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A number of multidimensional poverty measures that respect the ordinal nature of dimensions have recently been proposed within the counting approach framework. Besides ensuring a reduction in poverty, however, it is important to monitor distributional changes to ensure that poverty reduction has been inclusive in reaching the poorest. Distributional issues are typically captured by adjusting a poverty measure to be sensitive to inequality among the poor. This approach, however, has certain practical and conceptual limitations. It conflicts, for example, with some policy-relevant measurement features, such as the ability to decompose a measure into dimensions post-identification and does not create an appropriate framework for assessing disparity in poverty across population subgroups. In this chapter, we propose and justify the use of a separate decomposable inequality measure – a positive multiple of “variance” – to capture the distribution of deprivations among the poor and to assess disparity in poverty across population subgroups. We demonstrate the applicability of our approach through two contrasting inter-temporal illustrations using Demographic Health Survey data sets for Haiti and India.

Accurate and rapid tracking and counting of building materials are crucial in managing on-site construction processes and evaluating their progress. Such processes are typically conducted by visual inspection, making them time-consuming and error prone. This paper aims to propose a video-based deep-learning approach to the automated detection and counting of building materials.

A framework for accurately counting building materials at indoor construction sites with low light levels was developed using state-of-the-art deep learning methods. An existing object-detection model, the You Only Look Once version 4 (YOLO v4) algorithm, was adapted to achieve rapid convergence and accurate detection of materials and site operatives. Then, DenseNet was deployed to recognise these objects. Finally, a material-counting module based on morphology operations and the Hough transform was applied to automatically count stacks of building materials.

The proposed approach was tested by counting site operatives and stacks of elevated floor tiles in video footage from a real indoor construction site. The proposed YOLO v4 object-detection system provided higher average accuracy within a shorter time than the traditional YOLO v4 approach.

The proposed framework makes it feasible to separately monitor stockpiled, installed and waste materials in low-light construction environments. The improved YOLO v4 detection method is superior to the current YOLO v4 approach and advances the existing object detection algorithm. This framework can potentially reduce the time required to track construction progress and count materials, thereby increasing the efficiency of work-in-progress evaluation. It also exhibits great potential for developing a more reliable system for monitoring construction materials and activities.

Using data on food insecurity in Israel, this chapter suggests borrowing techniques from the literature on multidimensional poverty to measure food insecurity, a distinction being made between “nominal” and “real” food insecurity. Various counting techniques are then implemented, including the well-known approach of Alkire and Foster. The chapter ends with a section where, following recent work by Dhongde, Li, Pattanaik, and Xu (2016), a distinction is also made between “basic” and “non-basic” dimensions of food insecurity.

The importance of gender equality is reflected not only in the Millennium Development Goals (MDGs), but also in the World Bank’s Gender Action Plan launched in 2007 as well as in other treaties and actions undertaken at regional and international levels. Unlike other gender poverty works, which are mostly based on monetary measurement, the purpose of this paper is to adopt a non-monetary approach.

The present study makes use of a counting approach to examine gender issues in Burkina Faso and Togo using household surveys, namely Enquête Intégrale surles Conditions de Vie des Ménages (2009/2010) and Core Welfare Indicators Questionnaire (2011), respectively. It focusses on six dimensions (housing, basic utilities, assets, education, employment and access to credit) largely recognized as MDG targets.

Main findings indicate that overall individuals are the most deprived in education in Burkina Faso, while the reverse situation is true in Togo. Gender inequality is observed in all dimensions since women always seem to be more deprived than men. The situation is also marked by regional disparities. Moreover, the assessment of dimensional contributions shows different patterns for each country. While employment proves to be the main contributor of gender inequality in Burkina Faso, three dimensions (assets, access to credit and employment) account together for most of the total contribution to gender inequality in Togo.

The main contribution of the paper is to use a multidimensional method (counting approach) to assess gender deprivation, with countries comparison. It also proposes an interesting combination of the decomposition by dimension with the subgroup’s decomposition in order to determine the largest contributor to gender inequality.

Virtually all analyses of hotspots have been devoted to a crude counting system, i.e. tallying the number of occurrences that take place in pre-specified units of space and time. Recent research shows that while usually half of all criminal events are concentrated in about 3 percent of places commonly referred to as “hotspots” of crime, similar proportions of harm concentrate in only 1 percent of places. These are “harmspots.” Identifying that harm is a more concentrated issue suggests wide policy and research implications, but what are the dynamics of these harmspots? The paper aims to discuss this issue.

This paper provides a descriptive framework for measuring, as well as evidence about, these patterns and concentrations, harmspots in Sussex, England.

There are four discrete offense categories that account for 80 percent of all the harm within harmspots. These categories include: sexual offenses, violence against the person, robbery and theft. Within these high harmspots, crime counts and harm are strongly correlated (r=0.82, p=0.001). Temporal analyses show that harmspots are not evenly spread across time and place, with night time and weekends becoming substantially more susceptible to harm – more than count-based models. Harmspot trajectory analysis suggests evidence of stability over time within the high harmspots; most harmspots remain chronically inflicted with harm. Violence and sexual offenses are random in their spatial distribution between the harmspots, but robberies and theft are more closely coupled to particular harmspots than others.

Implications of these findings are discussed in terms of future research avenues and crime policy.

The purpose of this paper is to develop a multidimensional innovation index (MII) framework for measuring and benchmarking multidimensional innovation of small and medium enterprises (SMEs) and groups of SMEs.

A counting dual cut-off method is employed. First, output and input innovation profiles and composite scores of individual SMEs are computed. Second, a set of four composite innovation indices are generated characterizing the group of SMEs under analysis: MIIo and MIIi measure multidimensional output and input innovation, respectively; while MIIr and MIIa assess the ratio and average of MIIo and MIIi, respectively. To test the MII framework, a survey was conducted among SMEs of the metalworking industry in Portugal.

In 2012, about 28.9 percent (42.2 percent) SMEs of the Portuguese metalworking industry were determined to be multidimensional output (input) innovative. The average percentage of dimensions for which output (input) innovative SMEs were innovative was 65.0 percent (66.0 percent). Thus, the industry MII vector was (MIIo; MIIi; MIIr; MIIa)=(0.188, 0.279, 0.674; 0.233). Significant differences were found across the industry, individual SMEs’ multidimensional output and input innovation scores, enabling the identification of groups of SMEs, which can be characterized and compared by computing the corresponding and specific MII vectors.

The research has limitations because of the small size of the sample and the benchmarking possibilities it provides.

The novelty of the MII framework lies in the counting dual cut-off method employed.

The use of the Web by academic researchers is discipline‐dependent and highly variable. It is increasingly central for sharing information, disseminating results and publicising research projects. This pilot study seeks to identify the subjects that have the most impact on the Web, and look for national differences in online subject visibility. The highest impact sites were from computing, but there were major national differences in the impact of engineering and technology sites. Another difference was that Taiwan had more high impact non‐academic sites hosted by universities. As a pilot study, the classification process itself was also investigated and the problems of applying subject classification to academic Web sites discussed. The study draws out a number of issues in this regard, having no simple solutions and point to the need to interpret the results with caution.

This paper presents a co-authorship study of authors who published in Digital Humanities journals and examines the apparent influence of gender, or more specifically, the quantitatively detectable influence of gender in the networks they form.

This study applied co-authorship network analysis. Data has been collected from three canonical Digital Humanities journals over 52 years (1966–2017) and analysed.

The results are presented as visualised networks and suggest that female scholars in Digital Humanities play more central roles and act as the main bridges of collaborative networks even though overall female authors are fewer in number than male authors in the network.

This is the first co-authorship network study in Digital Humanities to examine the role that gender appears to play in these co-authorship networks using statistical analysis and visualisation.

As an alternative poverty analysis approach, the purpose of this paper is to map the patterns of multiple deprivation in all 13 administrative regions of Namibia using the National Household Income and Expenditure Survey data.

Unsupervised statistical learning methods including the principal component analysis, k-means clustering and bivariate analysis were applied.

The results show that the multiple deprivation approach is a useful alternative in characterising poverty dynamics in the country. Specifically, the mapping shows that other dimensions of poverty such as access to utilities and services among other things are equally useful welfare indicators as they scored higher than income and consumption on discriminant ability.

Unpacking the multi-dimensionality aspect of poverty has drawn significant attention from development economists and continues to play a major role in policy formulation for developing countries. The study recommends buttressing of conventional income poverty measures with multiple deprivation approaches for a comprehensive picture on poverty issues.

This paper compares two alternative methods for measuring multidimensional poverty. This question has become extremely important in recent years, both in the scientific literature and in social policy. We propose to use latent class analysis to evaluate poverty in Spain. We make use of the “fuzzy set” approach, and compare the results achieved from these two methodologies.