Time, space, and the authorisation of sex premises in London and Sydney

While the regulation of commercial sex in the city has traditionally involved formal policing, recent shifts in many jurisdictions have seen sex premises of various kinds granted formal recognition via planning, licensing and environmental control. This means that ‘sexual entertainment venues’, ‘brothels’, or ‘sex shops’ are now not just labels applied to particular types of premise, but are formal categories of legal land use. However, these categories are not clear-cut, and it is not simply the case that changes in the law instantiate a change whereby these premises are bought into being at a particular point in time. Countering the privileging of space over time that is apparent within much contemporary research on sex and the city, this paper foregrounds the varied temporalities in play here, and describes how the actions of those policy-makers, municipal bureaucrats and officers allow sex premises to variously ‘fade in’, accelerate, linger or disappear as legal land uses within the city. We examine the implications of these different temporalities of the law by exploring how sex premises have been subject to regulation in London and Sydney, showing that the volatile, contradictory and fractured nature of legal space-making does not necessarily provide the certainty sought by the law but produces overlapping and contested understandings of what types of premise should be subject to regulation. More broadly the paper highlights how attention to the contingency and complexity of municipal law can help us better understand the ways that commercial sex is differently manifest in different citie

Modelling Grocery Retail Topic Distributions: Evaluation, Interpretability and Stability

Understanding the shopping motivations behind market baskets has high commercial value in the grocery retail industry. Analyzing shopping transactions demands techniques that can cope with the volume and dimensionality of grocery transactional data while keeping interpretable outcomes. Latent Dirichlet Allocation (LDA) provides a suitable framework to process grocery transactions and to discover a broad representation of customers' shopping motivations. However, summarizing the posterior distribution of an LDA model is challenging, while individual LDA draws may not be coherent and cannot capture topic uncertainty. Moreover, the evaluation of LDA models is dominated by model-fit measures which may not adequately capture the qualitative aspects such as interpretability and stability of topics. In this paper, we introduce clustering methodology that post-processes posterior LDA draws to summarise the entire posterior distribution and identify semantic modes represented as recurrent topics. Our approach is an alternative to standard label-switching techniques and provides a single posterior summary set of topics, as well as associated measures of uncertainty. Furthermore, we establish a more holistic definition for model evaluation, which assesses topic models based not only on their likelihood but also on their coherence, distinctiveness and stability. By means of a survey, we set thresholds for the interpretation of topic coherence and topic similarity in the domain of grocery retail data. We demonstrate that the selection of recurrent topics through our clustering methodology not only improves model likelihood but also outperforms the qualitative aspects of LDA such as interpretability and stability. We illustrate our methods on an example from a large UK supermarket chain.Comment: 20 pages, 9 figure

Assessing the impact of sporting mega-events on the social and physical capital of communities in host cities: the Gold Coast 2018 Commonwealth Games experience

Over the past decade there has been increasing research on how sporting mega-events such as the Olympic and Commonwealth Games are developing strategies, norms and rules to govern how they impact the host nation, city and communities, and in particular their impacts on economic, social, physical, human and cultural capital. This paper addresses a gap within these interconnected fields by examining how the strategies, norms and rules used to govern a mega-event may impact the social and physical capitals of communities in the host city during and following a mega-event. These associations are revealed through a novel methodology that combines the Institutional Grammar Tool developed by Crawford and Ostrom and the Community Capitals Framework devised by Flora and Flora, to analyse policy documentation, complemented by 11 in-depth interviews on the refurbishment of the Broadbeach Lawn Bowls Club as a venue for the 2018 Commonwealth Games in the City of Gold Coast, Australia

Danger from the Outside:Resident Perceptions of Environmental Contamination at Home

Research examining human experiences of environmental contamination highlights the significance of place in influencing responses. However, a dearth of information exists on how indoor contamination affects experiences of living with legacies of land and groundwater pollution. This paper addresses this shortfall by drawing on evidence derived from an online survey, 10 semi-structured interviews, and a focus group to examine factors associated with lifescape change in home environments. The findings suggest that perceptions of the visibility and transferability of contaminants, and whether such contaminants are located in either indoor or outdoor domestic spaces, influence residents’ experiences, in turn. Through its focus on interactions between people and pollution, this article makes an original contribution to research on the spatial dynamics of individuals’ experiences with contamination. In concluding, this paper highlights the need for public health communication to provide clear guidance aimed at reducing feelings of uncertainty within domestic spheres

How do governance visions, institutions and practices enable urban sustainability transformations? A study of Battambang and Sihanoukville, Cambodia

Whilst research has highlighted the challenges of rapid urbanization in Cambodia, few studies have focused on increased interest within Cambodia on how reforming urban governance can support urban sustainability transformations. Addressing this research gap, this study explores how urban governance might enable sustainability transformations in two second-tier cities—Battambang and Sihanoukville—in Cambodia, based on the analysis of open-ended interviews with fifty-five representatives involved in the development and implementation of urban sustainability plans and policies for these cities. The findings identify how urban governance visions, institutions and practices can be strengthened to enable sustainability transformations within these cities. The study highlights that alignment between the three tiers of governance—meta-governance (visions and worldviews), second-tier (structural and institutional) and third-tier (day-to-day interactions) is needed for urban sustainability transformations

Chapitre 1 - Contexte : une étude comparée sur la planification spatiale de l’artificialisation des sols en France et en Australie

Introduction « Les sols sont partout… sous nos villes et nos périphéries urbaines » (Hazelton et Murphy, 2011, p. 14). L’artificialisation anthropique des sols par des surfaces artificielles impénétrables, à travers des processus tels que l’urbanisation, est de plus en plus reconnue comme interférant avec les fonctions environnementales, économiques et sociales essentielles remplies par les sols (Scalenghe et Marsan, 2009 ; Salv..

Highly accurate model for prediction of lung nodule malignancy with CT scans

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://bioinformatics.astate.edu/NoduleX

Regional Topics in British Grocery Retail Transactions

Understanding the customer behaviours behind transactional data has high commercial value in the grocery retail industry. Customers generate millions of transactions every day, choosing and buying products to satisfy specific shopping needs. Product availability may vary geographically due to local demand and local supply, thus driving the importance of analysing transactions within their corresponding store and regional context. Topic models provide a powerful tool in the analysis of transactional data, identifying topics that display frequently-bought-together products and summarising transactions as mixtures of topics. We use the Segmented Topic Model (STM) to capture customer behaviours that are nested within stores. STM not only provides topics and transaction summaries but also topical summaries at the store level that can be used to identify regional topics. We summarised the posterior distribution of STM by post-processing multiple posterior samples and selecting semantic modes represented as recurrent topics. We use linear Gaussian process regression to model topic prevalence across British territory while accounting for spatial autocorrelation. We implement our methods on a dataset of transactional data from a major UK grocery retailer and demonstrate that shopping behaviours may vary regionally and nearby stores tend to exhibit similar regional demand

Posterior summaries of grocery retail topic models: Evaluation, interpretability and credibility

Understanding the shopping motivations behind market baskets has significant commercial value for the grocery retail industry. The analysis of shopping transactions demands techniques that can cope with the volume and dimensionality of grocery transactional data while delivering interpretable outcomes. Latent Dirichlet allocation (LDA) allows processing grocery transactions and the discovering of customer behaviours. Interpretations of topic models typically exploit individual samples overlooking the uncertainty of single topics. Moreover, training LDA multiple times show topics with large uncertainty, that is, topics (dis)appear in some but not all posterior samples, concurring with various authors in the field. In response, we introduce a clustering methodology that post-processes posterior LDA draws to summarise topic distributions represented as recurrent topics. Our approach identifies clusters of topics that belong to different samples and provides associated measures of uncertainty for each group. Our proposed methodology allows the identification of an unconstrained number of customer behaviours presented as recurrent topics. We also establish a more holistic framework for model evaluation, which assesses topic models based not only on their predictive likelihood but also on quality aspects such as coherence and distinctiveness of single topics and credibility of a set of topics. Using the outcomes of a tailored survey, we set thresholds that aid in interpreting quality aspects in grocery retail data. We demonstrate that selecting recurrent topics not only improves predictive likelihood but also outperforms interpretability and credibility. We illustrate our methods with an example from a large British supermarket chain