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We make use of the Volunteered Geographic Information (VGI) data to extract the total extent of the roads using remote sensing images. VGI data is often provided only as vector data represented by lines and not as full extent. Also, high geolocation accuracy is not guaranteed and it is common to observe misalignment with the target road segments by several pixels on the images. In this work, we use the prior information provided by the VGI and extract the full road extent even if there is significant mis-registration between the VGI and the image. The method consists of image segmentation and traversal of multiple agents along available VGI information. First, we perform image segmentation, and then we traverse through the fragmented road segments using autonomous agents to obtain a complete road map in a semi-automatic way once the seed-points are defined. The road center-line in the VGI guides the process and allows us to discover and extract the full extent of the road network based on the image data. The results demonstrate the validity and good performance of the proposed method for road extraction that reflects the actual road width despite the presence of disturbances such as shadows, cars and trees which shows the efficiency of the fusion of the VGI and satellite images.

This paper aims mainly at introducing applied statisticians and econometricians to the current research methodology with non-Euclidean data sets. Specifically, it provides the basis and rationale for statistics in Wasserstein space, where the metric on probability measures is taken as a Wasserstein metric arising from optimal transport theory.

The authors spell out the basis and rationale for using Wasserstein metrics on the data space of (random) probability measures.

In elaborating the new statistical analysis of non-Euclidean data sets, the paper illustrates the generalization of traditional aspects of statistical inference following Frechet's program.

Besides the elaboration of research methodology for a new data analysis, the paper discusses the applications of Wasserstein metrics to the robustness of financial risk measures.

This paper investigates the deterrence effect of development on crime against women in India. Specifically, the authors examine the deterrence effect of the composite development index, i.e. Human Development Index (HDI), along with other variables acting as development indicators such as women’s employment, the relative strength of women in the police force, urbanization, etc., on crimes against women.

This study adopts a fixed effect within-group (WG) panel regression model and pooled regression model on the data of 28 states over 20 years from 2000 to 2019. For checking the robustness of the results, the authors use the estimation from the system generalized method of moments.

The results confirm the deterrence effect of development as measured by the HDI and female labor force participation on various crimes against women. In addition, female feticide representing the socio-cultural attitude toward women turned out to be another significant determinant of almost all types of crime against women. Further, the study also finds the deterrence effect of variables such as police expenditure, the relative strength of women in the police force, urbanization and arrest rate on various crimes against women.

This research paper is unique because it tries to examine the deterrence hypothesis of development by taking a composite index of development, i.e. HDI and other variables at the state level in the Indian union.