This paper discusses the opportunities afforded by novel population sensing technologies in the field of ‘smart’ urban management. In particular, it focuses on the application of these new sources of data in retail analysis.

Our goal is to integrate data derived through novel pedestrian counting and point-of-sale systems to build a statistical model that captures the relationship between retail turnover and footfall in the UK. The point-of-sales data are provided by two UK-based food & beverage retailers. To accurately measure the pedestrian activity around retail units, we make use of the data generated by the SmartStreetSensor project: a deployment of a large network of sensors installed across 105 towns and cities in the UK that collect Wi-Fi probe requests generated by mobile devices. We propose and implement novel methods for processing these raw signals into accurate estimates of pedestrian activity without compromising participants' privacy.

The resulting data is then integrated into seasonal ARIMA and dynamic regression models that can be used to predict future sales. Our results indicate that the dynamic regression model that accounts for fluctuations in footfall data outperforms seasonal ARIMA model that uses only past values and behaviours of transaction data to predict future sales. Thus, we conclude that footfall does have a strong impact on retail sales and therefore integrating footfall measures into sales forecasting can significantly improve the forecasting results. We also examine differences between the two retailers and observe a stronger correlation at the Fast Food Retailer locations compared to the correlation at Family Restaurant locations.

本文讨论了新型的人口感知技术在“智能”城市管理领域中提供的机会。特别是，它着重于这些新数据源在零售分析中的应用。

我们的目标是整合通过新颖的行人计数和销售点系统获得的数据，以建立一个统计模型，以捕获英国零售额与客流量之间的关系。销售点数据由两家英国的食品和饮料零售商提供。为了准确衡量零售单元周围的行人活动，我们利用了SmartStreetSensor项目生成的数据：在英国105个城镇中安装了大型传感器网络，以收集移动设备生成的Wi-Fi探测请求。我们提出并实施了新颖的方法，将这些原始信号处理为对行人活动的准确估算，而不会损害参与者的隐私。

然后将所得数据集成到季节性ARIMA和动态回归模型中，这些模型可用于预测未来的销售量。我们的结果表明，用于解释人流量数据波动的动态回归模型优于仅使用交易数据的过去值和行为来预测未来销售的季节性ARIMA模型。因此，我们得出结论，客流量确实会对零售销售产生重大影响，因此将客流量测量方法纳入销售预测可以显着改善预测结果。我们还检查了两家零售商之间的差异，并发现与家庭餐馆位置相比，快餐零售商位置的相关性更强。