Comparing and modeling land use organization in cities

The advent of geolocated ICT technologies opens the possibility of exploring how people use space in cities, bringing an important new tool for urban scientists and planners, especially for regions where data is scarce or not available. Here we apply a functional network approach to determine land use patterns from mobile phone records. The versatility of the method allows us to run a systematic comparison between Spanish cities of various sizes. The method detects four major land use types that correspond to different temporal patterns. The proportion of these types, their spatial organization and scaling show a strong similarity between all cities that breaks down at a very local scale, where land use mixing is specific to each urban area. Finally, we introduce a model inspired by Schelling's segregation, able to explain and reproduce these results with simple interaction rules between different land uses.Comment: 9 pages, 6 figures + Supplementary informatio

Infomap Bioregions: Interactive mapping of biogeographical regions from species distributions

Biogeographical regions (bioregions) reveal how different sets of species are spatially grouped and therefore are important units for conservation, historical biogeography, ecology and evolution. Several methods have been developed to identify bioregions based on species distribution data rather than expert opinion. One approach successfully applies network theory to simplify and highlight the underlying structure in species distributions. However, this method lacks tools for simple and efficient analysis. Here we present Infomap Bioregions, an interactive web application that inputs species distribution data and generates bioregion maps. Species distributions may be provided as georeferenced point occurrences or range maps, and can be of local, regional or global scale. The application uses a novel adaptive resolution method to make best use of often incomplete species distribution data. The results can be downloaded as vector graphics, shapefiles or in table format. We validate the tool by processing large datasets of publicly available species distribution data of the world's amphibians using species ranges, and mammals using point occurrences. We then calculate the fit between the inferred bioregions and WWF ecoregions. As examples of applications, researchers can reconstruct ancestral ranges in historical biogeography or identify indicator species for targeted conservation.Comment: 8 pages, 4 figures, 2, tables, for interactive application, http://bioregions.mapequation.or

Interest communities and flow roles in directed networks: the Twitter network of the UK riots

Directionality is a crucial ingredient in many complex networks in which information, energy or influence are transmitted. In such directed networks, analysing flows (and not only the strength of connections) is crucial to reveal important features of the network that might go undetected if the orientation of connections is ignored. We showcase here a flow-based approach for community detection in networks through the study of the network of the most influential Twitter users during the 2011 riots in England. Firstly, we use directed Markov Stability to extract descriptions of the network at different levels of coarseness in terms of interest communities, i.e., groups of nodes within which flows of information are contained and reinforced. Such interest communities reveal user groupings according to location, profession, employer, and topic. The study of flows also allows us to generate an interest distance, which affords a personalised view of the attention in the network as viewed from the vantage point of any given user. Secondly, we analyse the profiles of incoming and outgoing long-range flows with a combined approach of role-based similarity and the novel relaxed minimum spanning tree algorithm to reveal that the users in the network can be classified into five roles. These flow roles go beyond the standard leader/follower dichotomy and differ from classifications based on regular/structural equivalence. We then show that the interest communities fall into distinct informational organigrams characterised by a different mix of user roles reflecting the quality of dialogue within them. Our generic framework can be used to provide insight into how flows are generated, distributed, preserved and consumed in directed networks.Comment: 32 pages, 14 figures. Supplementary Spreadsheet available from: http://www2.imperial.ac.uk/~mbegueri/Docs/riotsCommunities.zip or http://rsif.royalsocietypublishing.org/content/11/101/20140940/suppl/DC

Métodos para melhora da análise visual de redes em fluxo contínuo de dados

Temporal networks (also known as dynamic networks) are often used to model connections that occur over time between parts of a system by using nodes and edges. In temporal networks, all nodes, edges, and times, are known and available to be used in the analysis. However, in several real-world applications, data are produced in a massive and continuous way, which is known as data stream. In this case, the volume of data may be so large that the storage may be impossible and mining tasks become more challenging. In streaming temporal networks, edges are continuously arriving in non-stationary distribution. In both temporal and streaming temporal networks, patterns related to node and edge activity are typically irregular in time, which makes the visualization of such networks helpful to gain insights about network structure and dynamics. Nevertheless, the non-stationary distribution of incoming data increases complexity and turns the streaming temporal network visualization even more challenging. Several visualization layouts have been proposed, but they all have limitations. The main challenge in this context is the amount of visual information, that increases depending on the network size and density, and causes visual clutter due to edge overlap, fine temporal resolution, and node proximity. In this thesis, we propose methods to enhance the visualization of streaming temporal networks through the manipulation of the three network dimensions, namely node, edge, and time. Specifically, we propose: (i) CNO, a visual scalable node ordering method; (ii) SEVis, a streaming edge sampling method; and (iii) a streaming method that adapts the temporal resolution according to local levels of node activity. We also present a comparative study considering the combination of these methods. We show through case studies with real-world networks that each of these methods greatly improves layout readability, thus leading to a fast and reliable decision making.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorTese (Doutorado)Redes temporais (ou dinâmicas) são frequentemente usadas para modelar conexões que ocorrem ao longo do tempo entre partes de um sistema por meio de nós e arestas. Nessas redes, todos os nós, arestas e instantes de tempo são conhecidos e estão disponíveis para serem utilizados na análise. Entretanto, em várias situações reais, dados são produzidos de forma massiva e contínua, o que é conhecido como fluxo contínuo de dados (FCD). Nesse tipo de aplicação, o volume de dados pode ser tão grande que o armazenamento deles pode ser impossível e as tarefas de mineração se tornam ainda mais desafiadoras. Em redes provenientes de FCD, arestas são continuamente adicionadas em distribuição não-estacionária. Tanto em redes temporais quanto em redes em FCD, padrões relacionados à atividade de nós e arestas são tipicamente irregulares ao longo do tempo, o que torna a visualização dessas redes útil para obter insights sobre a estrutura e dinâmica delas. Por outro lado, a distribuição não-estacionária aumenta a complexidade e torna a visualização de redes em FCD ainda mais desafiadora. Vários layouts visuais foram propostos até hoje, mas todos possuem limitações. O principal desafio é a quantidade de informação visual, que aumenta dependendo do tamanho e densidade da rede e causa poluição visual devido à sobreposição de arestas, resolução temporal e proximidade dos nós. Nesta tese, nós propomos métodos para melhorar a visualização de redes em FCD por meio da manipulação das três dimensões da rede: nó, aresta e tempo. Mais especificamente, nós propomos: (i) CNO, um método de ordenação de nós visualmente escalável; (ii) SEVis, um método de amostragem de arestas em FCD; (iii) um método para FCD que adapta a resolução temporal de acordo com níveis locais de atividade de nós. Também apresentamos um estudo comparativo considerando a combinação destes métodos. Por meio de estudos de caso com redes reais, mostramos que cada um dos métodos melhora bastante a legibilidade do layout, levando a uma tomada de decisão rápida e confiável

Applications of Cohesive Subgraph Detection Algorithms to Analyzing Socio-Technical Networks

Socio-technical networks can be productively modeled at several granularities, including the interaction of actors, how this interaction is mediated by digital artifacts, and sociograms that model direct ties between the actors themselves. Cohesive subgraph detection algorithms (CSDA, a.k.a. “community detection algorithms”) are often applied to sociograms, but also have utility in analyzing graphs corresponding to other levels of modeling. This paper illustrates applications of CSDA to graphs modeling interaction and mediated association. It reviews some leading candidate algorithms (particularly InfoMap, link communities, the Louvain method, and weakly connected components, all of which are available in R), and evaluates them with respect to how useful they have been in analyzing a large dataset derived from a network of educators known as Tapped In. This practitioner-oriented evaluation is a complement to more formal benchmark based studies common in the literature

Skeleton coupling: a novel interlayer mapping of community evolution in temporal networks

Dynamic community detection (DCD) in temporal networks is a complicated task that involves the selection of an algorithm and its associated parameters. How to choose the most appropriate algorithm generally depends on the type of network being analyzed and the specific properties of the data that define the network. In functional temporal networks derived from neuronal spike train data, communities are expected to be transient, and it is common for the network to contain multiple singleton communities. Here, we compare the performance of different DCD algorithms on functional temporal networks built from synthetic neuronal time series data with known community structure. We find that, for these networks, DCD algorithms that utilize interlayer links to perform community carryover between layers outperform other methods. However, we also observe that algorithm performance is highly dependent on the topology of interlayer links, especially in the presence of singleton and transient communities. We therefore define a novel method for defining interlayer links in temporal networks called skeleton coupling that is specifically designed to enhance the linkage of communities in the network throughout time based on the topological properties of the community history. We show that integrating skeleton coupling with current DCD methods improves algorithm performance in synthetic data with planted singleton and transient communities. The use of skeleton coupling to perform DCD will therefore allow for more accurate and interpretable results of community evolution in real-world neuronal data or in other systems with transient structure and singleton communities.Comment: 19 pages, 8 figure

Perceiving Oldness in Parietal Cortex: fMRI Characterization of a Parietal Memory Network

The manner in which the human brain recognizes certain stimuli as novel or familiar is a matter of ongoing investigation. The overarching goal of this dissertation is to improve our understanding of how this may be accomplished. More specifically, work contained herein focuses on a recently described parietal memory network (PMN; Gilmore et al., 2015) that shows opposite patterns of activity when perceiving novel or familiar stimuli: deactivating in response to novelty, and activating in response to familiarity. Critically, our understanding of this network is based on explicit memory tasks, in which subjects are deliberately instructed to learn or remember information to perform the experimental task. The aim of this dissertation is to determine if the same opposing patterns of activity are present in task conditions in which no explicit orientation to stimulus history is required (i.e., implicit memory conditions). In Chapter 1, I review evidence that links activity within the PMN to encoding and retrieval processes, and describe how the perception of novelty and familiarity may explain various observations from prior literature. In this chapter I discuss several techniques that utilize functional magnetic resonance imaging (fMRI) to measure activations within the brain. These include single experiments utilizing blood oxygen level dependent (BOLD) activity as a means of associating specific behavioral phenomena with specific neural correlates; meta-analyses of many such fMRI studies; and the use of BOLD correlations in the absence of explicit task conditions to estimate the functional network structure of the human brain. It is from the work reviewed in this chapter that this dissertation\u27s empirical questions were derived. In Chapter 2, I discuss experimental data collected under implicit memory task conditions. This was designed to assess the degree to which activity predicted by explicit memory tasks is recapitulated under implicit conditions. Subjects observed stimuli multiple times, making simple semantic judgments during each presentation. The BOLD responses within each subject were measured for each presentation of each stimulus. PMN regions demonstrated two of three predicted patterns of activity: they deactivated relative to a resting baseline when initially processing a stimulus, and they increased in activity across multiple item presentations. Predicted above-baseline activations during final presentations were not observed. This suggests that existing hypotheses describing PMN functions should be revised in a way that suggests a more prominent role for attention in producing familiarity-related activations. In Chapter 3, the task data from Chapter 2 are compared to an individual with superior memory abilities. This individual (ND) is a memory athlete who has trained extensively in the use of mental imagery as a tool for rapid learning. When comparing him to the control group characterized in Chapter 2, we found no appreciable differences in neural activity in the implicit memory task. These findings are consistent with those observed in prior literature that suggest memory athletes do not possess unusual memory skills outside of the tasks they specifically train (Maguire et al., 2002; Ramon et al., 2016). In Chapter 4, resting-state functional connectivity (RSFC) MRI data are examined to estimate the functional network organization of all participants examined in Chapters 2 and 3. The PMN and several control networks were localized using this independent approach, and the activity within regions of each network was compared using data from the implicit memory task. Results suggest that the implicit memory task produces very similar activity in PMN and adjacent default mode network regions, and suggests that the task itself is not a practical means of localizing the PMN within single subjects. Chapter 5 serves as a summary of the results from Chapters 2-4. In this chapter I place key findings of the dissertation in a broader context and suggest future directions that might be taken to better understand the PMN. An updated hypothesis of PMN function is proposed to better account for possible attentional affects on network activity, and several future directions are considered

Detecting behavioural changes in human movement to inform the spatial scale of interventions against COVID-19

On March 23 2020, the UK enacted an intensive, nationwide lockdown to mitigate transmission of COVID-19. As restrictions began to ease, more localized interventions were used to target resurgences in transmission. Understanding the spatial scale of networks of human interaction, and how these networks change over time, is critical to targeting interventions at the most at-risk areas without unnecessarily restricting areas at low risk of resurgence. We use detailed human mobility data aggregated from Facebook users to determine how the spatially-explicit network of movements changed before and during the lockdown period, in response to the easing of restrictions, and to the introduction of locally-targeted interventions. We also apply community detection techniques to the weighted, directed network of movements to identify geographically-explicit movement communities and measure the evolution of these community structures through time. We found that the mobility network became more sparse and the number of mobility communities decreased under the national lockdown, a change that disproportionately affected long distance connections central to the mobility network. We also found that the community structure of areas in which locally-targeted interventions were implemented following epidemic resurgence did not show reorganization of community structure but did show small decreases in indicators of travel outside of local areas. We propose that communities detected using Facebook or other mobility data be used to assess the impact of spatially-targeted restrictions and may inform policymakers about the spatial extent of human movement patterns in the UK. These data are available in near real-time, allowing quantification of changes in the distribution of the population across the UK, as well as changes in travel patterns to inform our understanding of the impact of geographically-targeted interventions