素计算得出的门店相对热度评分。
提供机构:
dataplor
创建时间:
2024-03-06
搜集汇总
数据集介绍

背景与挑战
背景概述
该数据集提供了全球星巴克所有门店的全面信息,包括咖啡店、咖啡馆、配送中心等多种类型,覆盖超过12,000个品牌点。数据集包含丰富的属性,如唯一标识符、详细地址、地理坐标、营业状态、开业日期和受欢迎度指数,确保数据的准确性和实用性。这些特点使其成为研究星巴克全球品牌分布和商业分析的重要资源。
以上内容由遇见数据集搜集并总结生成



