Medicine is a knowledge area continuously experiencing changes. Every day, discoveries and procedures are tested with the goal of providing improved service and quality of life to patients. With the evolution of computer science, multiple areas experienced an increase in productivity with the implementation of new technical solutions. Medicine is no exception. Providing healthcare services in the future will involve the storage and manipulation of large volumes of data (big data) from medical records, requiring the integration of different data sources, for a multitude of purposes, such as prediction, prevention, personalization, participation, and becoming digital. Data integration and data sharing will be essential to achieve these goals. Our work focuses on the development of a framework process for the integration of data from different sources to increase its usability potential. We integrated data from an internal hospital database, external data, and also structured data resulting from natural language processing (NPL) applied to electronic medical records. An extract-transform and load (ETL) process was used to merge different data sources into a single one, allowing more effective use of these data and, eventually, contributing to more efficient use of the available resources. Developments in medical research lead to an increase in the life expectancy of populations. Although this evolution leads to very positive outcomes, such as the end of some diseases  and the discovery of new approaches to several others, other challenges occur more often, such as dementia, cancer, etc. However, as medical knowledge grows and develops, other areas like computer science experience developments which can be used to support physicians in addressing these challenges. At present, healthcare providers produce and store large volumes of data, both medical and nonmedical. These data can regard drug prescriptions, treatment records, general check-up information, physician’s notes, medical information, or financial and administrative information. These data are essential not only to follow up on patients but also for management or research purposes. The knowledge that can be taken out of these data is therefore relevant for many purposes, such as service quality, by allowing the physician to access details regarding the patient that allow to improve diagnosis and decision-making. Service productivity can also benefit from this knowledge, as access to the required data is faster, letting resources increase the volume of their outcome and service control can be enhanced, by allowing disclosure of medical and performance data, among many other benefits. Data can be stored either in legacy systems or electronic medical records (EMR) . EMRs arIt is clear that analyzing data in an integrated way can drastically improve patient quality of care and both clinical and financial outcomes, but the way in which we collect, read, integrate, understand, and leverage data remains a broken process. Data integration and consequent analysis allows full approaches in healthcare diagnosis. A diverse range of prior studies focused their effort on analyzing data to predict waiting times, based on months, weekday, weather conditions, and external events like a football match or concert, among others . NaturalThe format in which this information is recorded is essential because it has a direct impact on how it can be modeled to provide greater insight. Three different types of data can be found: Structured, semistructured, and unstructured . DiffereData, information and knowledge are three commonly misunderstood concept words that are occasionally used as equals, which may be the cause of some misunderstandings . The thrWith the purpose of transforming data into knowledge so that medical professionals can improve the quality of the services they provide and the use of the time they have available, this work aims at developing a framework process to integrate data from different sources and make it available in a more effective way. In doing so, data from different medical records that result from a natural language processing (NPL) transformation are operated using an extract-transform and load (ETL) procedure to produce a single integrated file, allowing more effective use of that data. Addressing the goal of this research, this work starts with an analysis of the state-of-the-art in the area of data manipulation, considering the different types of data . Then, the methodology considered for the development of the framework is exposed. Three use cases are considered, two focused on structured data and a third one based on semistructured and unstructured data. The case studies that are presented concern the use of structured and unstructured data together and how the healthcare sector can gain value from the mixed use of these three types of data sets. The discussion is produced based on the findings from both use cases.Structured data are data that are stored in a fixed schema database, and out of the three data types, they are the easiest one to manage. The most common data in this scenario are demographic , admission and discharge dates, diagnosis codes (historic and current), procedure codes, laboratory results, medications, allergies, social information , and some vital signs  . The facThere is no consensual definition of data mining (DM). For example, according to Reference , DM is dThe unstructured data of an EMR are present in clinical notes, surgical records, discharge records, radiology reports, and pathology reports . ClinicaThere are some TM techniques that are quite common, such as the removal of stop words, the lowercase treatment. Preprocessing depends on the data in hand and what that data are intended for. In Reference , topic mIn Reference , both teIn Reference , a prediIn Reference , researcUntil now, both DM and TM case studies have been shown and analyzed. While in some, only structured data were used, others only use the structured data extracted from the unstructured free text from de NLP processing used. Recent research in the data processing  and biomThe need for preprocessing procedures for data standardization has been recently explored, in particular when considering CSV or other tabular formatted text sources. Arenas et al.  pointed http://w3c.github.io/csvw/ use-cases-and-requirements/). Regarding the scope of our work, some of the W3C recommendations should be underlined, in particular: (i) The relevance of associating syntactic and semantic information to each data column, and (ii) the relevance of defining only one data type for each column. The W3C work group suggested that the delivery of a dataset should be associated with metadata creation and sharing. Nonetheless, considering already available datasets, the workgroup stressed that the publication of metadata could benefit the user community and can be performed by an independent party, in addition to the dataset provider. The group argued that this possibility might enable the user community to benefit from each other’s efforts when it comes to dataset understanding and exchange.Aiming to facilitate data exchange and application interoperability through dataset reuse, this group was focused mainly on how metadata should be associated with a previously created dataset. The authors proposed the final recommendations based on the analysis of 25 case studies . An ontThe output of this process is data extracted of diseases, symptoms, and clinical procedures, from the EMRs’ narratives, outputting a file with all of the extractions concerning those domains. These data are transformed into a CSV file to be able to be merged with structured data. Research teams commonly need to validate, clean or transform data contained in CSV input datasets so that they can be used in experimental setups. For that reason, researchers frequently implement ad-hoc preprocessing routines not completely specified in published papers, which can make work reproduction and validation difficult. The purpose of this work is to merge different source files, using as a validation approach the data in CSV formats. The use of heterogeneous data sources in data-driven scientific projects frequently requires a preprocessing phase where input data are interpreted, validated, transformed, and saved into an output data format that is typically different from the input data format. The results of each project seriously depend on the quality of data used in the experiments, regardless of their purpose . The data preprocessing phase is crucial. Despite not having any data type information, CSV files are one of the simplest and most extensively used formats when it comes to data exchange .1)The extract process is responsible for the data extraction from the source system or database and makes it accessible for further processing or management process. In health care, this process needs to deal with data privacy, and most of the extract process has an anonymization process associated. At this point, the researcher decides which data make sense to use.2)The transform is a process based on a set of rules to transform the data from the source to the target. In the current research, a new approach using semantics is proposed for this phase. This can be a complex process taking into account different dimensionalities cases, and it needs to assure that all variables are in the same units so that they can later be joined and a clean process can be conducted. The transformation step also requires joining data from several sources, generating aggregations, surrogate keys, sorting, deriving newly calculated values, and applying advanced validation rules.3)The load process merges all data into a target database (output data).In order to address the lack of data preprocessing standardization in scientific projects, we propose to model the data preprocessing phase as an extract-transform and load (ETL) process. ETL derives from data warehousing and covers the process of how data are loaded from the source system to the data warehouse. A typical data preprocessing phase is thus composed of the following three phases: (i) Extract available data (extract), (ii) transform and clean data (transform), and (iii) store the output data in an output repository (load). These phases are preceded by the data source selection, typically a set of files or a database. (1)Split one row into many (1 : m row split). Input rows are split in a 1 : m transformation, in order to have just one row per patient examination (instead of one row containing multiple patient examinations). At this point, only the relevant input fields are kept. The specification of row splitting and field selection is made through an auxiliary file called split rules. In this file, each selected field of the input CSV file is associated to a type and eventually to a structure .(2)Semantic validation. At this step, the resulting data files of 1:m row split are submitted to semantic validation, where the rules defined in the auxiliary file semantic rules are applied. These rules are based on domain knowledge and aim at validating specific domain constraints, like thresholds or non-empty fields in specific columns;(3)Data join. At this point, data is joint by applying the rules defined in the last auxiliary file, named join rules. This file describes which fields should be present in the output files and which will be used as keys when joining the rows from each file. In addition to the output data files, a file named transparency report is generated. It includes statistical information about operations applied during the transformation process.The processed (output) data can then be used by data analysis and knowledge discovery algorithms. We structure our work in the transformation phase by dividing it into three sequential steps (E3TL). For each of these steps, there is an associated auxiliary configuration file. The first two steps are specific for each input file having its own specification and intermediary output file. The third and final step is unique and generates the final CSV output files. In more detail, the three E3TL steps are as follows see :(1)Splithttps://github.com/ruifpmaia/e3tl]. The python file is imported as a module, and then each E3TL transformation can be invoked as a function.In order to validate our methodology, we applied it to a biomedical dataset, specifically to an amyotrophic lateral sclerosis (ALS) dataset produced by neurologists from Hospital de Santa Maria, in Lisbon, Portugal. This dataset contains information about ALS patients, including personal and medical examination information that was previously anonymized. In our experimental implementation, all transformations were coded in Python and the supporting files specified using Javascript notation (JSON) notation. Our experimental implementation of the E3TL methodology was also coded in Python. JSON was also used for auxiliary files specification. The implementation is available either as a Jupyter Python notebook and the equivalent Python source +$". An example of the validation rules of an intermediary file (ALSFRS.csv) obtained from the processing (by field mapping) of the ALS-FRS.csv input file. The available parameters for the validation rules auxiliary file are the following:OutFile—The final output filename;FeatureMapping—The list of fields that should be present in each row of the output file and their relation to the input fields (from the intermediary files). Typically, each row includes dynamic (exams) and static information ;InputIdx—The index of the input field on the auxiliary input file.InputFile—The auxiliary input filename;Name—The final output field name;Type—The output field data type;Format—The output field format for non-basic types ;JoinKey—Indicates if the field is part of the Join key.The last step of the transformation phase is a join transformation where fields from the intermediary output files generated by the semantic validation step are possibly merged. According to the rules defined in the associated auxiliary file join rules, this step executes an Inner Join operation using the specified key. The result will be the generation of an output file with the specified fields. The available parameters of the join rules auxiliary file are the following:(a)1 : m row split execution example. The function receives two variables as arguments: One with the name of the CSV input data file and the second with the name of the JSON specification auxiliary file. As output, the function returns the name of the intermediary CSV output file.Our methodology produced output files with the expected data and structure according to the specified E3TL process. The output files generated for the Santa Maria dataset include dynamic and static information. The dynamic information was retrieved from the original files containing data of medical examinations  and was later joined with static information from the demographics file. The output result was stored in five different files and represented in (b) Semantic validation execution example. The function receives two variables as arguments: one with an intermediary CSV data filename generated by the previous step, and the second with the name of the JSON specification auxiliary file. As output, the function returns the name of the intermediary CSV output file.(c) Data join execution example. The function receives as arguments the JSON specification associated auxiliary file. As output, the function returns the name of the final CSV output file.The process generated a transparency report with the statistical description of input and output datasets and the number of operations executed in each phase of E3TL. The number of transformations applied and missing values found, for example, are registered in this file. Some procedures with small changes on Python scripts were applied to urgency data from a hospital in Lisbon. These data include registers from 1 January 2013 to 31 December 2017. The original data were enriched with external data, like weather conditions and events. The ETL process was applied to generate a greater data file see  that allIn another use case, these data were integrated with the output of NLP over EMR . A schemF1 score of 0.67.The evaluation of the NPL system was performed based on standard metrics calculated for 75 of the 5255 EMRs. These standard metrics are precision, recall, and F1 score. Two healthcare practitioners from the hospital manually annotated the clinical terms present in 75 EMRs in order to establish a gold standard for this evaluation. The evaluation showed that the NPL system coupled together in this research has a precision of 0.75, recall of 0.61, and an The main drawback of the CSV format is the fact that it does not enclose data and information type for the stored fields. This problem exponentially escalates when dealing with large datasets, updated by multiple users or organizations and using different standards for information representation. Typically, research teams working on data-driven scientific projects focus on data analysis and knowledge discovery algorithms, trying to obtain, for example, the best prediction models and results. They implement ad hoc data preprocessing routines based on their domain knowledge for data validation and transformation operations. Preprocessing routines are commonly not detailed in published works that are mostly focused on reporting the implemented data analysis techniques and obtained results. Little attention is given to preprocessing standardization. Considering tabular formats like CSV, this lack of documentation or incomplete description can be a relevant issue hurdling work assessment and reproduction. Without ensuring reproducibility, it may not be possible to re-execute part of or an entire scientific work, thus restraining its validation or further development.We propose improvements in the area of tabular data integration and data preprocessing in the medical area. A new ETL methodology—E3TL—was proposed based on CSV file preprocessing in data-driven scientific projects aiming at standardized procedures for data integration, cleaning, and transformation.Our approach has the advantage of defining a comprehensible and systematic ETL process for CSV preprocessing. It also integrates three primary vectors concerning data processing and exchange (or reuse) transparency: (i) Metadata declarations as a basic structure for data preprocessing; (ii) systematic data validation definitions; (iii) integrated transformation, validation, and filtering metrics summarized in a transparency report. By including metadata information in E3TL, we aimed at facilitating interpretation and sharing of the original dataset knowledge, following W3C recommendations.The results from our research can improve the quality of the service provided to the patients at the hospital as data are integrated and knowledge is available to medical professionals to gain a broader and more complete picture of the patient they are treating. In parallel, by having the data available and transformed into knowledge, they can lead to faster and more accurate diagnosis, which allows’ medical personnel to become more productive. From a more managerial perspective, this type of integration and the knowledge it makes available can also be used, for instance, for the prediction of waiting times , allowinApplying the proposed approach to other use cases and datasets may lead to refinements and further developments of the E3TL model, including the incorporation of more detailed statistic processes. This approach can be generalized to different cases, as long as the baseline conditions are verified. Generalization to other cases will require adjustments to the specificities of those cases. Nonetheless, the overall framework is applicable.New achievements regarding the availability of knowledge from data can be performed with the merge of different data sources using as input structured and unstructured data. This is not only very positive for the hospitals and healthcare centers but, and most importantly, for the patients. Considering that the main objective of this field is offering healthcare to those in need or, in some scenarios, the best quality of life possible, by using structured and unstructured data separately, results can be obtained as shown. This mixed approach can be used not only in technical areas but also in operational ones. For example, through better disease prediction models, earlier diagnostics can be achieved, allowing a more adjusted medication to the patients and eventual avoidance of more acute health states. It can also be used to forecast seasonality and understand what sector of the hospital would be more requested in each season to improve the allocation of staff. Data integration and manipulation allow DM processes to be conducted using enormous data volumes to anticipate problems and/or solve them at their early stages, improve diagnosis and treatment because more data and cross-check functions are available and, in general, improve the quality of health services and patients’ quality of life.One paramount issue in DM is that data can be spread across many entities . The integration that results from this approach allows to create new data sets with more information, and new knowledge can be generated to improve healthcare. Further, this integration of different data from the hospital database with ERM and external data also allows to improve management procedures based on the outputs from the data mining process. In this approach, the privacy issue was overcome with data anonymization. Data were used with the permission of the hospital.


This study set out to describe the association between the maternal mortality ratio (MMR) estimates and a set of socioeconomic indicators and compute the MMR inequalities among the provinces of Ecuador.A cross-sectional ecological study was conducted, using data for 2014 from the country’s 24 provinces. The MMR estimate was calculated for each province, as well as the association and its strength between MMR and specific socioeconomic indicators. For the indicators that were found to be significantly associated with MMR, inequality measurements were computed.Despite a relatively low MMR for Ecuador overall, ratios differed substantially among the provinces. Five socioeconomic indicators proved to be statistically significantly associated with MMR: total fertility rate, the percentage of indigenous population, the percentage of households with children who do not attend school, gross domestic product, and the percentage of houses with electrical service. Of these five, only three had MMR inequalities that were significant: total fertility rate, gross domestic product, and the percentage of households with electricity.This study supports research arguing that national averages can be misleading, as they often hide differences among subgroups at the local level. The findings also suggest that MMR is significantly associated with some socioeconomic indicators, including ones linked with significant health outcome inequalities. In order to reduce health inequities, it is crucial that countries look beyond national averages and identify the subgroups being left behind, explore the particular social determinants that generate these health inequalities, and examine the specific barriers and other factors affecting the subgroups most vulnerable to maternal health inequalities. The year 2015 marked the end of the Millennium Development Goal (MDG) era. For Ecuador, that period had been one of economic growth .Today, Ecuador and other countries face a new global health agenda that prioritizes universal health and equity, including through the Sustainable Development Goals (SDGs) and the new Global Strategy for Women’s, Children’s and Adolescents’ Health . In ordeTo help Ecuador prepare for these tasks, this study had two objectives:  to descrFor this research, a cross-sectional ecological study was conducted in 2014, using data from the 24 provinces of Ecuador.The variables considered in this study were one heath indicator, nine socioeconomic indicators, and one demographic indicator.The one heath indicator was the maternal mortality ratio (MMR).The nine socioeconomic indicators were:  total feThe one demographic indicator was live births.The indicators used in this study were derived from three key information sources –7 on socSome of the associations between the socioeconomic indicators and the MMR are intuitive, but others may not be. For instance, the average number of people per bedroom is an indicator of the quality of life and of living space. It especially reflects overcrowding and thus social deprivation. Therefore, this indicator was considered a proxy for poverty. On the other hand, the relationship between fertility and social factors helps to put the fertility rate into a larger, overall context, including identifying the most vulnerable sectors.The analyses included the MMR estimate for each province, which was calculated by dividing the number of maternal deaths by the total number of live births reported in the year 2014, expressed as the number of maternal deaths per 100 000 live births. A 95% confidence interval (CI) for the MMR in each province was also computed.First, an exploratory data analysis was carried out in order to ascertain the MMR and the selected socioeconomic indicators in Ecuador. Then, a study of MMR inequality was conducted for each of the selected socioeconomic indicators by taking two steps:  study thA weighted least squares regression model was used to study the association between MMR and the socioeconomic indicators. The weights were the number of live births in each province. The strength of the association was assessed with the Pearson correlation coefficient. The bootstrap method was used by considering 2 000 resamples in order to estimate the Pearson correlation and its bias-corrected 95% CI.Simple and complex measures of inequality were computed to explore the magnitude of MMR inequality for each socioeconomic indicator. The simple measures included computing the absolute difference (AD) and the relative ratio (RR) . The comIn order to compute the AD and the RR, quintiles of provinces for each of the socioeconomic indicators were created. For the purpose of this study, the first quintile (Group 1) represents the most disadvantaged provinces in terms of the quintile (Group 5) represents the most advantaged provinces for the indicator. The AD and the RR indicate the gap between the most disadvantaged and the most advantaged groups. The AD and the RR were computed by, respectively, subtracting and dividing the MMR in each of these two groups. Higher values of AD and of RR indicate greater inequality in maternal mortality.The MSII and PRSII were computed through regression model fitting. In order to fit the models, the complete data set of the provinces was first ordered by socioeconomic indicator status, from the most disadvantaged to the most advantaged.Ridit  as the independent variable. The weighted least squares method was used in this case.The MSII is based on the estimated slope parameter by fitting a linear regression model that considers the MMR as the dependent variable and the MinRidit - MaxRidit), where b is the estimated slope parameter computed by fitting the linear regression model. The MinRidit and the MaxRidit are, respectively, the observed minimum and maximum Ridit values. The theoretical minimum and maximum Ridit values are 0 and 1, so that MSII =-b.The MSII was computed as MSII = b (U (MinRidit - MaxRidit), bL (MinRidit - MaxRidit)], where bL and bU are, respectively, the lower and upper limits computed from the 95% CI for the slope parameter. In order to compute the PRSII, the Poisson regression model was fitted by considering the number of maternal deaths as the response variable and theRidit as the independent variable.The 95% CI for the MSII was computed as , where bL and bU are, respectively, the lower and upper limits of the 95% CI for the slope parameter computed by fitting the Poisson regression model.The formula for the 95% CI for the PRSII was ) .Of the five socioeconomic indicators that were statistically associated with MMR, only three proved to have statistically significant MMR inequality measures when using the MSII and PRSII: total fertility rate, GDP, and the percentage of households with electricity .The AD between the group with the highest total fertility rate (Group 1) and that with the lowest total fertility rate (Group 5) was 70 . This meWith respect to the total fertility rate for each province, the MSII value of 26.1  indicateRegarding the MMR inequality that GDP produces, the AD value of 60  indicateConsidering the values for each province in Ecuador, the MSII value of 28.8  indicateIn terms of housing conditions, the AD value of 42  indicateConsidering all the values of each province, the MSII value of 22.6  indicateIn recent years, Ecuador has made significant achievements in terms of overall economic growth, poverty reduction, and social service expansion. However, the country is still struggling to ensure that economic progress is made at all levels of society, and that improvements in effective social services coverage and health outcomes are shared by everyone, including the most vulnerable. Similarly, Ecuador also displays significant differences in terms of health outcomes, such as maternal mortality. For example, the province in Ecuador with the lowest MMR  has an MMR that is similar to that of Trinidad and Tobago, which has one of the highest human development index (HDI) values in the Americas . In contThe results presented in our study support previous research that argues that national averages often hide differences at the local level, and that these disparities can be strongly associated with different socioeconomic indicators . For exaIn reviewing previous research, we have found that there are some studies on the association between socioeconomic indicators and maternal mortality in Latin American and Caribbean countries. However, few of these studies analyze the maternal mortality inequalities related to those socioeconomic factors. Our Ecuador study is important because it can provide decisionmakers with the type of information that is needed to determine which subgroups to target in order to reduce current health inequalities. The information can also provide insight into the specific mechanisms  that generate these health inequalities. Furthermore, the evidence from this study can lead to additional research that examines the specific barriers and other factors affecting the subgroups most vulnerable to maternal health inequalities.One of the strengths of this paper is the use of the Poisson regression model for computing a novel relative measure. On the other hand, there are some weaknesses in this study, such as the use of the cross-sectional ecological design that produces an ecological fallacy. This means that apparent associations between different provinces in Ecuador may not accurately reflect the true association between individuals within those provinces. However, inequality measurements using data for provinces  provide relevant and more accurate information for the development of local health policies because they make it possible to identify the provinces that require equity-based interventions.Ministerio de Salud Pública (MSP)). In dealing with a suspected case of maternal death, a multidisciplinary team carries out a thorough investigation to either confirm or rule out that initial analysis. Additionally, the vital records system (INEC in Spanish) and the MSP perform a semiannual process of active search for maternal deaths, through the review of death registers and local research processes.Limitations on the actual distribution of the maternal mortality data in the provinces by socioeconomics indicators may have also been a weakness in our analysis. As for the maternal mortality data, they come from the Epidemiological Surveillance System of the Ministry of Public Health  are not taken into account. Thus, the results of the analysis in this work have to be considered as a first description of the maternal mortality inequalities, and further studies are needed. In a future study we will consider the use of multilevel analysis, by utilizing data from the cantons and provinces.This study is one of the few studies analyzing maternal mortality inequalities in Ecuador. By carrying out an analysis combining descriptive measures, association measures, and inequality measures, we hope to provide decisionmakers with the type of information needed for priority-setting. Further, the mixed analysis used in this article is innovative in the sense that it expands on the methodology traditionally used in studies that measure health inequalities, and it offers additional information that may enrich the understanding of maternal mortality inequalities within a country.We acknowledge the entire group working in the Gerencia de Mortalidad Materna at the Ministry of Public Health in Ecuador, and Dr. Gina Tambini and Dr. Adrian Diaz from the Pan American Health Organization (PAHO) Country Office in Ecuador. We would also like to thank Ramon Martinez, of the Department of Noncommunicable Diseases and Mental Health at PAHO, for his support of this work.RPSP/PAJPH or the Pan American Health Organization (PAHO).The authors hold sole responsibility for the views expressed, which may not necessarily reflect the opinion or policy of the


The accumulation of knowledge required to produce economic value is a process that often relates to nations economic growth. Some decades ago many authors, in the absence of other available indicators, used to rely on certain measures of human capital such as years of schooling, enrollment rates, or literacy. In this paper, we show that the predictive power of years of education as a proxy for human capital started to dwindle in 1990 when the schooling of nations began to be homogenized. We developed a structural equation model that estimates a metric of human capital that is less sensitive than average years of education and remains as a significant predictor of economic growth when tested with both cross-section data and panel data. AYE) as a proxy indicator  ≠ 0, or weak, E[Ziihci] ≠ 0 but with low correlation, or both  = 0 means ϵi1. The force of the instruments will then be proportional to the degree of heteroskedasticity of ϵi2 with regard to Zi. Thus, identification requires that the error terms of the regression of the first stage are heteroskedastic. As Lewbel E[Ziϵ1i]  or both  introduc or both  showed ts Lewbel  notes, ts Lewbel  mentionss Lewbel . Followiyi,t is the log difference of GDP per worker; yi,t−1 is the GDP per worker in the first year of the period; μi are unobserved country-specific effects and δt includes temporary effects affecting different countries. To address these temporary effects a set of time dummies for each regressions is included. To further evaluate the robustness of our index, we proceed with a panel data analysis. Following the reasoning of cross-section data, μi  which could be correlated with other regressors leading to biased parameter estimations. Models of panel data avoid this problem by treating those individual characteristics as time-invariant and eliminating them through transformations. Another problem is the presence of endogeneity in some variables in yi,t−1, is correlated with fixed effects on the error term.The estimation of A standard approach in the literature to deal with these problems is to use the difference GMM estimator developed by Arellano and Bond . This esihc, we compared our results for ihc with average years of education, as developed by Barro and Lee   for those models that include ihc and  wherein AYE is present; in both cases the coefficients are negative and highly significant.The effect of human capital on economic growth is first observed without incorporating control variables, except time dummies, columns (1) to (3). The lagged GDP is treated as a predetermined variable, while ihc and AYE have coefficients with positive signs and both are highly significant in pooled OLS models, but ihc shows a greater explanatory power. On the other hand, those variables in the estimates by fixed-effects do not appear to contribute much to the model; both are not significant and AYE shows a negative sign, which may be caused by potential bias from endogeneity problems and the lagged GDP value.Both ihc and AYE decreased, in the light of these new specifications, both indicators have similar performance to previous models. Column (3) shows the results for the system GMM estimator (two-step), employing only time dummies and the second lag to avoid the proliferation of instruments. We used Windmeijer’s correction for standard errors [ihc and AYE, the lagged GDP is within the limits set by columns (1) and (2), and has the expected sign with a high level of significance. Both proxies of human capital show positive coefficients which are highly significant. An increase of one standard deviation (1.7) in the proposed indicator increases GDP growth in 0.504, meanwhile, the educational variable does so at 0.642. On the other hand, when control variables are included in the regression models, ihc loses statistical significance, in opposition to AYE which remains highly significant for predicting economic growth.In columns (4) and (5) these same models are estimated by introducing the set of controls presented in the section of data. As noted, although the coefficients of d errors . For botThe Hansen J statistic shows p-values for the null hypothesis of the validity of the over-identifying restrictions. None of the specifications reject the null hypothesis and thus indicate the validity of the instruments. AR(1) and AR(2) are the p-values for auto-correlated errors of the first and second order respectively, in the first differences equation. While AR(1) is expected to be significant, AR (2) is a specification test under the null hypothesis of no serial correlation. Again, none of the specifications can reject this hypothesis. These tests indicate an appropriate specification of the models.AYE and the response of ihc to these specifications. First, AYE and ihc seem to perform well when not all the controls are used. When these are included both variables are no longer significant, they significantly reduce their impact, and fail in the identification of the models. The significant reduction of the instruments used in the identification may be influencing the results, because as shown by the pooled OLS estimation, both variables are highly significant, confirming the previous results.As before and once both variables are validated by dynamic panels, we evaluated the two concerns raised in relation to the performance of AYE plays a crucial role in the formation of human capital, impacting on economic growth significantly. It is in this period that the highest growth rates in terms of quantity-based education occur worldwide, particularly in the seventies, with increasing levels of human capital and productivity, as reflected in the proposed indicator.Hence, although restricting the sample to this sub-period does not allow to correct endogeneity problems satisfactorily, the estimates by pooled OLS allow to elucidate that AYE loses all relevance as a determinant of economic growth, even if no controls are included . With controls ihc loses significance when it is estimated by the system GMM estimator, which again can be explained by the small number of instruments, leading to identification failure. However, using other methods, ihc proved to be significant. The performance of ihc can also be explained by the inner dynamics of its indicators, including AYE. The marginal increases in educational variables based on quantity do not seem to be enough to influence the performance of human capital and, thus, productivity and economic growth. In this most recent period, factors related to the quality of human capital accumulation seem to be more influential on the performance of these variables. Given this scenario, ihc performs better, and this is because within its structure there are variables that somehow better approximate the quality of human capital. This indicator, however, shows limitations, mainly as a consequence of the availability of data to better measure other elements of the quality of this stock and, secondly, because the variables used in its development lead the indicator to stationary points.In this case, ihc and AYE when tackling differences in countries levels of development. ihc always shows the correct sign, with a statistically significant impact, AYE is not significant to the inclusion of all control variables. Furthermore, the test specification of ihc performs well in all regressions, while for AYE, it does not pass the Hansen J statistic when no controls are included.Another concern proposed above was related to the performance of AYE not only is reducing its growth rates, but it is also homogenizing around a level which possibly is difficult to escape by the cost-benefit ratio that implies increases in this investment. These trends together with the limitations of this indicator are likely to reduce the ability to influence on effective increments in human capital and, hence, on economic growth. The indicator ihc shows a superior performance in this context because it captures better the disparities in terms of human capital between countries. The differences in the scores of the proposed indicator better characterize the differences regarding productivity and the quality of education between countries.As explained above, the facts show that The aim of this paper was to propose a new index of human capital whose direct and indirect effects could be estimated so as to disentangle the relationships that exist between the process of accumulating knowledge required to produce economic value  and their returns and outcomes .By being based on a structural equation modeling approach, our index allows an ample definition from the viewpoint of latent variables in social sciences . Such anihc for a reasonably extended period of time ranging from 1970 to 2011. Besides, we were able to extend the results shown by Messinis and Ahmed [Given the availability of data, we were able to report nd Ahmed , by compnd Ahmed .AYE in the block of educational variables, future research could focus on the International Cognitive Assessment (ICA) scores [We have shown that our index performed better than existing measures as it tackles omitted variable bias, and prevents the limitations of information reduction techniques such as principal component analysis. The proposed measure showed good performance concerning different specifications and econometric techniques to explain economic growth. The proposed indicator overcame some weaknesses with a warning. Rather than including ) scores . A preca) scores , for exaOther improvements are indeed welcome in future efforts. For example, rather than including the number of patents per capita as a proxy of innovation, future research could include scientific productivity in physical and chemical science since they predict the future economic growth of countries better than other popular indices . Corrupt


Mycobacterium bovis is an archetypical zoonotic pathogen in that it can be transmitted from animals to humans and vice versa. Although elimination of zoonotic transmission of TB to humans is frequently promoted as the raison d'être for TB management in livestock, in many countries the control strategies are more likely based on minimizing the impact of sustained infection on the agricultural industry. Where wild animals are implicated in the epidemiology of the disease, the options for control and eradication can require involvement of additional stakeholder groups. Conflict can arise when different monetary and/or societal values are assigned to the affected animals. This may impose practical and ethical dilemmas for decision makers where one or more species of wild animal is seen by some stakeholders to have a greater value than the affected livestock. Here we assess the role of stakeholder values in influencing TB eradication strategies in a number of countries including Ireland, the UK, the USA, Spain, France, Australia, New Zealand and South Africa. What it reveals is that the level of stakeholder involvement increases with the complexity of the epidemiology, and that similar groups of stakeholders may agree to a set of control and eradication measures in one region only to disagree with applying the same measures in another. The level of consensus depends on the considerations of the reservoir status of the infected host, the societal values assigned to each species, the type of interventions proposed, ethical issues raised by culling of sentient wild animals, and the economic cost benefit effectiveness of dealing with the problem in one or more species over a long time frame. While there is a societal benefit from controlling TB, the means to achieve this requires identification and long-term engagement with all key stakeholders in order to reach agreement on ethical frameworks that prioritize and justify control options, particularly where culling of wild animals is concerned.When human health is put at risk from the transmission of animal diseases, the options for intervention often require input from stakeholders whose differing values systems contribute to decisions on disease management. Animal tuberculosis (TB), caused principally by  Mycobacterium bovis, being capable of infecting a wide range of mammalian species, the perceived risk is reflective of the historical close association between livestock and humans  were not, at least in the beginning, underpinned by strong scientific evidence , 15. Rat“Wildlife stakeholder” broadly describes any person or group with an interest in wildlife. The levels of interest and the weighted values that each stakeholder assigns to particular wildlife can be highly variable, and defining the moral and ethical viewpoints of stakeholders that influences their level of engagement can be difficult. This is because there is likely to be a complex interplay between the values that each stakeholder places on wildlife and how it is linked to their moral perspectives on animal rights, animal-human health, conservation and biodiversity . Value sad hoc blend of economic considerations, livelihood activities, knowledge, ethical perspectives, social acceptance, ecological concerns, cultural significance, and political will. This results in significant challenges for the selection of control policies where one or more species of wild animal is seen by some stakeholders to have a greater value than the affected livestock. It can also lead to demands for exceptionally high quality scientific evidence to justify particular interventions. Not all species are of equal significance in the epidemiology of disease, not all are considered equal when subjected to disease management, nor are they always equal in the eyes of stakeholders.Where wildlife start to encroach and compete with human interests negative value perceptions can increase among an expanded range of stakeholders. The divergence of values can lead to conflicts between those who place higher values on human activities  and livelihoods and those who value the protection and welfare of wild animals. How these differences are reconciled can depend on the environmental and animal ethics perspectives of the stakeholders . These pTo try and get a better understanding of how policy decisions to manage TB in wildlife are influenced by stakeholders, we have looked at a number of established TB control programmes worldwide where there is strong evidence of epidemiological involvement of wildlife in the transmission of infection. We highlight the influence of stakeholder values on the management of the disease where the contexts differed. The approaches to disease control range from relatively uncomplicated management systems in Australia where there was strong consensus between stakeholders because of the negative value pest status of the wild animals to the highly complex epidemiology of disease in South Africa where multiple species of high positive conservation value are affected and a diverse range of stakeholder groups are involved in the debate on how to control and manage the disease.M. bovis infection in animals was limited to two maintenance hosts: domestic cattle and feral water buffalo , with infection recorded in only one other wild animal host, feral pigs (Sus scrofa) (Dama dama) . Eradication was achieved following agreement of key stakeholders to the program, which included addressing the problem of wildlife reservoirs of infection . The las scrofa) , 30. The scrofa)  and in fma dama) . Also, aThe Australian history of bovine TB control mirrors that of other developed countries, with an evolution from a voluntary program in the early twentieth century to a national program commencing in 1970 . The iniFeral water buffalo were only found in the NT having been introduced there in the mid-1800s. In the 1960s the prevalence of TB in slaughtered bulls was 16%. In 1970 at the commencement of the BTEC program, the disease was endemic in buffalo across most of their range  with theThere was strong social, political, and cattle industry support for eradication of feral buffalo with the principal justification being the risk of transmission to cattle, even though there was only limited interaction between buffalo and cattle and no evidence of significant cross-species transmission . There wM. bovis infection. These suids were widespread and numerous in the region, and though the prevalence of confirmed M. bovis infection in some studies was high at 19.2% (M. bovis was never reported in the common brushtail possum (Trichosurus vulpecula). Elsewhere, the absence of infection among native wildlife allowed the focus of the TB campaign to remain on cattle and buffalo. Following the end of BTEC, all subsequent buffalo herds were derived from populations where infection had never been present. Since 2011, infection with M. bovis has been classified as an exotic disease of cattle in Australia  were considered as possibly a maintenance host in very limited areas  with deep polarization of opinion among many of the stakeholders, particularly in the UK  policy of gassing setts with hydrogen cyanide, leading to a number of commissioned reports over the following decades, with no clear resolution as to how the scientific evidence should inform policy. The Zuckermann review in 1980 recommended sampling of badgers in the vicinity of affected farms and culling at setts if badgers tested positive . In 1986badger culling can make no meaningful contribution to cattle TB control in Britain” , which oversaw the trial, concluded that “Britain” . This viontrols” . Cassidyontrols”  points oontrols” , 77. Theontrols” . As cattontrols” . In Nortontrols” . A surveontrols” .The multi-dimensional aspects and complexity of the evolution of cattle TB policy in the UK raises many questions on the ethics and value systems of stakeholders in the context of culling of badgers. The role of the media is of key importance in framing the viewpoints of many of the principal actors . Where tthe slaughter of a cow or the culling of a badger with a life of net positive value will result in a loss of utility. All else being equal, the killing of a cow or badger that could be expected to continue with a life of net positive value is, therefore, prima facie morally wrong, simply because it reduces total utility” They suggest that killing of badgers can be morally justifiable if it results in greater overall utility, e.g., the replacement of the (badger) utility by cattle, or an increase in human utility through improved farming economic benefits accruing from culling of badgers. They then pose the question as to how much culling of badgers is acceptable to justify the increased utility value of cattle? The contention from the ethics of utilitarianism is that the correct policy is one which produces overall highest utility. But this relies on an understanding of the consequences of the policy such that the impact of different policies can be objectively compared and measured. Their analysis concluded that non-culling approaches including badger vaccination policy options resulted in higher overall utility, and was superior to the badger culling option. In the absence of agreement among stakeholders, vaccination of badgers offers a utilitarian solution, and is now considered as a strategy that can address many of the negative issues associated with culling  in southern Spain have revealed prevalences of 52% in wild boar, 27% in red deer and 18.0 % in fallow deer (Lynx pardinus), which along with foxes (Vulpes vulpes) are considered as spill over hosts  are the principal wildlife maintenance hosts implicated in transmission of TB to livestock in Michigan and Minnesota  (Canis latrans), racoons (Procyon lotor), red fox (Vulpes vulpes), bobcat (Felis rufus) and black bear (Ursus americanus) have been found in the northern portion of the Michigan's Lower Peninsula which contains the core area and probably amounts to them being spillover hosts (The disease was first reported in wild deer in Michigan in 1975 and was considered an isolated event . In 1994500 km2) . With th500 km2) . While t500 km2) . As a ma500 km2) , 122. Ag500 km2) , 124 ander hosts . The fuler hosts .2. By 2011 only 12 beef cattle herds and 27 white-tailed deer had been diagnosed with TB. The result of studies to investigate the factors associated with deer-cattle transmission had implicated deer visits and damage to stored cattle feed as a major risk factor , African Af2 found in East Africa  and European Eu1 complex in South Africa  in 1928 (Sylvicapra grimmia), and springbok (Antidorcas marsupilias). More recently research has focused on the Hluhluwe-iMfolozi Park (960 km2) and Kruger National Park  where it is believed that TB was transmitted to the African buffalo (Syncerus caffer) from domestic cattle in the 1950s (Panthera leo), cheetah (Acinonyx jubatus), kudu, leopard (Panthera pardus), chacma baboon (Papio ursinus) (Diceros bicornis) (Ceratotherium simum)  –137, blaicornis) , 139 andm simum) . There wm simum) . Moleculm simum) .In South Africa a voluntary test and slaughter scheme for cattle was initiated in 1969, and by 1991 had reduced the disease prevalence to 0.04%. However, primarily due to financial and resource reasons this level of success was not sustained, and the disease levels increased thereafter .Crocuta crocuta), leopard and cheetah, and a range of scavengers and omnivores  vaccine candidate has shown some promise in a range of species  as a disease management tool, vaccination of wild animals against TB is often promoted as an alternative strategy, primarily because it provides for a non-destructive approach to controlling disease and addresses animal welfare concerns, as well as conservation concerns arising from deliberate killing of wild animals , 148. Th species –154. The species .In deciding on the appropriate control strategies to employ, the desired outcome needs to be carefully considered in order to avoid further conflict among stakeholders. For example, vaccination of badgers may address conservation concerns arising from culling a protected species, with the added benefit of protecting cattle from badger—cattle transmission. However, the time frames to achieve eradication will be much longer when compared with culling . StudiesThe eradication of TB from animals has faced many challenges since studies commenced in cattle, when in the early 1890s Koch's old tuberculin was found to be useful as a diagnostic tool for TB . Along wThe examples of TB management in the different countries portray a range of single and multi-host wild animal systems implicated in the transmission cycle of TB that involves livestock. In most cases epidemiological investigations have helped reveal the reservoir status (maintenance or spillover) of many of the species involved, and this has informed the type of control measure applied , 161. HoIn contrast, the culling of badgers in the UK is not short of controversy and reflects the polarized perspectives and viewpoints of the principal stakeholders. These would include the dairy and beef cattle industries and associated beneficiaries on one hand, and conservationists, animal rights groups and environmentalists on the other . The broThe current arguments for and against culling of badgers in the UK broadly align with the opposing framings of the badger and the societal values assigned to the badger by either side of the debate , 168. OnM. bovis, and the risk of infection from infected meat is negligible (M. bovis in unpasteurized milk poses a zoonotic risk to consumers (Some stakeholders have questioned the cost-benefit of continued costly surveillance of TB given that milk pasteurization is highly effective at killing gligible . While tonsumers , 172. IfWe have shown here that the level of engagement and ethical perspectives of stakeholders can change when wildlife disease management becomes part of an eradication programme . One of EG and LC contributed equally to this review including critical analysis of published data and preparation of the manuscript. The opinions expressed in this paper are solely those of the authors.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.


Recently, citizen involvement has been increasingly used in urban disaster prevention and management, taking advantage of new ubiquitous and collaborative technologies. This scenario has created a unique opportunity to leverage the work of crowds of volunteers. As a result, crowdsourcing approaches for disaster prevention and management have been proposed and evaluated. However, the articulation of citizens, tasks, and outcomes as a continuous flow of knowledge generation reveals a complex ecosystem that requires coordination efforts to manage interdependencies in crowd work. To tackle this challenging problem, this paper extends to the context of urban emergency management the results of a previous study that investigates how crowd work is managed in crowdsourcing platforms applied to urban planning. The goal is to understand how crowdsourcing techniques and quality control dimensions used in urban planning could be used to support urban emergency management, especially in the context of mining-related dam outages. Through a systematic literature review, our study makes a comparison between crowdsourcing tools designed for urban planning and urban emergency management and proposes a five-dimension typology of quality in crowdsourcing, which can be leveraged for optimizing urban planning and emergency management processes. Among workers . As poin workers , differe workers . Accordi workers .mobile participatory sensing have been proposed and evaluated. Looking at the insights reported in a standard document released by the National Water Agency [In recent years, citizen involvement has been increasingly used in disaster prevention and management, taking advantage of new pervasive and ubiquitous technologies. This new scenario has created a unique opportunity to leverage the work of crowds of volunteer citizens. As a result, a set of computational tools based on crowdsourcing and r Agency , 3.5 milr Agency , dams widisaster risk reduction [In order to reduce the risk of disasters through systematic efforts of prevention and early detection, the concept of eduction  has beeneduction ,9,10. Deeduction . The maieduction . RespectWe claim that crowdsourcing approaches may bring clearly identifiable benefits when applied to this domain, as disaster management tasks are difficult to automate due to their heterogeneity and inconsistency . MoreoveThe remainder of this paper is structured as follows. Successively reaching high-quality outputs from a massive number of crowd participants is still a challenge for the widespread adoption of crowdsourcing systems . Here, tIn the literature, several models have been proposed for dealing with quality control and management issues in crowdsourcing. For instance, Zheng and co-workers  proposedstigmergic approaches for geographical data collection  has been also addressed as a successful way of engaging crowds of citizens to improve the data quality [The use of wikis as  quality . Accordi quality , some of quality ,30. Some quality , and lac quality . Another quality .(i)All dwellings and sirens that cover the floodplain—self-saving zone (ZAS)—informing the number of dwellings and people affected;(ii)The meeting points in ZAS, informing the escape route, with the number of people expected at each point;(iii)A complete map of the dam break covering ZAS and self-rescue zone (ZSS), informing the total of affected people (ZAS and ZSS), as well as identifying within the floodplain the existence of sensitive buildings ;(iv)Federal, state, and urban highways with large circulation of vehicles that will need to be banned;(v)Identify the routes that should be used as detours, block points and alternative routes.Due to recent tragedies and the resulting changes in Brazilian legislation, it is urgent that companies responsible for mining dams update their emergency action plans to be sent to city authorities and civil defense agencies. Among other information, a dataset should be maintained, including:Although many research studies have examined the use of crowdsourcing in urban planning, few studies of current factors in developing flood risk mitigation strategies could be found in the literature. One notable exception is the work of Cheung and Feldman  who examAfter the Mariana dam disaster in 2015 , changesThe aim of the simulation is to enable the entire population in the risk area to become aware of escape routes and meeting points . Once thIn situations of major urban disasters, the need for rapid rebuilding of houses, infrastructure, and public facilities is a major challenge in a stressful and resource-constrained environment. Therefore, the use of emergency shelters is an operation of transferring the unsheltered to prepared temporary structures that present area availability for such purpose. They must have an infrastructure capable of meeting the basic needs of water and sanitation, among others, ensuring the survival of those affected. Assembly of the shelters should be done within a short time (a couple of days or weeks). As stated by Costa and colleagues , these sAs it can be observed from However, the challenges regarding shelter deployment begin at the preparation stage. Urban, infrastructural, and architectural projects are necessary to ensure better accommodation of the homeless and to bring dignity throughout the different stages of emergency management. The choice of location, the techniques used for building shelters, as well as the community equipment used can be predicted in the projects, despite the uncertainties. These design solutions must be specific to each type of disaster and the case of dam disruption would be no different. In addition, the particularities of each affected city or neighborhood must be observed, which means that despite generic project solutions for the impacts of the dam disruption, specific projects should be developed for the different parts of the territory, considering topography, possible areas of impact, infrastructure, population characteristics, and so on.A systematic literature review can be understood as a “form of secondary study that uses a well-defined methodology to identify, analyze and interpret all available evidence related to a specific question in a way that is unbiased and (to a degree) repeatable” .This section describes how the literature review was conducted in order to characterize work management on crowdsourcing platforms when applied to urban planning. The review was performed according to the PRISMA Statement , which iThe planning of this review allows us to identify goals and build a protocol that presents the research methodology used. This leads to the following research question (RQ):(RQ1) How does crowd work function in crowdsourcing platforms intended to support urban planning?More specifically, how does the management of crowdsourcing processes take place, and how does the management and control of the tasks that arise operate? To help answer this, the RQ was broken into a set of research questions that were directly associated with the quality dimensions of work on crowdsourcing platforms .(RQ1.1) Task definition: What type of tasks are requested from participants?(RQ1.2) User interface: How do participants choose their tasks on the interface?(RQ1.3) Granularity: Do the tasks performed show traces of complexity, such as interdependence and coordination?(RQ1.4) Policy compensation: What kind of strategies and motivations for engagement can be identified?(RQ1.5) Work profile: To what extent were issues of work quality, performance, and worker expertise/reputation assessed?The review introduces three auxiliary attributes that are intended to categorize the platform, including: (i) The name of the platform; (ii) the urban planning context; and (iii) the crowdsourcing technique applied. As a result of a revision of a prior version of this paper , we made(“crowdsourcing experiment” OR “crowdsourcing application” OR “crowdsourcing platforms” OR “crowdsourcing case” OR “crowdsourcing tool” OR “plataforma de crowdsourcing”) AND (“urban planning” OR “urban design” OR “planejamento urbano”)The search string was developed through the combination of terms that capture crowdsourcing applications oriented to urban planning or urban design. Some tests were carried out on Google Scholar to evaluate the quality and quantity of the returned results. Search terms were included in both English and Portuguese. Afterwards, we used the boolean operator OR to link the main terms. Moreover, all search terms were combined using the boolean operator AND. Finally, the following search string was defined:The searches were initially based on the Google Scholar and then performed at the ACM Digital Library, SpringerLink, Scopus, and ScienceDirect without time restriction. We also elaborated criteria for inclusion and exclusion of articles in order to select those studies that were relevant for answering the RQs.The first step consisted of using the search string on the selected data sources, starting with Google Scholar as the primary search engine. At this stage, the only exclusion criterion was to exclude duplicate publications, resulting in a total of 412 articles. It is worth noting that some studies were added through backward snowball sampling, a technique used to complement the traditional systematic review process by identifying references that have cited the publications found in the search process . The numSubsequently, in the second step, the analysis of titles and abstracts of the 412 articles was carried out. Inclusion criteria were as follows: (i) Be a complete, non-duplicate article; and (ii) the article should deal with an application, experiment, presentation, or framework of some crowdsourcing platform to support urban planning. Studies presenting non-peer reviewed contents  and editorials, summaries of conferences, extended abstracts, panel discussions, or introductions to special issues were removed. Furthermore, book chapters, tutorials, keynotes, and technical reports were also excluded and approximately 85 papers were selected.In the third step, a brief read of 85 articles was carried out, with the inclusion criteria that the paper had the potential to answer the RQs. It is also noteworthy that the quality assessment was entirely conducted by the first author. The exclusion criteria remained the same as in step 2, since the mere reading of the title and abstract was sometimes not enough to see if there was a crowdsourcing platform applied to urban planning . At the end of this step, 29 articles were selected.The 29 articles were read completely in step 4. The inclusion criteria were applied based on a minimum quality score required. For the following questions presented in After selecting the unique primary studies, the information about each venue in which the article was published was also recorded. Such metadata were stored for documentation and analysis purposes. The distribution of studies per type of venue is listed in A data extraction form was created to store all relevant data from primary studies. Only one author gathered metadata from the final set of articles taking into account each data extraction field. This process supported the entire classification and analysis procedures by matching each item to the corresponding value, as followed in previous work . The teThe first author extracted and analyzed the data, although this process involved all of the authors of this paper. In particular, the information for each item extracted was tabulated see  and the In this section, we analyze the results obtained from the synthesis of primary studies. In this sense, we provide an overview of the several aspects that current studies address in order to answer the RQs. The study selection resulted in 21 primary studies, published between 2011 and 2018. As it can be observed on the number of papers published per year, the occurrence of primary studies is low  before the year 2015. This indicates that the interest on crowdsourcing for urban planning has been increasing in recent years, particularly in response to the need for solving complex urban problems affecting millions of people in different cities around the world.Looking at the results provided by the selected articles, a total of 18 different crowdsourcing platforms were identified and analyzed using feature analysis , a qualiIt is important to highlight the fact that most platforms were classified as OC, with more emphasis on VGI, mobile crowdsourcing, and participatory sensing. Six platforms explore TC-based approaches, and four of them incorporate open collaboration mechanisms such as comment and vote on proposed ideas . Only on(RQ1.1) Task definition: What kind of tasks were requested from participants?All the different tasks observed along the 18 selected platforms can be classified into the following categories: (i) Submitting ideas or projects; (ii) criticizing ideas or projects; (iii) voting; (iv) manually entering information into maps; (v) analyzing or classifying images; (vi) evaluating or classifying texts; (vii) photographic record; and (viii) answering specific questions.As shown in (RQ1.2) User Interface (UI): How did the participants choose their tasks on interface?In the selected studies, three ways of making the tasks available in the UI were identified: (i) When the crowdsourcing process is exclusively composed of a single task, so in the UI, only one task is made available during the whole period in which the process is open; (ii) crowdsourcing is composed of more than one task, but only one task per step is available; and (iii) when multiple tasks are simultaneously available in the UI. Some aspects can be noticed from (RQ1.3) Granularity: Do the tasks performed show traces of complexity, such as interdependence and coordination?Coordination is about managing the dependencies between tasks  and it c(RQ1.4) Policy compensation: What kind of strategies and motivations for engagement can be identified?Three types of motivation have been identified, namely: (i) Civic or academic motives; (ii) awards; and (iii) fun and curiosity . In five(RQ1.5) Work profile: Does the study report on assessments of the quality of work performed and the expertise (or reputation) of employees?In Ideas competition. All platforms support tasks related to submitting an idea or project. Our results also suggest that 80% of such platforms allow users to vote while 60% enables criticizing on projects and ideas. In addition, only 20% have tasks related to photographic record and answering specific questions. In 60% of the platforms analyzed, tasks were available to be executed simultaneously. Basically, the idea is that users can submit their own ideas in parallel, criticize, and vote on other projects for determined time. Furthermore, 40% of analyzed platforms have task dependency with explicit coordination, and it occurs mainly because users can criticize different ideas incorporated and/or (re)submitted by authors. Regarding the type of motivation, 40% involve prizes to engage users and all platforms report academic or civic motivation. Furthermore, 40% of the platforms analyzed in this study require reputation or expertise. It is also worth noting that all platforms provide support for verifying the quality of work performed by means of evaluation strategies.In mobile crowdsourcing, there is no information about crowdsourcing platforms for urban planning able to support the analysis and classification of images and similar items. On the other hand, 40% of the platforms provide support for submitting and/or criticizing ideas or projects, entering information into maps, analyzing or classifying texts, and providing photographic records. Results indicate that there are a small number of platforms (20%) with voting mechanisms and features for answering questions with high levels of specificity. Our findings highlight a reduced number of platforms and tools supporting single tasks in mobile crowdsourcing when compared to VGI and participatory sensing. This is also a pattern in the case of the platforms that require domain expertise and user reputation.Regarding the study of VGI and participatory sensing, there is an absence of platforms for supporting tasks such as voting, criticizing, and submitting ideas or projects. Such types of crowdsourcing are more suitable for single tasks. In addition, it is important to note that these platforms differ in terms of task dependency and coordination.In this section, results based on previous review work  are exte• VGI techniques and mapping for emergency plansGeospatial data based on personal local knowledge are of particular importance in disaster situations . In ordeAll the VGI process could be based in alternated and sequential cycles of mapping and image analysis. In the mapping cycle, participants would be able to represent relevant geographical objects while in the image analysis cycle, they can verify and validate those mapped points, lines, and polygons. As it can be seen in According to the RQ3, VGI platforms mainly show task dependency. In the particular context of VGI for mapping emergency plans, dependency between tasks occurs through the mapping–validating cycles while coordination happens in an implicit way.Strategies to engage participants can be based on civic or academic motives if the idea of building a collaborative emergency map is accepted. Furthermore, the whole mapping process can explore fun and curiosity, if a gamification process is adopted. Both in mapping and validation cycles, some geographical objects may require a specific expertise or enough reputation on the part of participants.In their extensive review article, Haworth and Bruce  argued t• Mobile GWAP participatory sensing to support escape route simulations Through mobile crowdsourcing techniques, opportunities for citizen participation are created and a pool of participants around the city can engage in tasks and generate data in a distributed manner. Ubiquitous devices such as tablets and smartphones allow people to take pictures, to answer questions, to vote, to draw everywhere, and facilitate to register facts and ideas . A GWAP In support to escape route simulations, a mobile GWAP could be applied. On an app with a navigation mode, a map with the escape routes, the user location, and the encounter point as a goal are plotted. The system will represent existing road signs. The game works with a timer, the participants must follow the escape route, catch points, and find the encounter point as fast as possible. At the end, a survey is applied. The collected data are used to evaluate the participant performance and to support new simulations, which characterize it as participatory sensing as well.In the first stage, within the game, participants could walk through the streets following the escape routes and points, which will characterize a “enter information into a map” task type  should be developed. In this sense, a TC could be run in which interdisciplinary teams would be tasked with submitting their ideas for review. During the whole TC process, participants would be able to submit and criticize ideas and vote in parallel How to distribute tasks along the crowdsourcing process?(ii)Among specialists and the public in general, how to select the most suitable participants for specific tasks and how to engage them?(iii)How to weight and merge crowdsourcing results in a transparent way and how to evaluate the efficiency of this applied heuristic?According to the RQ1.5, in analyzing whether or not the quality of work has been achieved, it is reasonable to expect that the main challenges and questions for quality will be found in task design, since all platforms pay attention to the work profileBesides the problem-solving aspect, where crowds can generate ideas, discuss, and make decisions collaboratively, crowdsourcing approaches may also explore humans as sensors. This capability is more evident on approaches focused on mapping tasks, such as VGI or participatory sensing with geographical location. In social media-supported crowdsourcing, for example, a Twitter user may be considered to be a sensor and a tweet created by a user will be sensory information . This soIn previous work, we had conducted a comprehensive literature review on crowdsourcing platforms in the context of urban planning . The goaTwo major contributions were reported in this paper: (i) A five-dimension typology of quality in crowdsourcing, which can be leveraged for optimizing urban planning and emergency management processes; and (ii) a comparison between crowdsourcing tools designed for urban planning and urban emergency management, which was explored in the previous section and summarized in the The framework draws attention to the social, technical, institutional, and policy aspects that need to be considered to manage the crowd work across these key dimensions. Concerning the strengths of using crowdsourcing in urban planning and emergency management, the possibility of asynchronous and remote crowd participation is most suitable for citizens that feel uncomfortable to contribute in face-to-face meetings or have difficulties to attend specific places or schedules. Through crowd decision documentation, the discussions and results involved in the solution process are documented in the crowdsourcing system, which makes it more transparent and safe. Once citizens can investigate problems, submit ideas, or observations, they strengthen relationships with the cities and places around them. As a result, crowds can generate different solutions and perspectives for the same problem. Especially in the context of urban emergency management, the problems are more tactile, urgent, and the sense of relevance is bigger when compared to urban planning. If implemented successfully, crowdsourcing tools and strategies can be replicated in different cities with similar problems and contexts while contributing to massive crowd engagement. In urban emergency management, people from different places can identify with the relevance and urgency of the proposal and feel motivated to collaborate in the crowdsourcing process. However, using crowdsourcing platforms has some drawbacks. For instance, finding suitable workers and matching them according to the task requirements implies that participants feel motivated to engage. At the same time, the task execution often requires special skills or reputation. Moreover, the crowd can feel unmotivated and drop the process if the goals are not clearly stated. Another potential drawback is related to the exclusion of a part of the population that is uncomfortable to participate in such digital events or social media. This is even more remarkable for elderly people who can have very particular viewpoints and extensive knowledge about the city. In addition, one potential threat of using crowdsourcing in real-world scenarios is the external manipulation of the outcomes generated by the crowd. Furthermore, private information can be leaked and this is particularly dangerous in the context of urban emergency management.Through a critical lens, our systematic review inevitably has certain limitations regarding the bias in the selection of primary studies. This situation was also reported in previous work . Thus, Predicated on using the resources of the crowd to address urban emergency management in highly dynamic scenarios, the results of this study contribute to the recognition of the current landscape by observing tendencies, limitations, and opportunities for research in this field, besides serving as a reference for the implementation of future crowdsourcing solutions. While this research is limited to the context studied in the primary studies, future research aims at understanding the use of social media in disaster prevention and urban planning and how heuristics or applied algorithms could be used to synthesize and generate results from crowd inputs.