Does an Urban Wilderness Promote Gentrification? A Case Study from Knoxville, Tennessee, USA

The Knoxville Urban Wilderness (KUW) is a successful example of a growing global movement to utilize vacant urban land as many cities “de-urbanize”. A key question is whether this particular kind of green space promotes social inequality via green gentrification. Our analysis shows how the KUW has affected nearby home prices. Socioeconomic data including income, educational attainment, and race is also presented to explore the possibility of gentrification in South Knoxville. Our findings do not support strong evidence of gentrification, which implies that lower-income households are benefiting from advances in environmental amenities. Other households in specific areas are benefiting from both increases in home values and from expansions of the KUW. These are encouraging results for urban planning efforts that seek to utilize large areas of vacant urban land while also having positive social and economic impacts.

Cloud4NFICA-Nearness Factor-Based Incremental Clustering Algorithm Using Microsoft Azure for the Analysis of Intelligent Meter Data

Intelligent electricity meters (IEMs) form a key infrastructure necessary for the growth of smart grids. IEMs generate a considerable amount of electricity data incrementally. However, on an influx of new data, traditional clustering task re-cluster all of the data from scratch. The incremental clustering method is an essential way to solve the problem of clustering with dynamic data. Given the volume of IEM data and the number of data types involved, an incremental clustering method is highly complex. Microsoft Azure provide the processing power necessary to handle incremental clustering analytics. The proposed Cloud4NFICA is a scalable platform of a nearness factor-based incremental clustering algorithm. This research uses the real dataset of Irish households collected by IEMs and related socioeconomic data. Cloud4NFICA is incremental in nature, hence accommodates the influx of new data. Cloud4NFICA was designed as an infrastructure as a service. It is visible from the study that the developed system performs well on the scalability aspect.

Privacy concerns with using public data for suicide risk prediction algorithms: a public opinion survey of contextual appropriateness

Purpose Existing algorithms for predicting suicide risk rely solely on data from electronic health records, but such models could be improved through the incorporation of publicly available socioeconomic data – such as financial, legal, life event and sociodemographic data. The purpose of this study is to understand the complex ethical and privacy implications of incorporating sociodemographic data within the health context. This paper presents results from a survey exploring what the general public’s knowledge and concerns are about such publicly available data and the appropriateness of using it in suicide risk prediction algorithms. Design/methodology/approach A survey was developed to measure public opinion about privacy concerns with using socioeconomic data across different contexts. This paper presented respondents with multiple vignettes that described scenarios situated in medical, private business and social media contexts, and asked participants to rate their level of concern over the context and what factor contributed most to their level of concern. Specific to suicide prediction, this paper presented respondents with various data attributes that could potentially be used in the context of a suicide risk algorithm and asked participants to rate how concerned they would be if each attribute was used for this purpose. Findings The authors found considerable concern across the various contexts represented in their vignettes, with greatest concern in vignettes that focused on the use of personal information within the medical context. Specific to the question of incorporating socioeconomic data within suicide risk prediction models, the results of this study show a clear concern from all participants in data attributes related to income, crime and court records, and assets. Data about one’s household were also particularly concerns for the respondents, suggesting that even if one might be comfortable with their own being used for risk modeling, data about other household members is more problematic. Originality/value Previous studies on the privacy concerns that arise when integrating data pertaining to various contexts of people’s lives into algorithmic and related computational models have approached these questions from individual contexts. This study differs in that it captured the variation in privacy concerns across multiple contexts. Also, this study specifically assessed the ethical concerns related to a suicide prediction model and determining people’s awareness of the publicness of select data attributes, as well as which of these data attributes generated the most concern in such a context. To the best of the authors’ knowledge, this is the first study to pursue this question.

Acceptance of New Land-Use Activities by Hmong and Khmu Ethnic Groups: A Case Study in Northern Lao People’s Democratic Republic

Reducing emissions from deforestation and forest degradation (REDD+) to address climate change has historically included little evaluation of how heterogeneous local communities respond to REDD+ interventions and new land-use activities. We assessed differences in the acceptance of new land-use activities as a function of livelihoods of the Hmong and Khmu ethnic groups in northern Lao People’s Democratic Republic, where REDD+ was implemented between 2011 and 2018. Our socioeconomic data, collected by a questionnaire-based survey and focal group discussions, showed that the Hmong more effectively incorporated support from REDD+ than the Khmu because the Hmong owned grazing land. Our findings highlight the importance of understanding the capabilities and characteristics of each ethnic group when implementing new land-use activities (i.e., designing and implementing alternative livelihoods) within a target area to ensure distributional equity in heterogeneous communities. Such a consideration should be included in land-use policy and also be a part of the social safeguards in the land-use sector.

Assessment of symptoms of depression, anxiety and stress in academics from higher education institutions under conditions of social distance

Mood disorders are among the most prevalent health problems affecting the young adult population, especially academics of higher education. This scenario may be even more evident with the social isolation related to COVID-19. The aim of this study was to investigate symptoms of stress, depression and anxiety in academics from private institutions of higher education, under social isolation. The study was performed using questionnaires to obtain socioeconomic data and the Anxiety, Depression and Stress Scale (DASS-21), using an online platform. The results demonstrated that most participants had symptoms of stress, anxiety or depression. People with mood disorders had severe scores on the DASS-21. In addition, there was a higher prevalence of severe and extremely severe symptoms in females when compared to males. Finally, there was a negative correlation with age, demonstrating that, as younger, higher the score. The participants had significant scores of mood disorders probably because the majority work and study. In addition, they probably increase the susceptibility to these conditions with COVID-19 pandemic. The predominant age corresponds to the so-called emerging adult phase and the majority of women is probably related to the fact that most health sciences courses participated in the research and had predominantly women.

Clustering of Socioeconomic Data in Hong Kong for Planning Better Community Health Protection

The concept of socioeconomic vulnerability has made a substantial contribution to the understanding and conceptualization of health risk. To assess the spatial distribution of multi-dimensional socioeconomic vulnerability in an urban context, a vulnerability assessment scheme was proposed to guide decision-making in disaster resilience and sustainable urban development to reduce health risk. A two-stage approach was applied in Hong Kong to identify subgroups among Tertiary Planning Units (TPU) (i.e., the local geographic areas) with similar characteristics. In stage 1, principal components analysis was used for dimension reduction and to de-noise the socioeconomic data for each TPU based on the variables selected, while in stage 2, Gaussian mixture modeling was used to partition all the TPUs into different subgroups based on the results of stage 1. This study summarized socioeconomic-vulnerability-related data into five principal components, including indigenous degree, family resilience, individual productivity, populous grassroots, and young-age. According to these five principal components, all TPUs were clustered into five subgroups/clusters. Socioeconomic vulnerability is a concept that could be used to help identify areas susceptible to health risk, and even identify susceptible groups in affluent areas. More attention should be paid to areas with high populous grassroots scores and low young-age score since they were associated with a higher mortality rate.

Déterminants de l’accès aux crédits agricole par les producteurs d’anacarde au nord-Bénin

Pour sécuriser les revenus agricoles des producteurs béninois, des mécanismes de financement sont mis en place dans la production d’anacarde. La présente étude identifie les déterminants de l’accès au financement par les producteurs d’anacarde. A cet elffet, des données socioéconomiques ont été collectées par enquête auprès de 160 producteurs de noix de cajou choisis aléatoirement dans deux communes du Nord-Bénin. La statistique descriptive a été utilisée pour caractériser les types de financement. Un modèle de régression Logit binaire a été estimé pour déterminer les variables influençant l’accès aux crédits par les producteurs d’anacarde. Les principaux résultats de cette étude indiquent trois types de financement sont obtenus par les producteurs d’anacarde et financés par divers acteurs. Le sexe, l'âge, l'appartenance à une organisation, les contacts avec les services de vulgarisation et la formation à la production d’anacarde déterminent l'accès au crédit agricole au nord du Bénin. Enfin, a promotion du financement agricole dans la zone d’étude permettra de réguler la capacité à introduire l’agroforesterie dans l’exploitation agricole dans le contexte d’amélioration des conditions de vie et la protection des sols. English title: Determinants of access to agricultural credits by casnacard growers in north Benin In order to secure the agricultural income of producers in Benin, agricultural financing mechanisms have been put in place in cashew production. This study aimed at identifying the determinants of access to finance by cashew producers. To this end, socioeconomic data was collected through a survey of 160 cashew producers chosen at random from two municipalities in North Benin. Descriptive statistics were used to characterize the types of funding. A binary logit regression model was estimated to determine the variables influencing access to credit by cashew producers. The main results of this study indicate three types of financing obtained by cashew producers, which are financed by various actors. In addition, gender, age, membership of an organization, contacts with extension services and training in cashew production determine access to agricultural credit in northern Benin. Finally, the promotion of this financing in the study area will make it possible to regulate the capacity to introduce agroforestry on the farm, all of which contributes to the improvement of living conditions and soil protection.

Identification of Groundwater Contamination in a Rapidly Urbanized Area on a Regional Scale: A New Approach of Multi-Hydrochemical Evidences

Efficient identification of groundwater contamination is a major issue in the context of groundwater use and protection. This study used a new approach of multi-hydrochemical indicators, including the Cl-Br mass ratio, the hydrochemical facies, and the concentrations of nitrate, phosphate, organic contaminants, and Pb in groundwater to identify groundwater contamination in the Pearl River Delta (PRD) where there is large scale urbanization. In addition, the main factors resulting in groundwater contamination in the PRD were also discussed by using socioeconomic data and principal component analysis. Approximately 60% of groundwater sites in the PRD were identified to be contaminated according to the above six indicators. Contaminated groundwaters commonly occur in porous and fissured aquifers but rarely in karst aquifers. Groundwater contamination in porous aquifers is positively correlated with the urbanization level. Similarly, in fissured aquifers, the proportions of contaminated groundwater in urbanized and peri-urban areas were approximately two times that in non-urbanized areas. Groundwater contamination in the PRD was mainly attributed to the infiltration of wastewater from township-village enterprises on a regional scale. In addition, livestock waste was also an important source of groundwater contamination in the PRD. Therefore, in the future, the supervision of the wastewater discharge of township-village enterprises and the waste discharge of livestock should be strengthened to protect against groundwater contamination in the PRD.