The increasing interest in complex networks research has been a consequence of several intrinsic features of this area, such as the generality of the approach to represent and model virtually any discrete system, and the incorporation of concepts and methods deriving from many areas, from statistical physics to sociology, which are often used in an independent way. Yet, for this same reason, it would be desirable to integrate these various aspects into a more coherent and organic framework, which would imply in several benefits normally allowed by the systematization in science, including the identification of new types of problems and the cross-fertilization between fields. More specifically, the identification of the main areas to which the concepts frequently used in complex networks can be applied paves the way to adopting and applying a larger set of concepts and methods deriving from those respective areas. Among the several areas that have been used in complex networks research, pattern recognition, optimization, linear algebra, and time series analysis seem to play a more basic and recurrent role. In the present manuscript, we propose a systematic way to integrate the concepts from these diverse areas regarding complex networks research. In order to do so, we start by grouping the multidisciplinary concepts into three main groups, namely features, similarity, and network connectivity. Then we show that several of the analysis and modeling approaches to complex networks can be thought as a composition of maps between these three groups, with emphasis on nine main types of mappings, which are presented and illustrated. Such a systematization of principles and approaches also provides an opportunity to review some of the most closely related works in the literature, which is also developed in this article.

Nowadays, hyperspectral image classification widely copes with spatial information to improve accuracy. One of the most popular way to integrate such information is to extract hierarchical features from a multiscale segmentation. In the classification context, the extracted features are commonly concatenated into a long vector (also called stacked vector), on which is applied a conventional vector-based machine learning technique (e.g. SVM with Gaussian kernel). In this paper, we rather propose to use a sequence structured kernel: the spectrum kernel. We show that the conventional stacked vector-based kernel is actually a special case of this kernel. Experiments conducted on various publicly available hyperspectral datasets illustrate the improvement of the proposed kernel w.r.t. conventional ones using the same hierarchical spatial features.

Spatial Reasoning is a logical reasoning system that assumed entities located in space and have a spatial structure. Making machines that can perceive and understand space has always been a researcher's dream. Our lives could be enhanced by their assistance; with the spatial intelligence of machines, we would have new methods of planning and navigation through spatial orientation. Artificial Intelligence - Our everyday concepts are populated with qualitative spatial description. Something is sitting next to something else.

This book is an updated version of a well-received book previously published in Chinese by Science Press of China (the first edition in 2006 and the second in 2013). It offers a systematic and practical overview of spatial data mining, which combines computer science and geo-spatial information science, allowing each field to profit from the knowledge and techniques of the other. To address the spatiotemporal specialties of spatial data, the authors introduce the key concepts and algorithms of the data field, cloud model, mining view, and Deren Li methods. The cloud model is a qualitative method that utilizes quantitative numerical characters to bridge the gap between pure data and linguistic concepts. The mining view method discriminates the different requirements by using scale, hierarchy, and granularity in order to uncover the anisotropy of spatial data mining.

Many methods have been proposed for detecting emerging events in text streams using topic modeling. However, these methods have shortcomings that make them unsuitable for rapid detection of locally emerging events on massive text streams. We describe Spatially Compact Semantic Scan (SCSS) that has been developed specifically to overcome the shortcomings of current methods in detecting new spatially compact events in text streams. SCSS employs alternating optimization between using semantic scan (Liu and Neill (2011)) to estimate contrastive foreground topics in documents, and discovering spatial neighborhoods (Shao et al. (2011)) with high occurrence of documents containing the foreground topics. We evaluate our method on Emergency Department chief complaints dataset (ED dataset) to verify the effectiveness of our method in detecting real-world disease outbreaks from free-text ED chief complaint data.

Applications of spatial data analysis are endless, cutting across many disciplines. With the advent of various sensors today – space, airborne, terrestrial alike, spatial analysis is becoming paramount in order to turn these data into valuable information. However, processing massive amount of data is a challenge as it requires complex procedures and multiple tools. The session shall highlight the latest development in big data analytics as well as some exemplary applications from different domains.

Google has said it will ban ads for payday loans because they can be "deceptive or harmful". The ban, which will come into force globally from 13 July, will cover loans that can be due within 60 days and, in the US, loans that carry an annual interest rate of 36% or higher. In a blog post, Google's director of public policy, David Graff, wrote: "Research has shown that these loans can result in unaffordable payment and high default rates for users so we will be updating our policies globally to reflect that. "This change is designed to protect our users from deceptive or harmful financial products." The ban will not cover mortgages, car loans, student loans, commercial loans or credit cards, said Graff, adding: "We'll continue to review the effectiveness of this policy, but our hope is that fewer people will be exposed to misleading or harmful products." In a quote accompanying the announcement, Wade Henderson, the president and chief executive of the Leadership Conference on Civil and Human Rights, said: "This new policy addresses many of the longstanding concerns shared by the entire civil rights community about predatory payday lending.

ONITORING and predicting road traffic is of great importance for traffic managers. With the increase of mobile sensors, such as GPS devices and smartphones, much information is at hand to understand urban traffic. In the last few years, a large amount of research has been conducted in order to use this data to model and analyze road traffic conditions. The aim of this paper is to tackle the issue of predicting the destination of vehicles given a prefix of their trajectory. This problem has been the subject of a Kaggle challenge entitled "ECML/PKDD 15: Taxi Trajectory Prediction (I)" [1]. The observations are time-stamped locations that correspond to the different positions of vehicles moving within a city monitored at different observation times. When dealing with a dataset composed of trajectories, the difficulty lies in the fact that the data convey both spatial information (locations of the vehicles on the map of the city) and temporal information (for each vehicle, the locations are indexed by time, which creates a sequence of locations that compose a full trajectory). Hence the data have a spatiotemporal structure that must be taken into account in order to model their evolution while the trajectories of the destination points to be predicted are unknown. Vehicle trajectories are also constrained to a road network which makes their time progression very irregular.

Analyzing huge amounts of spatial data plays an important role in many emerging analysis and decision-making domains such as healthcare, urban planning, agriculture and so on. For extracting meaningful knowledge from geographical data, the relationships between spatial data objects need to be analyzed. An important class of such relationships are topological relations like the connectedness or overlap between regions. While real-world geographical regions such as lakes or forests do not have exact boundaries and are fuzzy, most of the existing analysis methods neglect this inherent feature of topological relations. In this paper, we propose a method for handling the topological relations in spatial databases based on fuzzy region connection calculus (RCC). The proposed method is implemented in PostGIS spatial database and evaluated in analyzing the relationship of diseases as an important application domain. We also used our fuzzy RCC implementation for fuzzification of the skyline operator in spatial databases. The results of the evaluation show that our method provides a more realistic view of spatial relationships and gives more flexibility to the data analyst to extract meaningful and accurate results in comparison with the existing methods.

The conservation of key wildlife species such as tigers and elephants are threatened by poaching activities. In many conservation areas, foot patrols are conducted to prevent poaching but they may not be well-planned to make the best use of the limited patrolling resources. While prior work has introduced PAWS (Protection Assistant for Wildlife Security) as a game-theoretic decision aid to design effective foot patrol strategies to protect wildlife, the patrol routes generated by PAWS may be difficult to follow in areas with complex terrain. Subsequent research has worked on the significant evolution of PAWS, from an emerging application to a regularly deployed software. A key advance of the deployed version of PAWS is that it incorporates the complex terrain information and generates a strategy consisting of easy-to-follow routes. In this demonstration, we provide 1) a video introducing the PAWS system; 2) an interactive visualization of the patrol routes generated by PAWS in an example area with complex terrain; and 3) a machine-human competition in designing patrol strategy given complex terrain and animal distribution.