  Choosing a good location when opening a new store is crucial for the future
success of a business. Traditional methods include offline manual survey, which
is very time consuming, and analytic models based on census data, which are un-
able to adapt to the dynamic market. The rapid increase of the availability of
big data from various types of mobile devices, such as online query data and
offline positioning data, provides us with the possibility to develop automatic
and accurate data-driven prediction models for business store placement. In
this paper, we propose a Demand Distribution Driven Store Placement (D3SP)
framework for business store placement by mining search query data from Baidu
Maps. D3SP first detects the spatial-temporal distributions of customer demands
on different business services via query data from Baidu Maps, the largest
online map search engine in China, and detects the gaps between demand and sup-
ply. Then we determine candidate locations via clustering such gaps. In the
final stage, we solve the location optimization problem by predicting and
ranking the number of customers. We not only deploy supervised regression
models to predict the number of customers, but also learn to rank models to
directly rank the locations. We evaluate our framework on various types of
businesses in real-world cases, and the experiments results demonstrate the
effectiveness of our methods. D3SP as the core function for store placement has
already been implemented as a core component of our business analytics platform
and could be potentially used by chain store merchants on Baidu Nuomi.
