Comparison of social structures within cities of very different sizes.
ABSTRACT: People make a city, making each city as unique as the combination of its inhabitants. However, some cities are similar and some cities are inimitable. We examine the social structure of 10 different cities using Twitter data. Each city is decomposed to its communities. We show that in many cases one city can be thought of as an amalgamation of communities from another city. For example, we find the social network of Manchester is very similar to the social network of a virtual city of the same size, where the virtual city is composed of communities from the Bristol network. However, we cannot create Bristol from Manchester since Bristol contains communities with a social structure that are not present in Manchester. Some cities, such as Leeds, are outliers. That is, Leeds contains a particularly wide range of communities, meaning we cannot build a similar city from communities outside of Leeds. Comparing communities from different cities, and building virtual cities that are comparable to real cities, is a novel approach to understand social networks. This has implications when using social media to inform or advise residents of a city.
Project description:Severe iodine deficiency in mothers is known to impair foetal development. Pregnant women in the UK may be iodine insufficient, but recent assessments of iodine status are limited. This study assessed maternal urinary iodine concentrations (UIC) and birth outcomes in three UK cities. Spot urines were collected from 541 women in London, Manchester and Leeds from 2004⁻2008 as part of the Screening for Pregnancy End points (SCOPE) study. UIC at 15 and 20 weeks' gestation was estimated using inductively coupled plasma-mass spectrometry (ICP-MS). Associations were estimated between iodine status (UIC and iodine-to-creatinine ratio) and birth weight, birth weight centile (primary outcome), small for gestational age (SGA) and spontaneous preterm birth. Median UIC was highest in Manchester (139 μg/L, 95% confidence intervals (CI): 126, 158) and London (130 μg/L, 95% CI: 114, 177) and lowest in Leeds (116 μg/L, 95% CI: 99, 135), but the proportion with UIC <50 µg/L was <20% in all three cities. No evidence of an association was observed between UIC and birth weight centile (-0.2% per 50 μg/L increase in UIC, 95% CI: -1.3, 0.8), nor with odds of spontaneous preterm birth (odds ratio = 1.00, 95% CI: 0.84, 1.20). Given the finding of iodine concentrations being insufficient according to World Health Organization (WHO) guidelines amongst pregnant women across all three cities, further studies may be needed to explore implications for maternal thyroid function and longer-term child health outcomes.
Project description:Cities are home to an increasing majority of the world's population. Currently, it is difficult to track social, economic, environmental and health outcomes in cities with high spatial and temporal resolution, needed to evaluate policies regarding urban inequalities. We applied a deep learning approach to street images for measuring spatial distributions of income, education, unemployment, housing, living environment, health and crime. Our model predicts different outcomes directly from raw images without extracting intermediate user-defined features. To evaluate the performance of the approach, we first trained neural networks on a subset of images from London using ground truth data at high spatial resolution from official statistics. We then compared how trained networks separated the best-off from worst-off deciles for different outcomes in images not used in training. The best performance was achieved for quality of the living environment and mean income. Allocation was least successful for crime and self-reported health (but not objectively measured health). We also evaluated how networks trained in London predict outcomes three other major cities in the UK: Birmingham, Manchester, and Leeds. The transferability analysis showed that networks trained in London, fine-tuned with only 1% of images in other cities, achieved performances similar to ones from trained on data from target cities themselves. Our findings demonstrate that street imagery has the potential complement traditional survey-based and administrative data sources for high-resolution urban surveillance to measure inequalities and monitor the impacts of policies that aim to address them.
Project description:High levels of 'excess' mortality (ie, that seemingly not explained by deprivation) have been shown for Scotland compared to England and Wales and, especially, for its largest city, Glasgow, compared to the similarly deprived English cities of Liverpool and Manchester. It has been suggested that this excess may be related to differences in 'Sense of Coherence' (SoC) between the populations. The aim of this study was to ascertain whether levels of SoC differed between these cities and whether, therefore, this could be a plausible explanation for the 'excess'.Three post-industrial UK cities: Glasgow, Liverpool and Manchester.A representative sample of more than 3700 adults (over 1200 in each city).SoC was measured using Antonovsky's 13-item scale (SOC-13). Multivariate linear regression was used to compare SoC between the cities while controlling for characteristics (age, gender, SES etc) of the samples. Additional modelling explored whether differences in SoC moderated city differences in levels of self-assessed health (SAH).SoC was higher, not lower, among the Glasgow sample. Fully adjusted mean SoC scores for residents of Liverpool and Manchester were, respectively, 5.1 (-5.1 (95% CI -6.0 to -4.1)) and 8.1 (-8.1 (-9.1 to -7.2)) lower than those in Glasgow. The additional modelling confirmed the relationship between SoC and SAH: a 1 unit increase in SoC predicted approximately 3% lower likelihood of reporting bad/very bad health (OR=0.97 (95% CI 0.96 to 0.98)): given the slightly worse SAH in Glasgow, this resulted in slightly lower odds of reporting bad/very bad health for the Liverpool and Manchester samples compared to Glasgow.The reasons for the high levels of 'excess' mortality seen in Scotland and particularly Glasgow remain unclear. However, on the basis of these analyses, it appears unlikely that a low SoC provides any explanation.
Project description:The appearance of large geolocated communication datasets has recently increased our understanding of how social networks relate to their physical space. However, many recurrently reported properties, such as the spatial clustering of network communities, have not yet been systematically tested at different scales. In this work we analyze the social network structure of over 25 million phone users from three countries at three different scales: country, provinces and cities. We consistently find that this last urban scenario presents significant differences to common knowledge about social networks. First, the emergence of a giant component in the network seems to be controlled by whether or not the network spans over the entire urban border, almost independently of the population or geographic extension of the city. Second, urban communities are much less geographically clustered than expected. These two findings shed new light on the widely-studied searchability in self-organized networks. By exhaustive simulation of decentralized search strategies we conclude that urban networks are searchable not through geographical proximity as their country-wide counterparts, but through an homophily-driven community structure.
Project description:Despite global connectivity, societies seem to be increasingly polarized and fragmented. This phenomenon is rooted in the underlying complex structure and dynamics of social systems. Far from homogeneously mixing or adopting conforming views, individuals self-organize into groups at multiple scales, ranging from families up to cities and cultures. In this paper, we study the fragmented structure of American society using mobility and communication networks obtained from geo-located social media data. We find self-organized patches with clear geographical borders that are consistent between physical and virtual spaces. The patches have multi-scale structure ranging from parts of a city up to the entire nation. Their significance is reflected in distinct patterns of collective interests and conversations. Finally, we explain the patch emergence by a model of network growth that combines mechanisms of geographical distance gravity, preferential attachment and spatial growth. Our observations are consistent with the emergence of social groups whose separated association and communication reinforce distinct identities. Rather than eliminating borders, the virtual space reproduces them as people mirror their offline lives online. Understanding the mechanisms driving the emergence of fragmentation in hyper-connected social systems is imperative in the age of the Internet and globalization.
Project description:In China, the majority of food enterprises are small-sized and medium-sized. While the supervision costs are high, food safety issues are still emerging. Food circulation is an indispensable part in the entire food chain. At present, there are few studies on the regional spread of food safety risks in the circulation field from a macro perspective. This study combines GIS and social network analysis methods to deeply explore the regional circulation characteristics of substandard foods. First, we crawl the dataset of Food Safety Sampling Inspection Result Query System. Then we obtain the geographical locations of the manufacturers and distributors by GIS. Finally, we construct the province-level and city-level substandard foods' circulation networks, and employ social network analysis to target key cities and paths. The experimental results show that the circulations of substandard foods are characterized by dense province-level network and sparse city-level network, and they are mostly local and short-distance trafficking. 361 cities are divided into 13 city clusters considering the network connection characteristics. Chongqing, Beijing, Zhengzhou, and Changsha are identified as key cities by all measurement indicators, and at least four indicators can identify Shanghai and Wuhan. These cities have the highest priority for combating substandard foods' circulation networks.
Project description:<h4>Background</h4>The present study aimed to investigate the effects of a multi-level intervention on hookah smoking frequency and duration among Iranian adolescents and adults.<h4>Methods</h4>In this study, two comparable cities in Iran were selected to participate in an intervention program based on a social-ecological model (SEM). In each city, 133 hookah smokers in coffee houses were selected. Environmental changes in coffee houses such as serving light foods and games were conducted. A virtual group named "no hookah" was established on the Telegram application to train participants in the intervention group. Messages, pictures, and short videos were sent to the participants through that virtual network. The frequency and duration of hookah consumption were assessed in both groups at baseline and after the intervention.<h4>Results</h4>The frequency of hookah consumption decreased in 72.6% of participants in the intervention group (vs. 6.3% in the control group), and the duration of hookah consumption per session decreased in 39.5% of participants in the intervention group (vs. 5.5% in the control group).<h4>Conclusions</h4>Using multi-level interventions through a social-ecological model can reduce hookah consumption in adults.
Project description:BACKGROUND:Twitter represents a social media platform through which medical cannabis dispensaries can rapidly promote and advertise a multitude of retail products. Yet, to date, no studies have systematically evaluated Twitter behavior among dispensaries and how these behaviors influence the formation of social networks. OBJECTIVES:This study sought to characterize common cyberbehaviors and shared follower networks among dispensaries operating in two large cannabis markets in California. METHODS:From a targeted sample of 119 dispensaries in the San Francisco Bay Area and Greater Los Angeles, we collected metadata from the dispensary accounts using the Twitter API. For each city, we characterized the network structure of dispensaries based upon shared followers, then empirically derived communities with the Louvain modularity algorithm. Principal components factor analysis was employed to reduce 12 Twitter measures into a more parsimonious set of cyberbehavioral dimensions. Finally, quadratic discriminant analysis was implemented to verify the ability of the extracted dimensions to classify dispensaries into their derived communities. RESULTS:The modularity algorithm yielded three communities in each city with distinct network structures. The principal components factor analysis reduced the 12 cyberbehaviors into five dimensions that encompassed account age, posting frequency, referencing, hyperlinks, and user engagement among the dispensary accounts. In the quadratic discriminant analysis, the dimensions correctly classified 75% (46/61) of the communities in the San Francisco Bay Area and 71% (41/58) in Greater Los Angeles. CONCLUSIONS:The most centralized and strongly connected dispensaries in both cities had newer accounts, higher daily activity, more frequent user engagement, and increased usage of embedded media, keywords, and hyperlinks. Measures derived from both network structure and cyberbehavioral dimensions can serve as key contextual indicators for the online surveillance of cannabis dispensaries and consumer markets over time.
Project description:In the twenty-first century, ongoing rapid urbanization highlights the need to gain deeper insights into the social structure of cities. While work on this challenge can profit from abundant data sources, the complexity of this data itself proves to be a challenge. In this paper, we use diffusion maps, a manifold learning method, to discover hidden manifolds in the UK 2011 census dataset. The census key statistics and quick statistics report 1450 different statistical features for each census output area. Here, we focus primarily on the city of Bristol and the surrounding countryside, comprising 3490 of these output areas. Our analysis finds the main variables that span the census responses, highlighting that university student density and poverty are the most important explanatory variables of variation in census responses.
Project description:The COVID-19 pandemic before mass vaccination can be restrained only by the limitation of contacts between people, which makes the digital economy a key condition for survival. More than half of the world’s population lives in urban areas, and many cities have already transformed into “smart” digital/virtual hubs. Digital services ensure city life safe without an economy lockout and unemployment. Urban society strives to be safe, sustainable, well-being, and healthy. We set the task to construct a hybrid sociological and technological concept of a smart city with matched solutions, complementary to each other. Our modeling with the elaborated digital architectures and with the bionic solution for ensuring sufficient data governance showed that a smart city in comparison with the traditional city is tightly interconnected inside like a social “organism”. Society has entered a decisive decade during which the world will change by moving closer towards SDGs targets 2030 as well as by the transformation of cities and their digital infrastructures. It is important to recognize the large vector of sociological transformation as smart cities are just a transition phase to human-centered personal space or smart home. The “atomization” of the world urban population raises the gap problem in achieving SDGs because of different approaches to constructing digital architectures for smart cities or smart homes in countries. The strategy of creating smart cities should bring each citizen closer to SDGs at the individual level, laying in the personal space the principles of sustainable development and wellness of personality. <h4>Supplementary Information</h4> The online version contains supplementary material available at 10.1007/s11625-020-00889-5.