Discovering Urban Functional Zones By Latent Fusion of Users GPS Data and Points of Interests

With rapid development of socio-economics, the task of discovering functional zones becomes critical to better understand the interactions between social activities and spatial locations. In this paper, we propose a framework to discover the functional zones by analyzing urban structures and social behaviors. The proposed approach models the inner influences between spatial locations and human activities by fusing the semantic meanings of both Point of Interests (POIs) and human activities to learn the latent representation of the regions. A spatial based unsupervised clustering method, Conditional Random Filed (CRF), is then applied to aggregate regions using both their spatial information and discriminative representations. Also, we estimate the functionality of the regions and annotate them by the differences between the normalized POI distributions which properly rank various functionalities. This framework is able to properly address the biased categories in sparse POI data, when exploring the unbiased and true functional zones. To validate our framework, a case study is evaluated by using very large real-world users GPS and POIs data from city of Raleigh. The results demonstrate that the proposed framework can better identify functional zones than the benchmarks, and, therefore, enhance understanding of urban structures with a finer granularity under practical conditions

Context Trees: Augmenting Geospatial Trajectories with Context

Exposing latent knowledge in geospatial trajectories has the potential to provide a better understanding of the movements of individuals and groups. Motivated by such a desire, this work presents the context tree, a new hierarchical data structure that summarises the context behind user actions in a single model. We propose a method for context tree construction that augments geospatial trajectories with land usage data to identify such contexts. Through evaluation of the construction method and analysis of the properties of generated context trees, we demonstrate the foundation for understanding and modelling behaviour afforded. Summarising user contexts into a single data structure gives easy access to information that would otherwise remain latent, providing the basis for better understanding and predicting the actions and behaviours of individuals and groups. Finally, we also present a method for pruning context trees, for use in applications where it is desirable to reduce the size of the tree while retaining useful information

Temporal decomposition and semantic enrichment of mobility flows

Mobility data has increasingly grown in volume over the past decade as loc- alisation technologies for capturing mobility ows have become ubiquitous. Novel analytical approaches for understanding and structuring mobility data are now required to support the back end of a new generation of space-time GIS systems. This data has become increasingly important as GIS is now an essen- tial decision support platform in many domains that use mobility data, such as eet management, accessibility analysis and urban transportation planning. This thesis applies the machine learning method of probabilistic topic mod- elling to decompose and semantically enrich mobility ow data. This process annotates mobility ows with semantic meaning by fusing them with geograph- ically referenced social media data. This thesis also explores the relationship between causality and correlation, as well as the predictability of semantic decompositions obtained during a case study using a real mobility dataset

Reconstructing human activities via coupling mobile phone data with location-based social networks

In the era of big data, the ubiquity of location-aware portable devices provides an unprecedented opportunity to understand inhabitants' behavior and their interactions with the built environments. Among the widely used data resources, mobile phone data is the one passively collected and has the largest coverage in the population. However, mobile operators cannot pinpoint one user within meters, leading to the difficulties in activity inference. To that end, we propose a data analysis framework to identify user's activity via coupling the mobile phone data with location-based social networks (LBSN) data. The two datasets are integrated into a Bayesian inference module, considering people's circadian rhythms in both time and space. Specifically, the framework considers the pattern of arrival time to each type of facility and the spatial distribution of facilities. The former can be observed from the LBSN Data and the latter is provided by the points of interest (POIs) dataset. Taking Shanghai as an example, we reconstruct the activity chains of 1,000,000 active mobile phone users and analyze the temporal and spatial characteristics of each activity type. We assess the results with some official surveys and a real-world check-in dataset collected in Shanghai, indicating that the proposed method can capture and analyze human activities effectively. Next, we cluster users' inferred activity chains with a topic model to understand the behavior of different groups of users. This data analysis framework provides an example of reconstructing and understanding the activity of the population at an urban scale with big data fusion