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This study explores the deeper meaning of socio-economic frequency distributions, particularly in their accumulated form, called an OGIVE [1]. Due to the obsession of statisticians during the past century with “significance” little attention had been paid to statistical methods in the social sciences [2]. The first part of this study, reviewing the asymmetric nature of frequency distributions of socio-economic data may surprise statisticians in the medical and bio-science fields. Customary measures developed for their symmetric distri- butions do not apply to socio-economic data. The second part of this study deals with the Ogive, a cumulative form of the same frequency data. Textbooks on statistics mention the Ogive only as a curiosity, describing how to construct one. Yet never mentioned is its meaning, its interpretation, its contribution to a fuller understanding of the underlying socio- economic situation This study explores how to extract from an Ogive features of the frequency distribution that otherwise remain unnoticed.

In this paper we propose a novel approach that relates age-specific cohort fertility rates with socioeconomic conditions faced by each cohort in each single age to understand the behaviour of fertility. We implement this approach to study the evolution of fertility over the last 40 years in Spain, where both fertility and socioeconomic situations have undergone substantial changes during the period under scrutiny. We find female education and labour market conditions to be highly correlated with the evolution of cohort age-specific fertility rates in Spain.

The visual analytics approach has become an important tool for analytical reasoning in the fields of information visualization, scientific visualization and cartography. Much research effor...

Travel behaviour varies with respect to the income. Directly reported incomes used in the travel behaviour studies are subject to the issues of under and non-reporting. To account for this, we propose principal component analysis (PCA) on household asset ownership data to estimate socio-economic wellbeing score (SEWS) as the proxy of income. SEWS is used to understand the variation in travel behaviour of people belonging to different income groups in Vishakhapatnam. We have used sample data of 2623 households collected in 2012–2013. Internal coherency test and chi-square tests are conducted to assess the robustness of estimated SEWS. Travel behaviour analysis highlights that both the trip length and mode choice significantly varies with regard to the SEWS in Vishakhapatnam. People belonging to the low and low middle SEWS group are more dependent on walking and travel shorter distances as compared to the middle high and very high SEWS group. Encouraging the middle high and very high SEWS group to travel short distances and use low carbon modes of transport will need interventions related to the development control regulations and infrastructure provision. Since SEWS is estimated using multiple variables and captures the consumption pattern of the households, therefore, it can be used as the proxy of income in travel behaviour studies.

We demonstrated an innovative learning method of convolutional neural network (CNN) to identify landuse and land cover (LULC) patterns and extract features to disaggregate socio-economic factors by...

The COVID-19 pandemic has changed the way that society functions. The state governments have chosen different measures to respond to the spread of the virus as there was no clear evidence on what the best way is to respond. In this paper, we gather the evidence of governments’ stringency measures and couple it with countries’ socio-economic data (including the amounting of tests that each country performed) to explore how they influenced the COVID-19 spread. We evaluated how well seven different regression models can predict the spread of the virus. The spread of the virus is expressed through the number of positively identified individuals as well as deaths reported due to the virus. For the prediction of the number of new cases, ElasticNet algorithm was the best performing, followed by Linear Regression and LASSO. LASSO algorithms predicted the best number of new deaths. The metrics used to evaluate were: R-squared score, Adjusted R-squared Score, mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE). The most effective measure was the stay-at-home measure followed by workplace and school closures. With the availability of new and more accurate data and variables, the models can be further improved.

This chapter studies issues involved in the identification of urban centres and determining their boundaries. It takes a critical look at the prevailing procedures of distinguishing urban centres from rural areas (and related terms) and proposes a new approach for areal classification and an operational methodology to address the current deficiencies in the system as followed in South Asian countries in conducting their population Census. An examination of the methodologies used by global institutions for delimiting urban centres’ boundaries and estimating urban population is undertaken including OECD methods, the framework used in World Development Report and the e-Geopolis project. A fourfold criteria for areal classification are proposed. The method proposed is illustrated with a case study of Sri Lanka, comparing results with their official estimates. Integrating GIS technology with socio-economic data, it highlights how existing estimates of urban population would change as a result of the new approach.

Integrating socio-economic dimensions in ecosystems analysis and management is becoming increasingly important, particularly from a mapping standpoint. A key challenge with such integration is reconciling different geospatial representations based on census and administrative frameworks with natural ecosystems boundaries.This article presents one method for addressing this challenge by mapping an information rich 'ecumene'. In this approach, communities are mapped as human habitats using natural boundaries as opposed to administrative-type boundaries, integrated with authoritative socio-economic data. To illustrate the benefits of this approach, two example applications are provided that: 1) map and estimate the population of the 'forest ecumene' of Canada, and 2) map labour force distribution patterns associated with the forest sector and its relation to forest areas in Canada. Benefits and limitations of this approach are discussed, from which a number of priority areas for future research are identified.

Climate change puts at risk all current and future transport projects. Investing proactively in climate adaptation of transport infrastructure is paramount to providing resilience and sustainable transport systems that may promote social and economic growth. Despite the importance of such investments, the financial resources of many road administrations are constrained, creating an urgent need to allocate these resources efficiently to capture the highest social, environmental, and economic benefits. This paper aims to tackle this issue by presenting a methodology to prioritize climate change adaptation interventions based on a set of economic, social, and risk reduction criteria. This method uses a network-wide approach to the road system in order to capture co-benefits, redundancies, and costs of disruption of road segments due to flood events. All underlying data in each of the criteria is part of a geospatial database that includes the location of agriculture, fishery production areas, high poverty, and flood maps. The methodology was developed at the request of the World Bank Africa Team to support the Government of Mozambique to prioritize climate change adaptation intervention in two central provinces, Zambezia and Nampula. After combining criteria for agriculture, fishery, poverty, network criticality, and hazard risk, the results suggest that most roads in the coastal districts of these provinces could be identified as top priority for climate change adaptation interventions.

The present study tried to determine the spatial expansion of urban areas in all the cities in Turkey and to examine the relationship between this spatial expansion with the related demographic, employment, educational, industrial and other indicators using the Geographical Information Systems. The present study was made up of three parts. In the first part, the urban areas in Turkey were determined using the pixel-based image classification methods. In the second part, the development levels of the cities, one of socio-economic indicators, were determined using the Principle Components Analysis. In the last part, statistical analyses were conducted to examine the relationship between the development levels of cities and urban areas. The results obtained revealed that the cities with larger urban area were more in the western part of the country and while those with less urban area were in the eastern part of the country. A similar distribution was also true for the socio-economic development order. The Pearson correlation coefficient of 0.711 between these variables demonstrated that there was a linear positive correlation in between. According to the results of Moran’s I spatial auto-correlation analysis, the distribution of both urban area and socio-economic development throughout the country had a relationship with place. According to geographically weighted regression analysis, the demographic, education and health indicators had the biggest influence on urban area.

Over past few years, there has been lot of emphasis for promotion of entrepreneurship in India and particularly in the North-Eastern states. These states in India represent a distinct socio-economic and geographical identity. Research in areas of entrepreneurship and economy usually focuses on the number and size of the business entities. However, the age distribution of business entities gives a lot of useful insight about the formation or firms, structure and prevailing economic environment of any region. This paper attempts to respond the need of research in this direction. This region-specific and secondary data based study considers 7 North East sister states of India, namely Assam, Arunachal Pradesh, Manipur, Meghalay, Mizoram, Nagaland and Tripura. It uses the company registration data of the live companies, from Ministry of Corporate Affairs (MCA), Government of India for each year up to 2014. It also takes various other socio economic data from different government sources. Put together, these states are reported to have 1.025 million Micro, Small and Medium Enterprises (MSMEs) and 6265 companies. Assam has the lion’s share in numbers of MSMEs as well as Companies. In this research the age of the companies is calculated as on the year 2015 and pertinent questions about the rate of formation, growth and decline in these states in terms of the age of the business is discussed. The research tries to evaluate if there is any relationship between major socio economic parameters, formation of companies and MSMEs. This research contributes to the theory as well as practice by indicating the industry age and its characteristics in the North-Eastern region of India.

The present study “A study on job satisfaction of bank employees with reference to State Bank of India, Kanyakumari District” is an empirical study based on the survey conducted through census method among the employees working in the State Bank of India in Kanyakumari District. The primary data related to the employees working in the State Bank of India in Kanyakumari District have been collected through a pre-structured questionnaire, prepared by the researcher. The questionnaire contains questions pertaining to personal data, socio economic data, employment details and satisfaction in work.

Today the world faces the fact that 10 million tonnes of waste, primarily plastic waste, pas through the river. This global issue has become a serious problem that can be resolved with location-based that utilizes remote sensing technology, following recent developments of technology. This study aims to estimate the weight of potential plastic waste at the estuary before it enters the ocean and becomes marine debris. Several parameters are developed based on three main aspects, which are environment as graded by LULC, social aspect as provided by population density using building data, and economic aspect as provided by nightlight from NOAA’s VIIRS. The estimation used raster-based digital numbers processing, by using estimated data and providing weight. As a result, it shows that most of the metropolitan cities such as Jakarta contribute almost 507 tonne of plastic waste per day. For other cities, its generation is directly proportional to the increase in population. This study could become a consideration to synthesize policies, with the fact that massive population density impacts the increasing of the plastic waste generation.

Decision makers have to analyze both social and economic situation of the cities because of the budget constraint of the governments while considering investment to the cities. In this respect, data envelopment analysis (DEA) has been widely used to determine the efficiency of the cities and to make clear the needs of the cities calling decision making units. In this study, socio-economic ranking of the cities of Turkey is presented by using DEA and linear discriminant analysis and the cities are compared to each other according to the socio-economic development efficiency scores. Then, the highlighted inferences about the cities are presented according to the NUTs-1 classification. © 2018 Springer Science+Business Media, LLC, part of Springer Nature

The objective of this paper is to provide data users with a worldwide assessment of the age reporting in the Tanzania Population Census 2012 data. Many demographic and socio-economic data are age-sex attributed. However, a variety of irregularities and misstatements are noted with respect to age-related data and less to sex data because of its biological differences between the genders.

A direct demand model requires relatively little analysis time and incurs a low cost. It is also known to be useful for the preliminary screening of promising configurations or concepts. This study reviews direct demand models of 12 existing urban railways using demographic data based on a block group level which is approximately 1/24 of a traditional zone area. However, direct demand models are limited. Therefore, a new approach is suggested. The proposed method is based on a field study and an empirical analysis. The study finds factors that affect ridership at the station level. As a case study, the proposed approach is tested using 54 light railway transit stations. The results of this empirical study demonstrate its applicability to improve the error rates of the predicted ridership at the station level.

Russian Abstract: В работе анализируются шесть существующих методологий оценки социально-политических рисков (Индекс политической нестабильности; модель страновых политических рисков Коплина – О'Лири; Индекс мира и конфликтной нестабильности Лэджера; Глобальный индекс мира; Индекс неустойчивости государств Университета Джорджа Мейсона; Индекс несостоявшихся и неустойчивых государств). Разбирается теоретическая модель социальной нестабильности, вводится методология сетевого анализа и приводится прогноз страновых рисков для наиболее нестабильных стран. English Abstract: The paper analyzes six existing methodologies for assessing socio-political risks (the Index of Political Instability, the model Koplin's country political risks - O'Leary; Index of Lager's peace and conflict instability; Global Peace Index; The George Mason University Instability Index; Index of failed and unstable states). A theoretical model of social instability is analyzed, a methodology of Network analysis is introduced, and a forecast of country risks for the most unstable Countries is provided.

Understanding the vegetation dynamics in urban areas in both quantitative and qualitative aspects is essential to population welfare and also to economic, social and environmental development. However, it is necessary appropriate tools for monitoring and analysis of the landscape dynamic in a systematic way. Therefore, this study proposes a methodology to analyze the relationship between intra-urban vegetation and the social-economic data using the integrated techniques of remote sensing and GIS as well as data mining. This research intends to answer questions such as: Is it possible to extract the intra-urban vegetation as well as identify the intra-urban vegetation changes from medium spatial resolution images and digital image processing techniques? Is it possible to establish a relationship between the intra-urban vegetation changes and social-economic information using data mining techniques?

In implementing water reforms under the National Water Initiative, water managers need to draw on quantitative socio-economic data. These data can help water managers build 'profiles' of populations affected by water reforms; understand how water is used by those populations; and understand the possible socio-economic implications of changes in water availability as a result of reforms. This article describes where these data can come from, and discusses some of the advantages and disadvantages of these sources.

Subnational socio-economic datasets are required if we are to assess the impacts of global environmental changes and to improve adaptation responses. Institutional and community efforts should concentrate on standardization of data collection methodologies, free public access, and geo-referencing.

Agricultural land has changed remarkably response to the rapidly rural urbanization in China since the further market-oriented reform of early 1990s. Using accurate remote sensing images and socioeconomic data, this paper identified the temporal pattern of agricultural land conversion and estimated their driving forces during 1992-2010 in Yueqing City. Agricultural land change can be summarized to three trends: Firstly, the conversion between non-agricultural land and agricultural land was very remarkable, the non-agricultural land increased obviously from 1992 to 2010, in verse farmland decreased continuously; Secondly, the loss agricultural land mostly were the higher quality cropland. The expansion of settlements and industrial park was almost completely at the expense of fertilize farmland in flat area, but hillside and tideland were reclaimed for planting grain to keep the dynamic balance of total agricultural land amount. Thirdly, the changes of agricultural land distributed regularly and according to the distance to the No.104 national highway. In addition, the stepwise regression analysis indicated that both the booming of rural private enterprises and increasing of population was main driving force of agricultural land conversions. According to the results, we discussed the negative impact of fertile farmland shrink and gave some feasible advices in the end.

Most previously developed coastal vulnerability/sensitivity indices acknowledge that the addition of socio-economic variables would assist in defining vulnerable areas. This study investigated the incorporation of socio-economic variables into a GIS based coastal vulnerability index for wave-induced erosion in Northern Ireland. In this application, a socio-economic sub-index was developed to contribute potentially one third of the overall index score; the other components consisted of coastal forcing and coastal characteristic sub-indices. All variables were ranked on an arbitrary 1–5 scale with 5 being most vulnerable. The variables were merged within sub-indices and then the sub-indices were combined to produce the overall index. Several problems were encountered in assessing socio-economic vulnerability indicators. These relate to the inherent difficulties involved in ranking socio-economic data on an interval scale. Temporal aspects also caused difficulties as socio-economic variables vary ov...

It has become apparent that models that have been applied widely in economics, including Machine Learning techniques and Data Mining methods, should take into consideration principles that derive from the theories of Personality Psychology in order to discover more comprehensive knowledge regarding complicated economic behaviours. In this work, we present a method to extract Behavioural Groups by using simple clustering techniques that can potentially reveal aspects of the Personalities for their members. We believe that this is very important because the psychological information regarding the Personalities of individuals is limited in real world applications and because it can become a useful tool in improving the traditional models of Knowledge Economy., Comment: UKCI 2012, the 12th Annual Workshop on Computational Intelligence, Heriot-Watt University, 2012

In this paper, an effective algorithm has been proposed for the loss assessment of disasters. First, we talk about the loss assessment of disasters based on case-based reasoning, and then, the application of socio-economic data in case retrieve and case adjustment were discussed, at last, we take the loss assessment of affected people of flood disaster, typhoon and earthquake, to prove the algorithm, experiment results show that the algorithm is feasible and effective.

Addressing social determinants in the field of tuberculosis (TB) has received great attention in the past years, mainly due to the fact that worldwide TB incidence has not declined as much as expected, despite highly curative control strategies. One of the objectives of the World Health Organization Global Task Force on TB Impact Measurement is to assess the prevalence of TB disease in 22 high-burden countries by active screening of a random sample of the general population. These surveys provide a unique opportunity to assess socio-economic determinants in relation to prevalent TB and its risk factors. This article describes methods of measuring the socio-economic position in the context of a TB prevalence survey. An indirect measurement using an assets score is the most feasible way of doing this. Several examples are given from recently conducted prevalence surveys of the use of an assets score, its construction, and the analyses of the obtained data.

The nutritional status according to anthropometric data was assessed in 756 schoolchildren from 5 low-income state schools and in one private school in the same part of Rio de Janeiro, Brazil. The prevalence of stunting and wasting (cut-off point

The post harvest loss of potato in Ethiopia is more than 25% which includes harvesting loss. To minimize the harvesting loss, providing appropriate equipment (technology) is essential. As a result, a study was carried out to develop and select suitable potato digger for small holder potato producers. Comparisons were conducted on three types of potato diggers; traditional plow, AIRIC potato (ground nut) digger and third newly developed potato digger (BD digger). Bio physical and socio economic data were taken using a structured data sheet. Data was analyzed using descriptive statistics and non parametric statistical tests. The BD digger gave the highest average exposing efficiency (92.40%) and lowest tuber damage (0.81%). The average digging loss for BD digger, with 0.25 ha/hr working capacity, was the lowest (7.61%). The Kruskal Wallis analysis revealed highest positive rank sum (107) for the digger. Overall, farmers’ ranked BD digger as best performing. The financial analysis indicated that BD digger has a net benefit advantage of ETB 522 in one harvesting season from a quarter hectare of land. Thus, it is important to promote the technology as a means of post harvest loss reduction.

Farming Systems Analysis (FSA) should lead to insight in the functioning of farming systems and help to develop various alternative scenarios for development.

Famine early warning systems using socio-economic data suffer from several problems. One is that they cannot, and do not attempt to, distinguish between qualitatively different kinds of famine. The second is that they cannot predict these either accurately or early enough. This is because all the socio-economic indicators produce both false positives and false negatives, the indicators themselves are "late" and because interpretation of the data is complex and time-consuming. The third problem is that within the context of a famine that is occurring, these indicators cannot predict excess mortality. The argument is illustrated with examples from the 1984-5 famine in Darfur, Sudan.

Publisher Summary This chapter discusses some of the problems faced by data collection systems in developing countries. It discusses some exploratory data analysis (EDA) techniques that are well suited in meeting these problems. The chapter provides some examples taken from two large data collection projects in developing countries. Field research in developing countries poses special problems, many of which endanger the quality of the data collected. Often it is difficult to monitor data collection or check data automatically as they are collected. EDA methods are resistant to the effects of extraordinary observations. They are often easy to compute by hand and can usually be taught to people without college-level mathematics training. They provide graphical displays that are easy to interpret and that are designed to highlight extraordinary data points. EDA methods can provide a valuable first step in standard statistical analyses. In addition to being computable by hand, EDA methods are now readily available on a wide variety of computers in several languages. They are generally inexpensive computationally, even for moderately large sets of data. Often they can be placed on inexpensive microcomputers. EDA methods have been used in teaching statistics for the past 10 years, but only more recently has their value to research been widely recognized.