Fundamental structures of dynamic social networks

Social systems are in a constant state of flux with dynamics spanning from minute-by-minute changes to patterns present on the timescale of years. Accurate models of social dynamics are important for understanding spreading of influence or diseases, formation of friendships, and the productivity of teams. While there has been much progress on understanding complex networks over the past decade, little is known about the regularities governing the micro-dynamics of social networks. Here we explore the dynamic social network of a densely-connected population of approximately 1000 individuals and their interactions in the network of real-world person-to-person proximity measured via Bluetooth, as well as their telecommunication networks, online social media contacts, geo-location, and demographic data. These high-resolution data allow us to observe social groups directly, rendering community detection unnecessary. Starting from 5-minute time slices we uncover dynamic social structures expressed on multiple timescales. On the hourly timescale, we find that gatherings are fluid, with members coming and going, but organized via a stable core of individuals. Each core represents a social context. Cores exhibit a pattern of recurring meetings across weeks and months, each with varying degrees of regularity. Taken together, these findings provide a powerful simplification of the social network, where cores represent fundamental structures expressed with strong temporal and spatial regularity. Using this framework, we explore the complex interplay between social and geospatial behavior, documenting how the formation of cores are preceded by coordination behavior in the communication networks, and demonstrating that social behavior can be predicted with high precision.Comment: Main Manuscript: 16 pages, 4 figures. Supplementary Information: 39 pages, 34 figure

Modeling Urban Areas Epidemiological Risk Exposure Using Multispectral Spaceborne Data

In recent decades, the world has been fast urbanizing. More than half of the world’s human population now live in urban areas. Such high density of urban population is resulting in air and water pollution, land degradation, and infectious diseases spread risks prominence. However, the increasing quality (in terms of finer spatial and temporal resolution)and quantity of Earth Observation (EO) satellite data provide new perspectives for analysing these phenomena. Within the specific domain of epidemiological risks dynamics in urban areas which is the focus of this work, the use of multispectral optical EO sensor data has created new opportunities. These data through their visible, near, mid, far and thermal infrared bands provide planetary-­‐scale access to environmental variables such as temperature, humidity, and vegetation types, location and conditions. Since these environmental variables affect the development of vectors causing infectious disease (e.g., mosquitoes), there is the possibility to use EO data to estimate them, and obtain disease risk models. The Ae. aegypti mosquito species transmits Zika, Dengue, and Chikungunya, diseases widespread in more than 100 world countries, and is concentrated in urban areas. The development of this vector depends significantly on local environmental temperature, humidity, precipitation and vegetation. In this regard, multispectral EO data can provide globally consistent and scalable sources to obtain the required environmental variable inputs, and extract significant and consistent monitoring and forecasting models for vector population. The work reported in this thesis about this topic has led to the following results: 1) A method to map vegetation types in urban areas at high spatial resolution using Sentinel2 multispectral EO data. The results show an improvement in the quality of the resulting vegetation maps with respect to what is available by means of state-­of-­the-­art techniques. 2) A method that combines EO-­based spectral indices, temperature layers, and precipitation measurement to model the temporal evolution of the local mean Ae. aegypti population. The approach leverages the random forest (RF) machine learning (ML) technique and its embedded nonlinear features importance ranking (mean decrease impurity, MDI) to rank the effects of environmental variables and explain the resulting model. 3) A weighted generalized linear modeling (GLM) technique to predict Ae. aegypti population using multispectral EO data covariate inputs. GLMs are generally simple to implement and explain, but do not provide the same level of prediction quality as ML methods. The proposed weighted GLM compares well with ML techniques in quality, and provides capability for more explicitly interpretation of the results. 4) A recurrent neural network (RNN) technique for spatio­‐temporal modeling of Ae. Aegypti population at the urban block level using multispectral EO data as inputs. This study is needed because spatial models obscure seasonality effects while temporal model are blind to spatial changes in micro-­climates. The proposed technique shows great promise with respect to the use of free multispectral EO data for spatio-­temporal epidemiological modeling. All the proposed techniques have been applied in the Latin American region where the risk of Ae. aegypti vector transmitted diseases are the highest in the world. They were validated thanks to the long term partnership with the University of Alagoas in Maceio (Brazil) and the Brazilian company: ECOVEC

Complex networks analysis in socioeconomic models

This chapter aims at reviewing complex networks models and methods that were either developed for or applied to socioeconomic issues, and pertinent to the theme of New Economic Geography. After an introduction to the foundations of the field of complex networks, the present summary adds insights on the statistical mechanical approach, and on the most relevant computational aspects for the treatment of these systems. As the most frequently used model for interacting agent-based systems, a brief description of the statistical mechanics of the classical Ising model on regular lattices, together with recent extensions of the same model on small-world Watts-Strogatz and scale-free Albert-Barabasi complex networks is included. Other sections of the chapter are devoted to applications of complex networks to economics, finance, spreading of innovations, and regional trade and developments. The chapter also reviews results involving applications of complex networks to other relevant socioeconomic issues, including results for opinion and citation networks. Finally, some avenues for future research are introduced before summarizing the main conclusions of the chapter.Comment: 39 pages, 185 references, (not final version of) a chapter prepared for Complexity and Geographical Economics - Topics and Tools, P. Commendatore, S.S. Kayam and I. Kubin Eds. (Springer, to be published

Pemilihan kerjaya di kalangan pelajar aliran perdagangan sekolah menengah teknik : satu kajian kes

This research is a survey to determine the career chosen of form four student in commerce streams. The important aspect of the career chosen has been divided into three, first is information about career, type of career and factor that most influence students in choosing a career. The study was conducted at Sekolah Menengah Teknik Kajang, Selangor Darul Ehsan. Thirty six form four students was chosen by using non-random sampling purpose method as respondent. All information was gather by using questionnaire. Data collected has been analyzed in form of frequency, percentage and mean. Results are performed in table and graph. The finding show that information about career have been improved in students career chosen and mass media is the main factor influencing students in choosing their career

Spatial data analysis in economics

Spatial data analysis has become a widely used tool among economists and social scientists. Improved availability of georeferenced social and economic data, a rising interest in data visualisation, spatial pattern recognition, and spatial interactions as well as improved statistical techniques increased the popularity of spatial data analysis techniques. The purpose of this work is to study spatial data analysis techniques and apply those techniques on social and economic issues. This work consists of three articles on applied spatial data analysis in economics. The first article studies the determinants of local supply differences in the market for election gambling machines (EGM). We study, whether a certain social and economic milieu (e.g. high unemployment) is associated with higher EGM supply. The second article studies spillover effects in the EGM market. The article explains why the EGM supply clusters in certain regions which results in hot spots with high gambling supply. Article three evaluates the impact of immigration on the voting behaviour in Germany. As an example, we use the 2015/2016 refugee crisis and study how refugee presence affected the regional election outcomes in the 2016 elections in Germany.Die Räumliche Datenanalyse findet zunehmende Verwendung bei Ökonomen und Sozialwissenschaftlern. Die steigende Beliebtheit lässt sich vor allem auf eine höhere Verfügbarkeit von geographisch kodierten Wirtschafts- und Sozialdaten sowie ein zunehmendes Interesse an Datenvisualisierung, Mustererkennung, räumlichen Interaktionen sowie verbesserten statistischen Methoden zur Analyse dieser Daten zurückführen. Das Ziel dieser Arbeit besteht darin die Räumliche Datenanalyse zur Klärung aktueller Fragen der Wirtschafts- und Sozialwissenschaften anzuwenden. Die Arbeit setzt sich aus drei Artikeln zusammen. Der erste Artikel erforscht die Determinanten von lokalen Angebotsunterschieden im Markt für elektronische Geldspielgeräten (EGM). Dabei wird untersucht, ob ein Zusammenhang zwischen dem soziökonomischen Milieu (z.B. hohe Arbeitslosigkeit) und höherem Angebot von Geldspielgeräten besteht. Der zweite Artikel untersucht Spillover Effekte im Markt für elektronische Geldspielgeräte. Es wird die Frage untersucht, warum sich in manchen Regionen Hotspots mit hohem Angebot von elektronischen Geldspielgerären bilden. Der dritte Artikel bewertet den Einfluss von Migration auf das Wahlverhalten in Deutschland. Wir betrachten die Flüchtlingskrise von 2015/2016 und untersuchen wie sich die Präsenz von Flüchtlingen auf das lokale Wahlverhalten während der Landtagswahlen 2016 auswirkte