The economic viability of an urban area in terms of trade and shopping significantly impacts its residents’ quality of life and is crucial for any sustainable development initiative. Geographic information systems (GIS) are well established, but the use of GIS technology within finance and trade analysis is still in its infancy. In this article, we highlighted the potential of GIS technology and big data analytics and demonstrated the importance of thinking in spatial terms for analysing patterns within the trade and finance industries. We studied spatiotemporal trade and shopping patterns in the city of Tabriz using data generated by customer purchase transactions obtained from 5200 stores, shopping, business and service centres. We employed time series transaction data collected from the points of sale in stores, shopping, service and business centres located in different areas of the city. We applied four well known geospatial big data driven approaches including machine learning nearest neighbour, kernel density estimation, space–time pattern mining and spatiotemporal coupling tele-coupling for detecting and mapping of spatial trade hotspot patterns. The results of this study indicated the potential of GIScience methods for the explicit spatial mapping of trade and shopping patterns. The results revealed that the city centre, particularly the Bazaar of Tabriz, acts as the city’s heart of trade, and we identify additional major business hotspots. Furthermore, the results allow for studying the impacts of unbalanced urban development in Tabriz, where the wealthy suburbs with high quality of life, such as Valiasr and Elguli, host the major shopping hotspots. The spatial patterns obtained enable local stakeholders, decision makers and authorities to develop strategic plans for urban sustainable development in Tabriz. The geospatial big data approach used can stimulate novel and progressive research. Results of this study demonstrate methodological advancements in GIScience by ’spatializing’ individual purchase data and therefor proposing an explicit geospatial big data analysis approach.

中文翻译：

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城市贸易和购物模式的时空映射：地理空间大数据方法

城市地区在贸易和购物方面的经济活力极大地影响着居民的生活质量，对于任何可持续发展举措都至关重要。地理信息系统 (GIS) 已十分成熟，但 GIS 技术在金融和贸易分析中的使用仍处于起步阶段。在本文中，我们强调了 GIS 技术和大数据分析的潜力，并论证了空间思维对于分析贸易和金融行业模式的重要性。我们利用从 5200 家商店、购物、商业和服务中心获得的客户购买交易生成的数据，研究了大不里士市的时空贸易和购物模式。我们使用了从位于城市不同区域的商店、购物、服务和商业中心的销售点收集的时间序列交易数据。我们应用了四种著名的地理空间大数据驱动方法，包括机器学习最近邻、核密度估计、时空模式挖掘和时空耦合远程耦合来检测和绘制空间贸易热点模式。这项研究的结果表明了地理信息科学方法在贸易和购物模式的明确空间绘图方面的潜力。结果显示，市中心，特别是大不里士集市，是该市的贸易中心，我们还确定了其他主要商业热点。此外，研究结果还可以研究大不里士城市发展不平衡的影响，那里生活质量高的富裕郊区，如瓦利亚斯尔和埃尔古利，是主要的购物热点。获得的空间模式使当地利益相关者、决策者和当局能够制定大不里士城市可持续发展的战略计划。使用的地理空间大数据方法可以激发新颖和进步的研究。这项研究的结果通过“空间化”个人购买数据证明了地理信息科学方法的进步，并因此提出了一种明确的地理空间大数据分析方法。