Proportion Of Urban Population Research Articles

Explaining COVID-19 mortality rates in the first wave in Europe

The beta regression has been received considerable attention in the last decade because of its applications to proportional data in several fields. We study the variability of coronavirus death rates in the first wave of twenty European countries using the beta regression with two systematic components for the mean and dispersion parameters. We prove empirically that the population density, proportion of urban population, hospital beds per 100 thousand and running time explain the variability of the COVID-19 death rates in the first wave of these countries.

Assessing Wealth-Related Inequalities in Demand for Family Planning Satisfied in 43 African Countries.

Background: Around 80% of the African population lives in urban areas, and a rapid urbanization is observed in almost all countries. Urban poverty has been linked to several sexual and reproductive health risks, including high levels of unintended pregnancies. We aim to investigate wealth inequalities in demand for family planning satisfied with modern methods (mDFPS) among women living in urban areas from African countries.Methods: We used data from 43 national health surveys carried out since 2010 to assess wealth inequalities in mDFPS. mDFPS and the share of modern contraceptive use were stratified by groups of household wealth. We also assessed the ecological relationship between the proportion of urban population living in informal settlements and both mDFPS and inequalities in coverage.Results: mDFPS among urban women ranged from 27% (95% CI: 23–31%) in Chad to 87% (95% CI: 84–89%) in Eswatini. We found significant inequalities in mDFPS with lower coverage among the poorest women in most countries. In North Africa, inequalities in mDFPS were identified only in Sudan, where coverage ranged between 7% (95% CI: 3–15%) among the poorest and 52% (95% CI: 49–56%) among the wealthiest. The largest gap in the Eastern and Southern African was found in Angola; 6% (95% CI: 3–11%) among the poorest and 46% (95% CI: 41–51%) among the wealthiest. In West and Central Africa, large gaps were found for almost all countries, especially in Central African Republic, where mDFPS was 11% (95% CI: 7–18%) among the poorest and 47% (95% CI: 41–53%) among the wealthiest. Inequalities by type of method were also observed for urban poor, with an overall pattern of lower use of long-acting and permanent methods. Our ecological analyses showed that the higher the proportion of the population living in informal settlements, the lower the mDFPS and the higher the inequalities.Conclusion: Our results rise the need for more focus on the urban-poorer women by public policies and programs. Future interventions developed by national governments and international organizations should consider the interconnection between urbanization, poverty, and reproductive health.

Meta-analysis and adjusted estimation of COVID-19 case fatality risk in India and its association with the underlying comorbidities

Management of coronavirus disease 2019 (COVID-19) in India is a top government priority.However, there is a lack of COVID-19 adjusted case fatality risk (aCFR) estimates and information on states with high aCFR. Data on COVID-19 cases and deaths in the first pandemic wave and 17 state-specific geodemographic, socio-economic, health and comorbidity-related factors were collected. State-specific aCFRs were estimated, using a 13-day lag for fatality. To estimate country-level aCFR in the first wave, state estimates were meta-analysed based on inverse-variance weighting and aCFR as either a fixed- or random-effect. Multiple correspondence analyses, followed by univariable logistic regression, were conducted to understand the association between aCFR and geodemographic, health and social indicators.Based on health indicators, states likely to report a higher aCFR were identified. Using random- and fixed-effects models, cumulative aCFRs in the first pandemic wave on 27 July 2020 in India were 1.42% (95% CI 1.19%–1.70%) and 2.97% (95% CI 2.94%–3.00%), respectively. At the end of the first wave, as of 15 February 2021, a cumulative aCFR of 1.18% (95% CI 0.99%–1.41%) using random and 1.64% (95% CI 1.64%–1.65%) using fixed-effects models was estimated. Based on high heterogeneity among states, we inferred that the random-effects model likely provided more accurate estimates of the aCFR for India. The aCFR was grouped with the incidence of diabetes, hypertension, cardiovascular diseases and acute respiratory infections in the first and second dimensions of multiple correspondence analyses. Univariable logistic regression confirmed associations between the aCFR and the proportion of urban population, and between aCFR and the number of persons diagnosed with diabetes, hypertension, cardiovascular diseases and stroke per 10,000 population that had visited NCD (Non-communicable disease) clinics. Incidence of pneumonia was also associated with COVID-19 aCFR. Based on predictor variables, we categorised 10, 17 and one Indian state(s) expected to have a high, medium and low aCFR risk, respectively. The current study demonstrated the value of using meta-analysis to estimate aCFR. To decrease COVID-19 associated fatalities, states estimated to have a high aCFR must take steps to reduce co-morbidities.

Geographical variation of emergency hospital admissions for ambulatory care sensitive conditions in older adults in Ireland 2012–2016

ObjectiveAmbulatory care sensitive (ACS) conditions are those for which intensified primary care management could potentially prevent emergency admissions. This study aimed to quantify geographical variation in emergency admissions with ACS...

Demographic and public health characteristics explain large part of variability in COVID-19 mortality across countries.

BackgroundThe numbers of coronavirus disease 2019 (COVID-19) deaths per million people differ widely across countries. Often, the causal effects of interventions taken by authorities are unjustifiably concluded based on the comparison of pure mortalities in countries where interventions consisting different strategies have been taken. Moreover, the possible effects of other factors are only rarely considered.MethodsWe used data from open databases (European Centre for Disease Prevention and Control, World Bank Open Data, The BCG World Atlas) and publications to develop a model that could largely explain the differences in cumulative mortality between countries using non-interventional (mostly socio-demographic) factors.ResultsStatistically significant associations with the logarithmic COVID-19 mortality were found with the following: proportion of people aged 80 years and above, population density, proportion of urban population, gross domestic product, number of hospital beds per population, average temperature in March and incidence of tuberculosis. The final model could explain 67% of the variability. This finding could also be interpreted as follows: less than a third of the variability in logarithmic mortality differences could be modified by diverse non-pharmaceutical interventions ranging from case isolation to comprehensive measures, constituting case isolation, social distancing of the entire population and closure of schools and borders.ConclusionsIn particular countries, the number of people who will die from COVID-19 is largely given by factors that cannot be drastically changed as an immediate reaction to the pandemic and authorities should focus on modifiable variables, e.g. the number of hospital beds.

Differentiation of Russian regions by life expectancy

The paper is devoted to the assessment of the relationship between regional life expectancy at birth (LE) with a number of economic and sociodemographic factors, in particular the gross regional product (GRP) and the share of urban population residing in large cities in the total population of the region. The analysis shows that the economic factor has a strong positive correlation with LE, especially in regions with unfavorable climatic conditions. The proportion of urban population residing in large cities in the total population of the region, influencing LE through improved health infrastructure and greater accessibility of health services to urban residents, merely complements the leading role of the economic factor.

Demystifying the varying case fatality rates (CFR) of COVID-19 in India: Lessons learned and future directions.

At the end of the second week of June 2020, the SARS-CoV-2 responsible for COVID-19 infected above 7.5 million people and killed over 400,000 worldwide. Estimation of case fatality rate (CFR) and determining the associated factors are critical for developing targeted interventions. The state-level adjusted case fatality rate (aCFR) was estimated by dividing the cumulative number of deaths on a given day by the cumulative number confirmed cases 8 days before, which is the average time-lag between diagnosis and death. We conducted fractional regression analysis to determine the predictors of aCFR. As of 13 June 2020, India reported 225 COVID-19 cases per million population (95% CI:224-226); 6.48 deaths per million population (95% CI:6.34-6.61) and an aCFR of 3.88% (95% CI:3.81-3.97) with wide variation between states. High proportion of urban population and population above 60 years were significantly associated with increased aCFR (p=0.08, p=0.05), whereas, high literacy rate and high proportion of women were associated with reduced aCFR (p<0.001, p=0.03). The higher number of cases per million population (p=0.001), prevalence of diabetes and hypertension (p=0.012), cardiovascular diseases (p=0.05), and any cancer (p<0.001) were significantly associated with increased aCFR. The performance of state health systems and proportion of public health expenditure were not associated with aCFR. Socio-demographic factors and burden of non-communicable diseases (NCDs) were found to be the predictors of aCFR. Focused strategies that would ensure early identification, testing and effective targeting of non-literate, elderly, urban population and people with comorbidities are critical to control the pandemic and fatalities.

The underlying influencing factors of farmland transfer in urbanizing China: implications for sustainable land use goals

Farmland property rights allocation such as farmland transfer has an important impact on economic development in rural areas and farmers’ livelihoods in developing countries. Examining the impacts of the external socioeconomic conditions helps realize the desired farmland transfer. This study attempts to analyze the underlying influencing factors of farmland transfer in urbanizing China. In particular, 18 underlying influencing factors are selected to describe urbanization and natural condition. Pearson correlation coefficients are computed to measure the linear relationships between underlying influencing factors and farmland transfer. The geographical detector method is further employed to quantify the individual effects of urbanization on farmland transfer and the interaction effects of urbanization on farmland transfer by assessing the degrees of spatial stratified heterogeneity. We discover that the effects of urban–rural basic living allowance gap, regional GDP per capita, urban population proportion on the proportion of transferred farmland area, proportion of farmland transferred to non-farmer users and proportion of transferred farmland cultivated non-grain crop are the strongest, respectively. The indices of urbanization level present have higher effects on farmland transfer than those of the urban–rural gap. Meanwhile, the interaction effect between two urbanization indices enhances the effect on the farmland transfer. Our study is believed to deepen the understanding of urbanization on farmland property rights allocation in rapidly urbanizing areas. It offers new insights into realizing the sustainable land use strategy in the socioeconomic transition in developing countries.

Spatial modeling of leprosy in the state of Bahia, Brazil, (2001-2015) and social determinants of health

This work analyzes the spatial distribution of leprosy in Bahia and associated social determinants. It is an ecological study, with leprosy data from 2001-2015. Three epidemiological indicators were selected: coefficient of detection in the general population and in children under 15 and the rate of new cases with grade II physical disability. These indicators were flattened by the Local Empirical Bayesian Model and Global and Local Moran statistics were applied. The independent variables were selected from the IBGE-2010 Census. Multivariate regressions were employed, followed by spatial regression. Leprosy exhibited a heterogeneous distribution in the state, with concentration in the north-west axis and the south region. For the general detection coefficient, five variables composed the final model: demographic density, urban population proportion, per capita income, proportion of extremely poor and households with over three people per dormitory. The illiteracy proportion made up the final model for the grade II rate of physical disability. No determinants of the occurrence of the disease were identified in children under 15. The modeling used contributed to demonstrate the spatial heterogeneity and social determinants of the disease in Bahia, revealing the complexity of the problem.

An Analysis on the Coordination between Urbanization of Population and Development of Real Estate Industry

Since the reform and opening up, the urbanization of population is accelerating, and more and more people are flowing to the big cities from the countryside. The proportion of urban population and rural population has undergone significant changes, which has also caused a major impact on China's economy, especially in the real estate industry. The population from the countryside to the city urgently needs houses, which is also a key reason for the urbanization of population in the real estate industry. This paper explains the concept of the urbanization of population and the reasons for the imbalance between real estate and the urbanization of population, and analyzes the relevant theories of the urbanization of population and the coordination between real estate industry and the urbanization of population. It also predicts the future developing trend of China's urbanization of population and real estate industry, and proposes some strategies for developing the real estate industry.

Impact of rapid urbanization on temporal and spatial pattern change of heavy rainfall in China during the past 60 years

Through the study of the daily heavy rainfall observation records of 659 meteorological stations in China from 1951 to 2010, we find that rapid urbanization may trigger the significant increase of large-scale heavy rainfall in China. The main conclusions are as follows: Decadal heavy rainfall amounts (HRA), heavy rainfall days (HRD) and heavy rainfall intensity (HRI) in China have increased significantly, with an increase of 68.71%, 60.15% and 11.52% respectively, and the increase of station number is 84.22%, 84.22% and 54.48% respectively, showing a time change process of “rapid slow re rapid increase” and a spatial change from Southeast Coast to Central, Southwest, North and Northeast China. The rapid urbanization factors, including the output value of the second industry (GDP2), the proportion of urban population (UP) and the annual average haze days (HD), are likely to be the main reasons for the increase of heavy rainfall in China. Their variance explanations for the HRA, HRD and HRI in China are 61.54%, 58.48% and 65.54% respectively, of which the variance explanations for the heavy rainfall rainfall, rainy day and rain intensity are respectively high only by haze 25.93%, 22.98% and 26.64% respectively, while the variance interpretation of climate factors, including WPSH, ENSO, AMO and AAO, is only 24.30%, 26.23% and 21.92% respectively. Compared with the forcing factors of rapid urbanization, the impact of these climate factors is only 1/3 of the former. The panel data of the annual mean of the total population and visibility days at the county level in China are significantly related to the HRA, HRD and HRI in China. The spatial correlation coefficient gradually increased from 1950s to 2000s, that is, the total population at the county level increased from 0.35, 0.36 and 0.40 to 0.54, 0.55 and 0.58 respectively, and the annual mean of visibility days increased from 0.36, 0.38 and 0.48 to 0.55, 0.57 and 0.58. It is further indicated that the rapid urbanization triggered the significant increase of heavy rainfall in China.

A model-based investigation into urban-rural disparities in tuberculosis treatment outcomes under the Revised National Tuberculosis Control Programme in India.

In this study, we investigate differences in tuberculosis (TB) treatment outcomes between urban and rural India and estimate their impact on epidemiological outcomes such as TB incidence, prevalence and mortality using a mathematical model of TB transmission dynamics. Publicly available district-level treatment outcomes data for new and previously treated TB cases was analyzed in conjunction with census data providing the proportion of urban population in each district to determine the effect of urbanity/rurality on treatment outcomes. Districts were grouped in clusters based on the proportion of urban population in each district, wherein the clusters were identified by applying machine learning methods. Regression analyses revealed that average treatment success rates among both new and previously treated cases decline with increase in the proportion of urban population in a district cluster, with substantially sharper declines in treatment success rates with degree of urbanity observed for previously treated cases. The impact of differences in treatment outcomes on epidemiological outcomes was estimated using a dynamic transmission model developed for this purpose. For example, the cluster with highest treatment success rates is projected to have an average of 3.2% fewer deaths per 100,000 population in comparison with the national average across 2019–24, and the cluster with the lowest treatment success rates has an average of 4.5% more deaths per 100,000 in comparison with the national average. We anticipate that these disparities in TB treatment outcomes and epidemiology between urban and rural India may motivate investigations into the associated causes and their redressal.

Mapping Public Concerns of Electronic Cigarettes in China

ABSTRACTE-cigarettes have gained growing visibility in recent years. However, there lacks a panoramic image of e-cigarette concerns, particularly in low-income countries, such as China. Studying the public concern at a macro level may help reveal the development of the e-cigarette market, and potential health burden of a province as a whole, which is crucial for the policy regulation of the e-cigarette market, and for the allocation of medical resources in the future. In this research, we attempt to provide the first representative portrait of e-cigarette concerns by using the index of e-cigarette search (ICS), which is constructed by online query of e-cigarette-related words/phrases of 31 provinces spanning from 2011 to 2014 in Baidu, the most popular search engine in China. E-cigarette concerns in all Chinese provinces increased over the time span. Our results from fixed-effects models further show that ICS is positively associated with the number of beds in medical institutions and road density of the province, but negatively associated with the proportion of urban population. In sum, provincial health care, infrastructure and urbanization are pivotal in shaping e-cigarette concerns at the provincial level in China.

Enabling Social Inclusion and Urban Citizenship of Older Adults through eHealth: The iZi Project in the Hague

While the elderly constitute a significant proportion of urban population, they are often not included in the decision-making processes concerning their health requirements. These exclusionary practices could be viewed as reflecting deficits in urban citizenship as well as a denial of what the French sociologist Henri Lefebvre defined in 1968 as the ‘right to the city’ (Lefebvre, 1968). This article is concerned with promoting the social inclusion of the elderly in urban spaces. It focuses on the potential of eHealth to facilitate their independent living in their own homes, an expressed priority of the elderly. It discusses a pilot project pioneered by the Municipality of The Hague where attention and space was given for the elderly to express their physical and emotional needs in different fora with relevant stakeholders, and reflect on ways in which eHealth could be of help to them. These ideas were important in creating the iZi Experience Home project, which also served as an important tool for creating awareness, enthusiasm and information about the possibilities of technology. The article examines the different processes involved in the development of eHealth applications, including the nature of the deliberations, the devices evolved and tried out in the homes of the elderly. Such methods also raised understanding regarding the challenges of using eHealth, such as the barriers faced by service providers, the costs associated with the gadgets and the resistance of caregivers to these techniques. The project demonstrated that traditional eHealth applications were indeed important in supporting the elderly through increased mobility, security and ability to remain in their homes. But these need to be complemented by community generation, spaces for sharing experiences and physical face-to-face interactions to bring about more comprehensive well-being and happiness. There is therefore the need to broaden the concept of eHealth to move beyond technical solutions only but to include the ideas of the patients, in this case the elderly, in policies, discussions with stakeholders, innovations and practices. In these ways, the elderly are supported to claim their rights to the city. The discussion contributes to understanding the challenges of exercising urban enacted citizenship amongst the elderly, and the need to include inclusion and democratic participation as rights and norms of ‘age-friendly’ cities.

Coupling Coordinated Development and Exploring Its Influencing Factors in Nanchang, China: From the Perspectives of Land Urbanization and Population Urbanization

The coordination relationship between land urbanization and population urbanization is crucial for achieving sustainable development under economic transition. Moreover, the balance between land urbanization and population urbanization is essential to guarantee the urbanization process of an entire city. This paper empirically analyzes the interaction between land urbanization and population urbanization in Nanchang from 2002 to 2017 based on the coupling coordination model (CCM). The impacts of the coordination degree on coordinated development are quantified by multivariate linear regression (MLR). The results show the following: (1) The indices of land urbanization and population urbanization in Nanchang showed an upward trend, and therefore the coordination degree in Nanchang increased significantly from 2002 to 2017. (2) The coordinated development of urbanization underwent two stages: disordered and moderately disordered. (3) The urban population proportion and the supporting capability of agricultural production had a positive impact on coordinated development. Meanwhile, the results also show that per capita education expenditures and the per capita public green area had negative impacts on the coordination degree, while economic development and the urban industrial structure were positive contributors to the coordination degree. Finally, this paper proposes that policies should be formulated to achieve coordinated development of urbanization. It can be concluded that the results regarding coordinated development of urbanization can help decision makers formulate effective measures to achieve coordinated development in the future.

Research on the Efficiency of Local Government Health Expenditure in China and Its Spatial Spillover Effect

The efficiency of the local government health expenditure (GHE) in China determines the level of public health services. However, the local government does not pay much attention to that efficiency, though the scale of local GHE is increasing. In this paper, first, we use the data envelopment analysis (DEA) method to measure the static overall efficiency of the local government health expenditure (GHE) in each region of China from 2007 to 2016. Then, based on the spatial statistical theory, global and local spatial Moran’s I value is utilized to investigate its spatial correlation and spatial agglomeration phenomenon. Finally, the spatial spillover effect (SSE) of the static overall efficiency of local GHE in each region is measured by constructing a spatial Durbin model (SDM). It is demonstrated that there are significant differences in the efficiency of the local GHE between different regions of China. In addition, it is shown that Moran’s I value of the static overall efficiency of the local GHE from 2007 to 2016 is positive. It passed the test of the 5% significance level, indicating that there is a positive spatial correlation between the efficiency of the local GHE and a spatial spillover effect. On the other hand, the decomposition of the SDM reveals that the proportion of GHE to financial expenditure, gross domestic product (GDP) per capita, and population density have a positive effect on the efficiency of the local GHE. Hence, their growth will improve the GHE efficiency in the local region and neighboring regions. In contrast, the proportion of urban population, illiteracy, and fiscal decentralization have a negative effect. Thus, their growth will decrease the GHE efficiency in the local region and neighboring regions. The results are discussed and suggestions are given based on the analysis in this paper. The main contribution of this work is to consider the spatial spillover effect in terms with realistic meaning. The results obtained can be used as a reference for optimizing the structure and improving the efficiency of government health inputs. It breaks the government’s GDP-only theory-based assessment system and helps to improve it by assessing the GHE efficiency.

Examining the influences of urbanization on carbon dioxide emissions in the Yangtze River Delta, China: Kuznets curve relationship

Exploring the impacts of urbanization on carbon dioxide emissions is a task that is of great significance in efforts to build low-carbon cities in China. While existing literature has identified links between carbon dioxide emissions and urbanization, the impacts of various mechanisms in various urbanization subsystems (economic, demographic, spatial, and social urbanization) on carbon dioxide emissions remain largely unexplored. This study conducts a comprehensive assessment of the multiple effects of urbanization on carbon dioxide emissions in the Yangtze River Delta, based on city panel remote sensing and statistical data covering the years 1992 to 2013. A stepwise panel data model was applied in combination with environmental Kuznets curve theory, so as to address four aspects of urbanization. The results show that carbon dioxide emissions increased notably in the Yangtze River Delta from 141.01 million tons in 1992 to 1448.28 million tons in 2013, and Shanghai and Suzhou were the largest two emitters in region. The impacts of urbanization on carbon dioxide emissions varied with different subsystems of urbanization. Economic urbanization exerted a positive impact in relation to carbon dioxide emissions with the relation between carbon dioxide emissions and gross domestic product per capita showing a Kuznets curve association. Population urbanization has exerted two opposing effects on carbon dioxide emissions, and population density and the proportion of urban population play a negative and positive role respectively. Spatial urbanization was here positively associated with carbon dioxide emissions, a result of the effects of the construction of new infrastructures and the conversion of existing land uses. Social urbanization demonstrated negative correlations in relation to emissions, mainly by improving low-carbon awareness in the general public. The effects of social consumption were insignificant. In addition, a number of control variables were also estimated to have varied effects on carbon dioxide emissions. The different influences of urbanization must, our findings indicate be considered in the task of formulating emission reduction measures for the Yangtze River Delta. Our analysis casts new light on the importance of exploring the multiple effects of urbanization in relation to emissions, knowledge which is vital to building low-carbon cities.

Fully vs. partially integrated services for TB-HIV in Myanmar: a health services review and a cohort study.

<sec id="st1"> <title>SETTING</title> In Myanmar, integrated services for tuberculosis (TB) and human immunodeficiency virus (HIV) are operated either fully (as one single unit) or partially (each vertical programme provides both services). </sec> <sec id="st2"> <title>OBJECTIVE</title> To review the fully and partially integrated services for TB-HIV and to assess their TB treatment outcomes. </sec> <sec id="st3"> <title>METHODS</title> We analysed the compiled service database and conducted a cohort study on newly registered TB-HIV patients in 12 townships in Myanmar. </sec> <sec id="st4"> <title>RESULTS</title> No significant association between region/state and types of integration was detected. Townships with partially integrated services had a significantly larger population size, higher proportion of urban population as well as higher TB and TB-HIV case notification rates; however, the cartridge-based Xpert® MTB/RIF assay was used less frequently than in townships with fully integrated services. Treatment completion rate and unfavourable outcomes were not significantly different between the two models, although fully integrated services reported a higher cure rate. </sec> <sec id="st5"> <title>CONCLUSIONS</title> These mixed results suggest that during expansion of fully integrated services, the progress of treatment outcomes should be monitored carefully. </sec>.

Spatial-temporal Analysis of Tuberculosis Incidence in Burundi Using GIS

Tuberculosis is one of the most contagious diseases that has been present for over 5000 years and it is still one of the most significant public health problems. This paper is intended to employ GIS in analyzing spatial variations of tuberculosis incidence in Burundi highlighting the main hot spots. Also, the paper aims to evaluate the temporal changes of TB incidence during the period 2009-2017 and guide the resource allocation. For this purpose, data on tuberculosis incidence at both province and health district level were analyzed. Data on incidence rate of TB and demographic data were collected at province level. Also, data on cases of TB recorded at health district level were acquired. The collected data were analyzed at both temporal and spatial scale. Temporal analysis involved identifying the various trends of TB incidence rate in various Burundi provinces during the period 2009-2017. Spatial analysis comprised mapping spatial variations in TB incidence rates and their trend over the period 2009-2017 and TB incidence at health district level. Moreover, Hot Spot analysis was performed to delineate those districts of statistically significant hot spots in TB incidence in Burundi. The temporal analysis of TB incidence rate, at province level, revealed that during the period 2009-2017, Burundi provinces have experienced varied trends of TB incidence with an annual change rate ranging between (-32.9) and (+5.22) in case of TB in all clinical forms and between (-12.2) and (+1.1) in case of Pulmonary TB. TB incidence rates were found to be positively correlated with proportion of urban population and population density. Meanwhile, spatial analysis of TB cases, revealed that eastern parts of Burundi have been experiencing relatively low incidence rates of TB compared to other parts of the country. This was highlighted by Hot Spot analysis that revealed a tendency of Pulmonary TB cases to be clustered and a hot spot in Pulmonary TB incidence was clearly distinguished in western parts of Burundi. Spatial temporal analysis highlights the potentials of GIS in recognizing trends and spatial pattern of such a disease and may support designing and implementing control programs and guide the resource allocation.

The Scope of Earth-Observation to Improve the Consistency of the SDG Slum Indicator

The continuous increase in deprived living conditions in many cities of the Global South contradicts efforts to make cities inclusive, safe, resilient, and sustainable places. Using examples of Asian, African, and Latin American cities, this study shows the scope and limits of earth observation (EO)-based mapping of deprived living conditions in support of providing consistent global information for the SDG indicator 11.1.1 “proportion of urban population living in slums, informal settlements or inadequate housing”. At the technical level, we compare several EO-based methods and imagery for mapping deprived living conditions, discussing their ability to map such areas including differences in terms of accuracy and performance at the city scale. At the operational level, we compare available municipal maps showing identified deprived areas with the spatial extent of morphological mapped areas of deprived living conditions (using EO) at the city scale, discussing the reasons for inconsistencies between municipal and EO-based maps. We provide an outlook on how EO-based mapping of deprived living conditions could contribute to a global spatial information base to support targeting of deprived living conditions in support of the SDG Goal 11.1.1 indicator, when uncertainties and ethical considerations on data provision are well addressed.