Crime analysis is important for social security management. With the advance of crowd sensing techniques, abundant multisource crowd sensed data could be used for crime analysis. The occurrence of crimes usually has some patterns in terms of temporal and spatial aspects. Investigating the spatio-temporal correlation of crimes could provide more useful cues for crime analysis and help discover underlying crime patterns. In this paper, we conduct a spatio-temporal study to understand urban crimes leveraging multisource crowd sensed data, including crime data, meteorological data, POI distribution, and taxi trips. Specifically, we first present monthly temporal trends and spatial distribution of crimes. We then investigate the spatio-temporal correlation using meteorological data (e.g., weather conditions and air temperature) and POI distribution and taxi trips. It is found that taxi trips and air temperature have a strong correlation with the crime, and some POI categories have a valuable correlation with the crime, e.g., College & University. We also find that Overcast days would witness more crime than other weather conditions.

Spatio-temporal analysis of urban crime leveraging multisource crowdsensed data

Optimal store placement aims to identify the optimal location for a new brick-and-mortar store that can maximize its sale by analyzing and mining users’ preferences from large-scale urban data. In ...

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MetaStore: A Task-adaptative Meta-learning Model for Optimal Store Placement with Multi-city Knowledge Transfer

Forecasting the fire risk is of great importance to fire prevention deployments in a city, which can reduce loss even deaths caused by fires. However, it is very challenging because fires are influenced by many complex factors, including spatial correlations, temporal dependencies, even the mixture of these two and external factors. Firstly, the fire risk of a region is influenced by temporal effect of internal factors (e.g., the historical fire risk records) and temporal effect of external factors (e.g., weather). Secondly, a region's fire risk is not only influenced by its inherent geospatial attributes (e.g., POIs) but also dependent on other regions in spatial. To address these challenges, we propose a machine learning approach to forecast the fire risk, entitled NeuroFire. NeuroFire can represent internal and external temporal effect then combine the temporal representation and spatial dependencies by a spatial-temporal loss function. Experimental evaluations on real-world datasets show that our NeuroFire outperforms 9 baselines, demonstrating the performance of our approach by several visualizations. Moreover, we implement a citywide fire forecasting system named CityGuard to display the analysis and forecasting results, which can assist the fire rescue department in deploying fire prevention.

CityGuard: Citywide Fire Risk Forecasting Using A Machine Learning Approach

With increasing expansion and urbanization of cities, the gap is constantly widening between the current provision of urban public toilets and the fast-growing toileting demand. Building new ones becomes a promising way to alleviate such issue. Nevertheless, where to build them in a city is challenging. Different from other location selection (e.g., commercial sites) problems, the selection of public toilet locations is hard to quantify and evaluate. On one hand, the toileting demand that determines whether the new public toilet is needed cannot be measured accurately. On the other hand, the modeling of the toileting demand is also complicated, being influenced by multiple factors, e.g., human mobility, human activity, and geographical characteristics. In this article, we propose a novel data-driven framework named ToiletBuilder to address it, which consists of three components, i.e., region identification, region representation, and region classification. Specifically, region identification obtains many reachable regions with the reasonable size. Region representation extracts city-specific features from multiple urban data to characterize location selection influencing factors for each region. A deep embedding model is further applied to learn a high-order and concise semantic representation. By labeling some regions with the true positive label (i.e., having public toilets served in these regions) in advance, region classification trains a positive-unlabeled (PU) learning model from these samples to identify unlabeled positive ones. Finally, we conduct extensive experiments based on four real-world data sets including road network, river network, taxi trajectory, and POI data, in the city of Chongqing, China. Results demonstrate the effectiveness of our proposed approach.

ToiletBuilder : A PU-Learning-Based Model for Selecting New Public Toilet Locations

Store site recommendation aims to predict the value of the store at candidate locations and then recommend the optimal location to the company for placing a new brick-and-mortar store. Most existing studies focus on learning machine learning or deep learning models based on large-scale training data of existing chain stores in the same city. However, the expansion of chain enterprises in new cities suffers from data scarcity issues, and these models do not work in the new city where no chain store has been placed (i.e., cold-start problem). In this article, we propose a unified approach for cold-start store site recommendation, Weighted Adversarial Network with Transferability weighting scheme (WANT), to transfer knowledge learned from a data-rich source city to a target city with no labeled data. In particular, to promote positive transfer, we develop a discriminator to diminish distribution discrepancy between source city and target city with different data distributions, which plays the minimax game with the feature extractor to learn transferable representations across cities by adversarial learning. In addition, to further reduce the risk of negative transfer, we design a transferability weighting scheme to quantify the transferability of examples in source city and reweight the contribution of relevant source examples to transfer useful knowledge. We validate WANT using a real-world dataset, and experimental results demonstrate the effectiveness of our proposed model over several state-of-the-art baseline models.

Knowledge Transfer with Weighted Adversarial Network for Cold-Start Store Site Recommendation

Store site recommendation is one of the essential business services in smart cities for brick-and-mortar enterprises. In recent years, the proliferation of multisource data in cities has fostered unprecedented opportunities to the data-driven store site recommendation, which aims at leveraging large-scale user-generated data to analyze and mine users’ preferences for identifying the optimal location for a new store. However, most works in store site recommendation pay more attention to a single data source which lacks some significant data (e.g., consumption data and user profile data). In this paper, we aim to study the store site recommendation in a fine-grained manner. Specifically, we predict the consumption level of different users at the store based on multisource data, which can not only help the store placement but also benefit analyzing customer behavior in the store at different time periods. To solve this problem, we design a novel model based on the deep neural network, named DeepStore, which learns low- and high-order feature interactions explicitly and implicitly from dense and sparse features simultaneously. In particular, DeepStore incorporates three modules: 1) the cross network; 2) the deep network; and 3) the linear component. In addition, to learn the latent feature representation from multisource data, we propose two embedding methods for different types of data: 1) the filed embedding and 2) attention-based spatial embedding. Extensive experiments are conducted on a real-world dataset including store data, user data, and point-of-interest data, the results demonstrate that DeepStore outperforms the state-of-the-art models.

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Jingmin Chen

Papers

DeepStore: An Interaction-Aware Wide&Deep Model for Store Site Recommendation With Attentional Spatial Embeddings

MetaStore: A Task-adaptative Meta-learning Model for Optimal Store Placement with Multi-city Knowledge Transfer

Knowledge Transfer with Weighted Adversarial Network for Cold-Start Store Site Recommendation