Assessing coastal vulnerability at the village level using a robust framework, the example of Canacona in South Goa, India.

Climate change poses a significant threat to coastal regions worldwide. This study presents and applies a modified Coastal Vulnerability Index (CVI) to assess coastal vulnerability at the village level, focusing on Canacona, a taluka in South Goa, India. It adapts the existing CVI methodology by incorporating additional variables to better represent the various dimensions of vulnerability, resulting in 21 variables split into a Physical Vulnerability Index (PVI) and a Social Vulnerability Index (SoVI). The results show spatial variability in coastal vulnerability across the studied villages, with Agonda and Nagercem-Chaudi found to be highly vulnerable and Loliem to be the least vulnerable. A hydrological modeling approach is also used to compare the CVI of every village with their susceptibility to inundation due to rising sea levels. The results demonstrate the influence of local factors on vulnerability, challenging previous taluka-level assessments given the scale upon which adaptation typically takes place.

Spatio-temporal assessment of COVID-19 lockdown impact on beach litter status and composition in Goa, India.

The study aimed to understand beach litter status at some of the world-famous beaches of Goa, West India, to comprehend the impact of the Covid-19 lockdown. We characterize litter in six categories (Nylon+Rubber, Plastics, Footwear, Glass, Metal, and Thermocol) for eight sampled beaches in the north and south Goa. All beaches show increased glass and decreased plastics (significant litter) during the lockdown period compared to the unlock period that marked the high tourist inflow. Beaches were classified and graded with colour codes using litter density exhibit light blue-green colour coding during the lockdown or unlock period, suggests clean maintenance. The Miramar beach located in the heart of the capital city showed relatively more litter density (yellow code) due to the combination of local people and tourist inflow. Morjim, Palolem, Velsao were littered the least during both periods.

Did Covid-19 lockdown positively affect the urban environment and UN- Sustainable Development Goals?

This work quantifies the impact of pre-, during- and post-lockdown periods of 2020 and 2019 imposed due to COVID-19, with regards to a set of satellite-based environmental parameters (greenness using Normalized Difference Vegetation and water indices, land surface temperature, night-time light, and energy consumption) in five alpha cities (Kuala Lumpur, Mexico, greater Mumbai, Sao Paulo, Toronto). We have inferenced our results with an extensive questionnaire-based survey of expert opinions about the environment-related UN Sustainable Development Goals (SDGs). Results showed considerable variation due to the lockdown on environment-related SDGs. The growth in the urban environmental variables during lockdown phase 2020 relative to a similar period in 2019 varied from 13.92% for Toronto to 13.76% for greater Mumbai to 21.55% for Kuala Lumpur; it dropped to -10.56% for Mexico and -1.23% for Sao Paulo city. The total lockdown was more effective in revitalizing the urban environment than partial lockdown. Our results also indicated that Greater Mumbai and Toronto, which were under a total lockdown, had observed positive influence on cumulative urban environment. While in other cities (Mexico City, Sao Paulo) where partial lockdown was implemented, cumulative lockdown effects were found to be in deficit for a similar period in 2019, mainly due to partial restrictions on transportation and shopping activities. The only exception was Kuala Lumpur which observed surplus growth while having partial lockdown because the restrictions were only partial during the festival of Ramadan. Cumulatively, COVID-19 lockdown has contributed significantly towards actions to reduce degradation of natural habitat (fulfilling SDG-15, target 15.5), increment in available water content in Sao Paulo urban area(SDG-6, target 6.6), reduction in NTL resulting in reducied per capita energy consumption (SDG-13, target 13.3).

Open data and injuries in urban areas-A spatial analytical framework of Toronto using machine learning and spatial regressions.

Injuries have become devastating and often under-recognized public health concerns. In Canada, injuries are the leading cause of potential years of life lost before the age of 65. The geographical patterns of injury, however, are evident both over space and time, suggesting the possibility of spatial optimization of policies at the neighborhood scale to mitigate injury risk, foster prevention, and control within metropolitan regions. In this paper, Canada's National Ambulatory Care Reporting System is used to assess unintentional and intentional injuries for Toronto between 2004 and 2010, exploring the spatial relations of injury throughout the city, together with Wellbeing Toronto data. Corroborating with these findings, spatial autocorrelations at global and local levels are performed for the reported over 1.7 million injuries. The sub-categorization for Toronto's neighborhood further distills the most vulnerable communities throughout the city, registering a robust spatial profile throughout. Individual neighborhoods pave the need for distinct policy profiles for injury prevention. This brings one of the main novelties of this contribution. A comparison of the three regression models is carried out. The findings suggest that the performance of spatial regression models is significantly stronger, showing evidence that spatial regressions should be used for injury research. Wellbeing Toronto data performs reasonably well in assessing unintentional injuries, morbidity, and falls. Less so to understand the dynamics of intentional injuries. The results enable a framework to allow tailor-made injury prevention initiatives at the neighborhood level as a vital source for planning and participatory decision making in the medical field in developed cities such as Toronto.

Mumbai's business landscape: A spatial analytical approach to urbanisation.

India has proven to be one of the most diverse and dynamic economic regions in the world. Its industry focuses predominantly on the service sector and immediate economic growth seems to steer India into the economic superpower. India's unique business landscape is felt at a regional level, where massive urbanization has become an unavoidable consequence of population growth and spatial allocation to the economic hubs of metropolitan cities. Mumbai, one of the world's largest cities, represents a unique combination of a diverse economic landscape and the growth of a megacity. The role of Mumbai in India's growth is of crucial importance for India's business landscape. This paper explores the massive urbanization processes of Mumbai's peri-urban areas and compares urban sprawl with the location of its business landscape. A spatial accounting methodology based on the proximity of Mumbai's different economic hubs will be used to measure the underlying pattern of the Mumbai region, concerning past and present urbanization, and the effect of this urbanization process has on the possible location of businesses. This business-urban ecosystem perspective will be implemented by a spatial analysis on the correlation between urban compactness and urban footprints, in relation to business concentration and its spatiotemporal evolution over the last hundred years.

Mars Terraforming: A Geographic Information Systems Framework.

This study has developed a GIS framework that uses spatial environmental and climate data to better understand areas on Earth that share the most environmental similarities to Mars. The purpose of developing this framework is to determine which vegetation is most likely to survive in closed bioregenerative life support systems on Mars, using as many in-situ materials and environmental elements as possible. Using remotely sensed climate data, digital elevation models, and vegetation occurrence data sourced from the Global Biodiversity Information Facility, three Mars-like study areas on Earth were analysed (the Antarctic Peninsula, Ellesmere Island, and Devon Island). This study found that plants that are part of the Bryophyte and Tracheophyta phyla are worthy of further research in regard to possible vegetation candidates that could be brought to Mars. In addition, the most promising candidate of the entire study is the genus Poa, which is found in the phylum Tracheophyta.

A geographical exploration of environmental and land use characteristics of suicide in the greater Toronto area.

In developed countries, suicide has become one of the leading causes of mortality. With approximately 3500 cases taking place in Canada annually, it is currently the seventh-most common cause of death. A clearer spatial understanding of the suicide landscape in rapidly changing urban environments would especially help mitigate this problem. This study examines suicide rates in Toronto between 2004 and 2011 as to understand the spatial distribution of suicide by means of the importance of metropolitan places. The study uses geocomputation and statistical methods, enabling spatial analysis as tools to further assess the prevalent gender disparities of self-harm, advancing the current findings in the suicide literature. The findings clearly expose that the dichotomy of gender (male and female) produce different spatial patterns of self-harm, and are impacted by landscape characteristics differently. Specifically, the configuration of different land cover types have a much great impact on the female population than male. This spatial-exploratory understanding of not only the geographical distribution of rates, but also an assessment of landscape influence can help to mitigate suicide depending on demographic and spatial-explicit characteristics found through advanced geostatistical and spatial analytical modeling.

Pollen sleuthing for terrestrial plant surveys: Locating plant populations by exploiting pollen movement.

PREMISE OF THE STUDY: We present an innovative technique for sampling, identifying, and locating plant populations that release pollen, without extensive ground surveys. This method (1) samples pollen at random locations within the target species' habitat, (2) detects species' presence using morphological pollen analysis, and (3) uses kriging to predict likely locations of populations to focus future search efforts. METHODS: To demonstrate, we applied the pollen sleuthing system to search for artificially constructed populations of Brassica rapa in an old field. Population size varied from 0-100 flowers labeled with artificial pollen (paint pellets). After characterizing the landscape, we pan-trapped 2762 potential insect vectors from random locations across the field and washed particulate matter from their bodies to assess artificial pollen abundance with a microscope. RESULTS: Population size greatly influenced artificial pollen detection success; following random pollen trap sampling and interpolation, ground surveys would be best focused on identified areas with high pollen density and low variation in pollen density. Sampling sites most successfully detected artificial pollen when they were located at higher elevations, near showy flowering plants that were not grasses. DISCUSSION: Detection of nascent populations using the proposed system is possible but accuracy will depend on local environmental factors (e.g., wind, elevation). Conservation and invasive species control programs may be improved by using this approach.