Economic Quotient Estimation to Optimise Small City Connectivity to Boost Development

Abstract

Poverty is a huge issue in India and one of the
major factors contributing to this is the inaccessibility of
a region. Lack of connectivity of remote places gives
rise to issues like high poverty levels, limited
opportunities of employment, lack of medical and
transport facilities, poor education and infrastructure,
inaccessibility to resources during an emergency, slow
response from nearby cities, and ill-equipped
infrastructure to face an issue. Moreover, there is slow
movement of goods and services from the village/town
leading to slow economic development. By conducting
research on the connectivity, infrastructure, and poverty
levels of a region and the correlation between them, the
paper aims to help solve this issue by creating a model
and estimation of a coefficient for the target area. To
solve the problem, the algorithm takes into account
factors like highway density, internal road density, and
the night light data of a region to predict an estimation
of the economic quotient of that region. In addition to
this, using the night light data, a connectivity model is
built which gives the optimum connections of every
village/town in the region to its nearest city. This has
been done using machine learning, data analysis, and
image processing on satellite images of that region with
the help of computer vision. This data can then be used
by the government or other priva