A Survey of Location Prediction on Twitter

Locations, e.g., countries, states, cities, and point-of-interests, are central to news, emergency events, and people's daily lives. Automatic identification of locations associated with or mentioned in documents has been explored for decades. As one of the most popular online social network platforms, Twitter has attracted a large number of users who send millions of tweets on daily basis. Due to the world-wide coverage of its users and real-time freshness of tweets, location prediction on Twitter has gained significant attention in recent years. Research efforts are spent on dealing with new challenges and opportunities brought by the noisy, short, and context-rich nature of tweets. In this survey, we aim at offering an overall picture of location prediction on Twitter. Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations. We first define the three tasks and review the evaluation metrics. By summarizing Twitter network, tweet content, and tweet context as potential inputs, we then structurally highlight how the problems depend on these inputs. Each dependency is illustrated by a comprehensive review of the corresponding strategies adopted in state-of-the-art approaches. In addition, we also briefly review two related problems, i.e., semantic location prediction and point-of-interest recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur

Review of Person Re-identification Techniques

Person re-identification across different surveillance cameras with disjoint fields of view has become one of the most interesting and challenging subjects in the area of intelligent video surveillance. Although several methods have been developed and proposed, certain limitations and unresolved issues remain. In all of the existing re-identification approaches, feature vectors are extracted from segmented still images or video frames. Different similarity or dissimilarity measures have been applied to these vectors. Some methods have used simple constant metrics, whereas others have utilised models to obtain optimised metrics. Some have created models based on local colour or texture information, and others have built models based on the gait of people. In general, the main objective of all these approaches is to achieve a higher-accuracy rate and lowercomputational costs. This study summarises several developments in recent literature and discusses the various available methods used in person re-identification. Specifically, their advantages and disadvantages are mentioned and compared.Comment: Published 201

Introducing the Spatial Conflict Dynamics indicator of political violence

Modern armed conflicts have a tendency to cluster together and spread geographically. However, the geography of most conflicts remains under-studied. To fill this gap, this article presents a new indicator that measures two key geographical properties of subnational political violence: the conflict intensity within a region on the one hand, and the spatial distribution of conflict within a region on the other. We demonstrate the indicator in North and West Africa between 1997 to 2019 to show that it can clarify how conflicts can spread from place to place and how the geography of conflict changes over time

A Fifty-Year Sustainability Assessment of Italian Agro-Forest Districts

DistrictAs cropland management and land use shifted towards more intensive practices, global land degradation increased drastically. Understanding relationships between ecological and socioeconomic drivers of soil and landscape degradation within these landscapes in economically dynamic contexts such as the Mediterranean region, requires multi-target and multi-scalar approaches covering long-term periods. This study provides an original approach for identifying desertification risk drivers and sustainable land management strategies within Italian agro-forest districts. An Environmental Sensitivity Area (ESA) approach, based on four thematic indicators (climate, soil, vegetation and land-use) and a composite index of desertification risk (ESAI), was used to evaluate changes in soil vulnerability and landscape degradation between the years 1960 and 2010. A multivariate model was developed to identify the most relevant drivers causing changes in land susceptibility at the district scale. Larger districts, and those with a higher proportion of their total surface area classified as agro-forest, had a significantly lower increase in land susceptibility to degradation during the 50 years when compared with the remaining districts. We conclude that preserving economic viability and ecological connectivity of traditional, extensive agricultural systems is a key measure to mitigate the desertification risk in the Mediterranean region