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The burden of type 2 diabetes (T2D) differs between socioeconomic groups. The present study combines ongoing and plausible trends in T2D incidence and survival by income to forecast future trends in cases of T2D and life expectancy with and without T2D up to year 2040. Using Finnish total population data for those aged 30 years on T2D medication and mortality in 1995–2018, we developed and validated a multi-state life table model using age-, gender-, income- and calendar year-specific transition probabilities. We present scenarios based on constant and declining T2D incidence and on the effect of increasing and decreasing obesity on T2D incidence and mortality states up to 2040. With constant T2D incidence at 2019-level, the number of people living with T2D would increase by about 26% between 2020 and 2040. The lowest income group could expect more rapid increases in the number with T2D compared to the highest income group (30% vs. 23% respectively). If the incidence of T2D continues the recent declining trend, we predict about 14% fewer cases. However, if obesity increases two-fold, we predict 15% additional T2D cases. Unless, we reduce the obesity-related excess risk, the number of years lived without T2D could decrease up to 6 years for men in the lowest income group. Under all plausible scenarios, the burden of T2D is set to increase and it will be unequally distributed among socioeconomic groups. An increasing proportion of life expectancy will be spent with T2D.

Compared to previous SARS-CoV-2 variants, the Omicron variant exhibited different epidemiological features. The purpose of this study was to assess the wave dynamics of pre-Omicron and Omicron waves in terms of differences and similarities. We investigated the COVID-19 waves since the beginning of the pandemic up to 28 August 2022, 1000 waves in total, as to their effectiveness for flattening the curve, calculated from the first and second time derivative of the daily case data. The average number of Omicron waves per month (42.78) was greater than the one of pre-Omicron waves per month (25.62). Omicron waves steepen and flatten the curve significantly faster, more effectively and with sharper peaks. Omicron waves generated more daily case data than pre-Omicron waves; the pre-Omicron trend showed increasing numbers over time, whereas the Omicron trend showed decreasing numbers. In denser populated countries, pre-Omicron waves were managed more effectively, in contrast to Omicron waves which were managed less effectively (but more effectively in less densely populated countries). This study provides the evidence for a different behaviour of Omicron waves in terms of wave dynamics, and thereby confirms that the Omicron variant is not only genetically different but even more so in terms of epidemiological dynamics. [ABSTRACT FROM AUTHOR]

There are now an estimated 114 million forcibly displaced people worldwide, some 88% of whom are in low- and middle-income countries. For governments and international organizations to design effective policies and responses, they require comparable and accessible socioeconomic data on those affected by forced displacement, including host communities. Such data is required to understand needs, as well as interactions between complex drivers of displacement and barriers to durable solutions. However, high-quality data of this kind takes time to collect and is costly. Can the ever-increasing volume of open data and evolving innovative techniques accelerate and enhance its generation? Are there applications of alternative data sources, advanced statistics, and machine-learning that could be adapted for forced displacement settings, considering their specific legal and ethical dimensions? As a catalytic bridge between the World Bank and UNHCR, the Joint Data Center on Forced Displacement convened a workshop to answer these questions. This paper summarizes the emergent messages from the workshop and recommendations for future areas of focus and ways forward for the community of practice on socioeconomic data on forced displacement. Three recommended areas of future focus are: enhancing and optimizing household survey sampling approaches; estimating forced displacement socioeconomic indicators from alternative data sources; and amplifying data accessibility and discoverability. Three key features of the recommended approach are: strong complementarity with the existing data-collection-to-use-pipeline; data responsibility built-in and tailored to forced displacement contexts; and iterative assessment of operational relevance to ensure continuous focus on improving outcomes for those affected by forced displacement.

Paediatric psoriasis has been associated with school absenteeism, limitation of physical activities, psychiatric disorders and, in the longer term, with sexual dysfunction and addictions. This raises the hypothesis that childhood onset psoriasis may affect patients’ educational development, and further social and professional outcomes. This study evaluated the relationship between childhood onset psoriasis and patients’ educational and socioeconomic characteristics, and the development of addictions in adulthood. This cross-sectional ancillary study captured patients’ characteristics at baseline in the French PSOBIOTEQ registry. Data in adulthood included: educational (baccalaureate) and socioeconomic (working activity) groups, smoking status (self-reporting of being a current smoker vs past smoker or non-smoker), alcohol consumption (defined as at least 1 glass of alcoholic beverage per day), and living conditions (alone/family/social institutions; child at home). A total of 1,960 patients were included, of whom 26.2% had childhood onset psoriasis. In multivariate analyses, childhood onset psoriasis was associated with smoker status (p = 0.02). No association was observed with educational level, working activity, living conditions, or alcohol consumption. This study provides reassuring data overall with regard to the impact of childhood onset psoriasis on major social outcomes. Evidence for some association with addictive behaviours paves the way for larger prospective studies assessing in depth the social and educational impact of this disease.

Paediatric psoriasis has been associated with school absenteeism, limitation of physical activities, psychiatric disorders and, in the longer term, with sexual dysfunction and addictions. This raises the hypothesis that childhood onset psoriasis may affect patients' educational development, and further social and professional outcomes. This study evaluated the relationship between childhood onset psoriasis and patients' educational and socioeconomic characteristics, and the development of addictions in adulthood. This crosssectional ancillary study captured patients' characteristics at baseline in the French PSOBIOTEQ registry. Data in adulthood included: educational (baccalaureate) and socioeconomic (working activity) groups, smoking status (self-reporting of being a current smoker vs past smoker or non-smoker), alcohol consumption (defined as at least 1 glass of alcoholic beverage per day), and living conditions (alone/family/social institutions; child at home). A total of 1,960 patients were included, of whom 26.2% had childhood onset psoriasis. In multivariate analyses, childhood onset psoriasis was associated with smoker status (p = 0.02). No association was observed with educational level, working activity, living conditions, or alcohol consumption. This study provides reassuring data overall with regard to the impact of childhood onset psoriasis on major social outcomes. Evidence for some association with addictive behaviours paves the way for larger prospective studies assessing in depth the social and educational impact of this disease. [ABSTRACT FROM AUTHOR]

Giardiasis   is one of the major causes of diarrhea among children. To determine the prevalence, risk factors, and genotype of Giardia intestinalis , a cross-sectional descriptive study was done on stool samples of 462 children attending three monastery primary schools from North Okkalapa Township in Yangon, Myanmar from January 2016 to February 2019. Socioeconomic data were collected using a pre-tested questionnaire after obtaining informed consent. Direct wet mount, formalin-ether sedimentation, and trichrome staining techniques were used for the primary identification and then molecular identification was carried out by conventional polymerase chain reaction (PCR)-sequencing assay. G. intestinalis was identified in 11.7% (54/462) of students. There was no significant association with water source (p=0.948) and drinking untreated water (p=0.595). The infection was more common in children with low-educated parents, unsanitary garbage disposal practices, and no restrooms. All isolates were G. intestinalis assemblage B. This is the first study characterizing human isolates in a lower region of Myanmar, at the molecular level [MOU1]. These findings pointed out the high prevalence of G. intestinalis among primary school children from densely populated and low-resource settings., Competing Interests: Human subjects: Consent was obtained or waived by all participants in this study. The Ethical and Research Committee of the University of Medicine 2, Yangon issued approval 16/ERC-3 (12-2016. This research was done after obtaining ethical clearance from the Ethical and Research Committee of the University of Medicine 2, Yangon (16/ERC-3 (12-2016) on 9.12.2016. Written informed consent was obtained from the parents/guardians of participant children after a thorough explanation of the procedures of this study. Official approval was obtained from the Department of Education in the North Okkalapa Township for obtaining samples from school children. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Myint et al.)

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Bangladesh has undergone dramatic land use and land cover changes (LULCC) in recent years, but no quantitative analysis of LULCC drivers at the national scale exists so far. Here, we quantified the drivers of major LULCC in combination with biophysical and socioeconomic observations at the sub-district level. We used Landsat satellite data to interpret LULCC from 2000 to 2010 and employed a Global Surface Water Dataset to account for the influences of water seasonality. The results suggest that major LULCC in Bangladesh occur between agricultural land and waterbodies and between forest and shrubland. Exclusion of seasonal waterbodies can improve the accuracy of our LULCC results and driver analysis. Although the gross gain and loss of agricultural land are large on the local scale, the net change (gross gain minus gross loss) at a country scale is almost negligible. Climate dynamics and extreme events and changes in urban and rural households were driving the changes from forest to shrubland in the southeast region. The conversion from agricultural land to standing waterbodies in the southwest region was mainly driven by urban household dynamics, population growth, distance to cities and major roads, and precipitation dynamics. This study, which is the first effort accounting for water seasonality and quantifying biophysical and socioeconomic drivers of LULCC at the national scale, provides a perspective on overall LULCC and underlying drivers over a decadal time scale and national spatial scale and can serve as a scientific basis for developing land policies in Bangladesh. [ABSTRACT FROM AUTHOR]

Accessing high-resolution, timely socioeconomic data such as data on population, employment, and enterprise activity at the neighborhood level is critical for social scientists and policy makers to design and implement location-based policies. However, in many developing countries or cities, reliable local-scale socioeconomic data remain scarce. Here, we show an easily accessible and timely updated location attribute--restaurant--can be used to accurately predict a range of socioeconomic attributes of urban neighborhoods. We merge restaurant data from an online platform with 3 microdatasets for 9 Chinese cities. Using features extracted from restaurants, we train machine-learning models to estimate daytime and nighttime population, number of firms, and consumption level at various spatial resolutions. The trained model can explain 90 to 95% of the variation of those attributes across neighborhoods in the test dataset. We analyze the tradeoff between accuracy, spatial resolution, and number of training samples, as well as the heterogeneity of the predicted results across different spatial locations, demographics, and firm industries. Finally, we demonstrate the cross-city generality of this method by training the model in one city and then applying it directly to other cities. The transferability of this restaurant model can help bridge data gaps between cities, allowing all cities to enjoy big data and algorithm dividends. [ABSTRACT FROM AUTHOR]

The mapping of urban areas at regional to global scales is a crucial task due to its value for environmental monitoring, habitat and biodiversity conservation, and decision-making. In most current applications, two techniques (i.e., supervised classification and data fusion) are widely applied in large-scale urban mapping. However, the costly training sample collection, inadequate data-source descriptions, and diverse urban characteristics (e.g., shape, size, socioeconomic status, and physical environment) are challenging problems for the urban mapping approaches. In this context, aiming at effectively deriving accurate urban areas at a large scale, we propose a novel ensemble support vector machine (SVM) method which consists of three steps: 1) the automatic generation of training data to reduce labor costs; 2) the construction of an ensemble SVM model to effectively combine the multisource data (including remote sensing and socioeconomic data); and 3) an adaptive patch-based thresholding technique to tackle the diverse urban characteristics. The proposed method is employed to map urban areas of China in 2005 and 2010, and the resulting maps are compared with the existing urban maps for 287 prefecture-level cities. It is found that our results present a satisfactory superiority, especially in challenging small cities, with a significant improvement in median Kappa (0.174 for 2005 and 0.203 for 2010). When incorporating moderate-resolution imaging spectroradiometer multispectral data as an additional source, the Kappa coefficient can be further raised by 0.028 for 2010. In general, the proposed method shows great potential for accurately mapping urban areas at regional, continental, or even global scales in a cost-effective manner. [ABSTRACT FROM AUTHOR]

Conservation actions (as Marine Protected Areas) are key tools to maintain coastal ecosystems. However, many reserves are characterized by several problems related to inadequate zonings that preclude important areas from economic activities, determining a strong hostility by local populations. Thus, estimations of marine economic values-in-use are needed for protection of marine ecosystem in order to find the best compromise between conservation priorities and local population needs. Algorithms to estimate monetary values of the main human activities in marine territories (large scale and small scale fishings, aquaculture, beach resorts, yachting, diving and commercial shipping) are here implemented using Gulf of Naples (centre Tyrrhenian sea, Italy) as study area example. These algorithms are based on different sources data (questionnaires, monitoring activities, official local authority reports, web and scientific literature). They can also be compared with each other being their outputs all expressed in the same measure unit. During the models development process a new flexible approach, called “Systematic Costs Assessment” (SCA), to assess opportunity costs in systematic conservation planning process was developed and applied. Results show that the total turnover in the Gulf of Naples is 3,950,753,487 € per year and 747,647,887 € per year excluding small scale fishing estimation, and one hectare of marine territory is worth 40,672 € and 7696 € per year excluding small scale fishing activity. In particular, excluding small scale fishing activity, beach resort and yachting show the highest values referred to one hectare of marine territories. In conclusion, SCA is a flexible approach where no long and costly sampling campaigns are always needed, provided that two assumptions have to be taken into account, in order to estimate credible values-in-use costs: i) do not use economic activities data and ecosystem services data in the same assessment layer, since it could lead to costs overestimation and ii) SCA method are efficient when used by operators with strong knowledge of the study area, since they are able to recognize parameters affecting economic activities of local population. [ABSTRACT FROM AUTHOR]

The burden of type 2 diabetes (T2D) differs between socioeconomic groups. The present study combines ongoing and plausible trends in T2D incidence and survival by income to forecast future trends in cases of T2D and life expectancy with and without T2D up to year 2040. Using Finnish total population data for those aged 30 years on T2D medication and mortality in 1995-2018, we developed and validated a multi-state life table model using age-, gender-, income- and calendar year-specific transition probabilities. We present scenarios based on constant and declining T2D incidence and on the effect of increasing and decreasing obesity on T2D incidence and mortality states up to 2040. With constant T2D incidence at 2019-level, the number of people living with T2D would increase by about 26% between 2020 and 2040. The lowest income group could expect more rapid increases in the number with T2D compared to the highest income group (30% vs. 23% respectively). If the incidence of T2D continues the recent declining trend, we predict about 14% fewer cases. However, if obesity increases two-fold, we predict 15% additional T2D cases. Unless, we reduce the obesity-related excess risk, the number of years lived without T2D could decrease up to 6 years for men in the lowest income group. Under all plausible scenarios, the burden of T2D is set to increase and it will be unequally distributed among socioeconomic groups. An increasing proportion of life expectancy will be spent with T2D., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Guzman-Castillo, Korhonen, Murphy and Martikainen.)

During the COVID-19 pandemic, most US states have taken measures of varying strength, enforcing social and physical distancing in the interest of public safety. These measures have enabled counties and states, with varying success, to slow down the propagation and mortality of the disease by matching the propagation rate to the capacity of medical facilities. However, each state's government was making its decisions based on limited information and without the benefit of being able to look retrospectively at the problem at large and to analyze the commonalities and the differences among the states and the counties across the country. We developed models connecting people's mobility, socioeconomic, and demographic factors with severity of the COVID pandemic in the US at the County level. These models can be used to inform policymakers and other stakeholders on measures to be taken during a pandemic. They also enable in-depth analysis of factors affecting the relationship between mobility and the severity of the disease. With the exception of one model, that of COVID recovery time, the resulting models accurately predict the vulnerability and severity metrics and rank the explanatory variables in the order of statistical importance. We also analyze and explain why recovery time did not allow for a good model. • We used the standard metrics of disease rate and mortality and defined three new metrics. • We formulated a Mobility Restraint Index (MRI) and analyzed its effect on disease severity. We combined it with other metrics to predict COVID severity at US county level. • Our models account for the main and joint effects of the featurs. They perform comparably with other models reported in literature. • We ranked the features used in our models in the order of their relative importance for each of the pandemic severity measures. • We confirmed importance of state-government responses to COVID measured by OxCGRT and identified values of OxCGRT metrics critical for disease mortality and spread. [ABSTRACT FROM AUTHOR]

Socioeconomic considerations are crucial in the design process of marine protected areas (MPAs). Most systematic planning processes that incorporate socioeconomic aspects mainly concentrate on extractive user interests by integrating spatial data on fisheries thus overlooking other interests such as non-extractive recreational uses of the marine environment such as wildlife observation, diving or kayaking. Additionally, most theory on systematic spatial conservation planning is focused on the design of single zone reserves. The present study, focused in Wales (UK), uses the systematic conservation software Marxan with Zones to quantify the benefits of integrating extractive and non-extractive interests in the planning process of MPAs and assesses whether the impacts on affected users differs between single vs. multiple zones MPAs. Results indicate that MPAs designed with consideration of non-extractive interests reduced the potential economic impacts on this sector by approximately 50% more than MPAs designed without that consideration, without extra cost to the extractive sector. The design of a multiple-zone MPA outperformed that of a single-zone MPA by reducing and generating more equitable impacts for both extractive and non-extractive interests. This study highlights the importance of including the interests of any groups that might be impacted by the designation of an MPA. [ABSTRACT FROM AUTHOR]

Different approaches exist for building a system of marine protected areas (MPAs), with stakeholder-based site selection at one end of the spectrum and science-based selection at the other. Although a combination of both approaches is typically adopted, the process tends to be dominated by one of them. However, for MPAs to be successful it is necessary that their design achieves a balance between both ecological conservation and socioeconomic needs. The present study aimed to assess, compare and integrate two different approaches to the planning process of MPAs in Wales (UK). A stakeholder-based approach and a science-based systematic approach were compared. Stakeholder priorities for the establishment of MPAs were identified during individual interviews with relevant stakeholders' representatives. Science-based solutions were developed using biological and socioeconomic spatial data in the decision support tool Marxan. A comparison of the outcomes generated by both approaches revealed that although the spatial configuration of the resulting MPAs differed, stakeholders performed well at including representative proportions of relevant marine habitats and species. The integration of the stakeholder driven approach with the science-based solution revealed that an integrated approach could be used as a tool to achieve conservation targets while simultaneously accounting for stakeholder's preferences, as the resulting integrated MPA solution met all conservation targets and was only slightly larger than the science-based solution alone. Results also revealed the potential utility of using stakeholders' knowledge as a proxy for identifying ecologically important areas when spatial data on conservation features are sparse. [ABSTRACT FROM AUTHOR]

A common objective in identifying conservation areas is to minimize conservation costs while achieving a set of conservation targets. Recent literature highlights the importance of incorporating socioeconomic costs into conservation planning. Here, we review how costs have been used in systematic marine conservation planning. Four approaches emerged from the literature: (1) uniform cost or area as a proxy for human use, (2) opportunity costs, (3) multiple socioeconomic costs, and (4) measures of naturalness or ecological impact of human activities. Most marine systematic conservation planning projects that used a spatially explicit socioeconomic cost focused on fisheries as the opportunity cost. No study has incorporated transaction or management costs into the design of marine protected areas using systematic conservation planning software. Combining multiple costs into one cost is one of the primary challenges of incorporating socioeconomic costs into conservation planning decision support tools. Combining many costs is feasible when each cost is measured in the same unit (e.g., dollars), but this information is rarely available in marine planning. Where the objective of the planning exercise is to minimize impacts on multiple stakeholder groups, the use of separate scenarios or multi-zone software may be a viable option. [ABSTRACT FROM AUTHOR]

Abstract: Systematic planning for conservation is highly regarded but relies on spatially explicit data that are lacking in many areas of conservation concern. The decision support tool Marxan is applied to a reef system in the central Philippines where 30 marine protected areas (MPAs) have been established in communities without much use of biophysical data. The intent was to explore how Marxan might assist with the legally required expansion to protect 15% of marine waters, and how existing MPAs might affect that process. Results show that biophysical information alone did not provide much guidance in identifying patterns of conservation importance in areas where the data are poor. Socioeconomic data were needed to distinguish among possible areas for protection; but here, as elsewhere in marine environments, the availability of such data was very limited. In the final analysis, local knowledge and integrated understanding of socioeconomic realities may offer the best spatially explicit information. The 30 existing MPAs, which encompassed a small proportion of the reef system, did not limit future options in developing a suite of MPAs on a broader scale. Rather, they appeared to generate the support for MPAs that is obligatory for any larger zoning effort. In summary, establishing MPAs based on community-driven criteria has biological and social value, but efforts should be made to collect ecological and socioeconomic data to guide the continued creation of MPAs. [Copyright &y& Elsevier]

Since 2000, researchers and staff at the University of Washington have been developing a unified Geographic Information Systems database of Russian Federation socioeconomic, demographic., and political information at the raion and city (gorsovet) level. The Central Eurasian Interactive Atlas (CEIA) that has grown from this effort serves both to archive and to provide access to annual progressions of standardized geographic and tabular data covering the entire Russian Federation at a level of geographic detail not readily available elsewhere. As the CEIA grows, it will serve as a curricular and research focus of the UW's Russian, East European, and Central Asian Studies program. [ABSTRACT FROM AUTHOR]

Regional economic impact models (REIMs) are needed for performance-based earthquake engineering and similar activities. The metropolitan-level performance measures that can be supported by earthquake engineering outputs are fundamentally economic, and regional economic impact models provide the logical link between engineering design decisions and policymaking. How well do REIMs consider the performance of structures (broadly defined to include infrastructure and lifelines) in these facilities’ role of facilitating regional economic performance? We have evaluated 19 REIMs of relatively recent vintage against 11 criteria. These include policy relevance, whether the model reports results for small spatial units, the level of industry disaggregation, the possibility to allow integration of models from various disciplines, whether multiperiod or dynamic analysis is possible, the extent to which key variables (including prices, technology and travel behavior) are endogenous, transferability between regions and countries, operationality, accessbility, updatability, and whether applications to earthquakes have been executed. [ABSTRACT FROM AUTHOR]

Objective: The purpose of the present study is to evaluate if the participants in psychosocial interventions for dementia caregivers are representative of the whole population of dementia patients or if some socioeconomic groups are over- or underrepresented. Design: The demographic and socioeconomic characteristics of the 128 participants of a randomized controlled study on the effects of caregiver education were compared with those of all the elderly residents of the City of Zurich (n = 64,856, elderly group), of all demented patients entering a City of Zurich nursing home in a 6-month period (n = 218, NH entry group) and of all demented inhabitants evaluated during a 20-month follow-up at a community memory clinic (n = 187, memory group). Methods: Data on income and wealth were derived from official tax records. The characteristics of the different groups were compared by χ² or t tests. Results: As expected in a study on caregiver education, the demented patients were younger, more often male and married than all other study groups (p < 0.01). The participants in the psychosocial intervention had significantly (p < 0.01) higher education than all other groups; this effect is caused in part by the higher proportion of males. The NH entry group was less well educated than the elderly group (p < 0.05). The intervention group had a higher income and was wealthier than the three other groups (p < 0.01), but there was no significant difference with respect to the wealth of the memory group. The 25% poorest of the elderly group made up only about 10% of the participants in the intervention group. However, the 25% richest of the elderly group made up 42% of the intervention group. The method of recruitment for the psychosocial intervention (by media, referral of physicians and by a memory clinic) was not significantly related to any of the demographic or socioeconomic parameters. Conclusion: The lower socioeconomic strata are clearly underrepresented in psychosocial interventions. [ABSTRACT FROM AUTHOR]

Air temperature (Ta) is an essential climatological component that controls and influences various earth surface processes. In this study, we make the first attempt to employ deep learning for Ta mapping mainly based on space remote sensing and ground station observations. Considering that Ta varies greatly in space and time and is sensitive to many factors, assimilation data and socioeconomic data are also included for a multi-source data fusion based estimation. Specifically, a 5-layers structured deep belief network (DBN) is employed to better capture the complicated and non-linear relationships between Ta and different predictor variables. Layer-wise pre-training process for essential features extraction and fine-tuning process for weight parameters optimization ensure the robust prediction of Ta spatio-temporal distribution. The DBN model was implemented for 0.01° daily maximum Ta mapping across China. The ten-fold cross-validation results indicate that the DBN model achieves promising results with the RMSE of 1.996 °C, MAE of 1.539 °C, and R of 0.986 at the national scale. Compared with multiple linear regression (MLR), back-propagation neural network (BPNN) and random forest (RF) method, the DBN model reduces the MAE values by 1.340 °C, 0.387 °C and 0.222 °C, respectively. Further analysis on spatial distribution and temporal tendency of prediction errors both validate the great potentials of DBN in Ta estimation. • Deep learning is utilized effectively to estimate Ta for the first attempt. • Fusing remote sensing, station, simulation and socioeconomic data do make sense. • The 0.01° daily maximum Ta across China has been generated accurately. [ABSTRACT FROM AUTHOR]

Accessing high-resolution, timely socioeconomic data such as data on population, employment, and enterprise activity at the neighborhood level is critical for social scientists and policy makers to design and implement location-based policies. However, in many developing countries or cities, reliable local-scale socioeconomic data remain scarce. Here, we show an easily accessible and timely updated location attribute-restaurant-can be used to accurately predict a range of socioeconomic attributes of urban neighborhoods. We merge restaurant data from an online platform with 3 microdatasets for 9 Chinese cities. Using features extracted from restaurants, we train machine-learning models to estimate daytime and nighttime population, number of firms, and consumption level at various spatial resolutions. The trained model can explain 90 to 95% of the variation of those attributes across neighborhoods in the test dataset. We analyze the tradeoff between accuracy, spatial resolution, and number of training samples, as well as the heterogeneity of the predicted results across different spatial locations, demographics, and firm industries. Finally, we demonstrate the cross-city generality of this method by training the model in one city and then applying it directly to other cities. The transferability of this restaurant model can help bridge data gaps between cities, allowing all cities to enjoy big data and algorithm dividends., Competing Interests: The authors declare no conflict of interest.

With rapid urbanization and economic development, artificial lighting at night brings convenience to human life but also causes a considerable urban environmental pollution issue. This study employed the Mann-Kendall non-parametric test, nighttime light indices, and the standard deviation method to investigate the spatio-temporal characteristics of artificial lighting in the Beijing-Tianjin-Hebei region. Moreover, nighttime light imagery from the Defense Meteorological Satellite Program Operational Linescan System, socioeconomic data, and high-resolution satellite images were combined to comprehensively explore the driving factors of urban artificial lighting change. The results showed the following: (1) Overall, there was an increasing trend in artificial lighting in the Beijing-Tianjin-Hebei region, which accounted for approximately 56.87% of the total study area. (2) The change in artificial lighting in the entire area was relatively stable. The artificial lighting in the northwest area changed faster than that in the southeast area, and the areas where artificial lighting changed the most were Beijing, Tianjin and Tangshan. (3) The fastest growth of artificial lighting was in Chengde and Zhangjiakou, where the rates of increase were 334% and 251%, respectively. The spatial heterogeneity of artificial lighting in economically developed cities was higher than that in economically underdeveloped cities such as Chengde and Zhangjiakou. (4) Multi-source data were combined to analyse the driving factors of urban artificial lighting in the entire area. The Average Population of Districts under City ( R 2 = 0.77) had the strongest effect on artificial lighting. Total Passenger Traffic ( R 2 = 0.54) had the most non-obvious effect. At different city levels, driving factors varied with differences of economy, geographical location, and the industrial structures of cities. Urban expansion, transportation hubs, and industries were the major reasons for the significant change in nighttime light. Urban artificial lighting represents a trend of overuse closely related to nighttime light pollution. This study of artificial lighting contributes to the rational planning of urban lighting systems, the prevention and control of nighttime light pollution, and the creation of liveable and ecologically green cities.