GIS Consultancy
Habitat Expansion report for City of Trees (Coursework)
The ability of migration of species from one patch of habitat to another is crucial for different reasons. Landscape connectivity allows access to different resources for species to survive and it also allows poleward movement in response to anthropogenic climate change. In this report, I conducted the landscape ecological analysis along with the landscape connectivity by using the patch-corridor-matrix model. The whole analysis performed focusing on Lesser Spotted Woodpecker species and their habitat suitability. I considered different factors like the Age of the woodlands, conditions of patches, resistance towards the movement of LSW and other crucial variables. This report is divided into two phases, the first phase explains the qualities of woodlands managed by the City of Trees and the resistance model to find the UMT (Urban Morphology Type) land covers that impact the movement of LSW (Lesser Spotted Woodpeckers). The second phase explained the barriers towards the movement of LSW form one habitat to another in different areas of resistance and it also used functional connectivity for calculating the least cost path for bird movement. The different patches of land covers and UMT land uses that can be used for expansion and as new woodland corridors are also included as structural connectivity in Phase II.
TravelTime Analysis report
This project was a part of my internship and I was working in collaboration with Dr Alasdair Rae. We were tasked with calculating travel times to radiotherapy centres
across the UK and then figuring out how many people lived within 15,
30, 45, 60, 75 and 90 minutes of each location. Our results focus on the
45 minute threshold, but we generated data for each time increment.
We generated travel time areas for each radiotherapy centre across the
UK so that we could determine the number of people within 45 minutes
of each location. We also did this for 15 increments up to 90 minutes but
this summary focuses on the 45 minute travel time area (this is also
known as an isochrone). We looked at the results in the aggregate, for
the whole of the UK, and also at the more local level of Westminster
parliamentary constituencies, of which there are 650.
The main spreadsheet we produced contains information on the total
number, and percent, of people living within 45 minutes of a radiotherapy
centre in the UK, by driving and public transport. For population, we
used Census 2021 data for England, Wales and Northern Ireland and for
Scotland we used the most recent mid-year estimates. Travel times were
calculated using the TravelTime API via QGIS
(https://traveltime.com/gis). This tool is widely used for location
intelligence and travel time analysis by large and small organisations
across the world, as well as many local and national governments and
the NHS.
For Census data, we used the lowest geographic level of data we could
obtain, which for England and Wales was Output Areas, the new Data
Zones in Northern Ireland and Data Zones in Scotland.
We have data for all constituencies and all 15 minute increments up to
90 minutes, for both public transport and driving, but the main
spreadsheet we have created for you focuses on the 45 minute travel
time - see screenshot below.
Remote Sensing
FLOOD: Measuring the Flood Disaster, Preparedness and Relief Mechanism in Dibrugarh, Assam
Like many states in India, Assam has been suffering a lot from floods concerning environmental degradation. The two have been leading to this natural havoc for most of the districts. The district of Dibrugarh of the state was one of the prominent districts, which was still under the severe stress of flood where hundreds of villages and even towns were affected. The purpose of this study was to find out how floods are leading to environmental degradation and thereby its effects. With the help of field-based data as well as some secondary information, it has been revealed that soil erosion, deforestation, degradation of soil quality due to deposition of huge amounts of sand and silt, loss of thousands of plants, domesticated as well as wild lives even human being, water pollution etc. are subjected by the floods. These are leading to a lack of day-to-day requirements of livelihood like food, shelter etc., unhygienic living conditions, loss of cultivated crops, the occurrence of various diseases, damage and destruction of educational, religious institutions, Govt. and Non- Govt. offices, disruption of communication due to damage of roads, bridges etc. Besides, many of the riverine people have been displaced from their original habitation. Great rivers of Assam- the Brahmaputra and the Barak and their large number of tributaries.
Spatial and Statistical Analysis
Identfying the intensity of road accidents using the STATS19 dataset by UK governement
Road safety is a global concern and with the increasing traffic and number of vehicles on the road it has become one of the major causes of death for human beings. Road Safety 2018 report published by the World Health Organisation discussed that more than 1.35 million deaths of people each year are caused by road accidents. Therefore, it is necessary to use Road accident analysis as an important way of understanding and this study conducted by me showed casualties and crash data visualisation to identify the intensity of road accidents among rural and urban areas and also perform statistical analysis like gender, time of day, type of area and type of casualties. Road traffic accidents are not only related to these variables there are many other factors responsible for them like inexperience, risk-taking behaviours, excessive speed, and drug and alcohol use which are more responsible for the collisions of young drivers (Rolison, 2018). The rapid urbanisation that currently is shaping the world is also resulting in changes in policymaking as they lead to higher road accident rates in bigger cities (Arnaum, Curiel and Bishop, 2020). There is also a study conducted by David Clarke in 2009 which stated that accidents related to speed and loss of control on bends have occurred over 4 times more on rural roads compared to urban roads and over half of them occurred in night and dark hours. This study will use the STATS19 dataset of casualty and crashes datasheets to analyse the road accident frequency and intensity in home area type and also find the casualties regarding different casualty types, and genders by joining both of the datasets and using Python libraries like Panda, numpy and matplotlib. Pyplot for analysis and visualisation. In conclusion, the study tried to find how the frequency of road accidents varies in the variables chosen and the results can be seen through the link provided below.
Link for the analysis report
Child food poverty and maternal education: a statistical analysis
Food poverty among child under 5 years of age is very severe in some countries recorded by the UNICEF and WHO. 1 out of every 3 children suffer from severe food poverty in countries that are considered in lower and middle income groups in UN. Severe child food poverty includes the total percentage of under 5 children that are able to consume only 1 or 2 out of the 8 principle food groups.
All around the world there are millions of families that are failing to provide nutritious food that is basic necessity for the growth of children both mentally and physically. Considering the ongoing food crisis this situation is tend to be worsen and will present a great toll on numerous families and children.
This research primarily aims to examine the relationship between maternal education and children under food poverty by using linear regression as statistical measure. The data used in this report explains what are the relations between the food poverty and maternal education in different income groups, the impact of incomes of household in different countries and impact of other variables on food poverty like gender and place of living.
The statistics analysis is being applied on the data collected by UNICEF all around the world on child food poverty and the analysis is conducted on different income groups in which countries are divided according to the World bank. Some data changes have been done from original database to perform statistical analysis with less data complications and better results in the report.
Link for the analysis report
Geospatial Data Archives
Source: © PIB Mumbai, 2024
First Water Body Census Data, India
On 12th 2023, the first water body census data was released. The initial census was conducted with the reference year 2017-18 across the country in 33 States/UTs except Daman & Diu, Dadra & Nagar Haveli and Lakshadweep and this dataset is based on the 2024 updated version of the water body census. This data archive is compiled from the JSON files from the Indian open data source which wasn’t suitable to utilise for geospatial analysis, so to make the dataset smaller in size and easy to process I have converted it into GeoPackages. You can download these geopackages by clicking on the name of the area of your choice below. I am releasing this data free of charge in the hope that this data becomes more useful.
The dataset was originally made by the Department of Water Resources, River Development & Ganga Rejuvenation Ministery of Jal Shakti and is licensed under the National Data Sharing and Accessibility Policy (NDSAP) which means that users need to follow the guidelines from the National Data Sharing and Accessibility Policy.
I have changed the data into GeoPackage and transformed a few fields (like number of people benefited by water bodies, number of villages benefited by water bodies and more), from string to integer. Each files have 57 fields which I have kept the same as the original.
In summary, I have changed the data into GeoPackage and transformed a few fields (like the number of people benefited by water bodies, the number of villages benefited by water bodies and more), from string to integer. Each files have 57 fields which I have kept the same as the original. With the changes in the dataset, the integer fields can be used for better geospatial analysis.
**Note: Tripura is not included in this dataset because it's not available to download, showing this error on the downloading page:-"NoSuchKey: status code: 404, request id: tx000000000000006a9173c-00661db3bd-666722f8-staas-ndcbbs1, host id:
.jpeg)
Source: © Wikipidea, 2024
Indian Cricket Stadium Dataset
This stadium data consists of 25 major stadiums in India. There are 9 fields in the datasets: Sl. No, Name of the Stadiums, City, Capacity, First International match, X Coordinate, Y Coordinate, Established Year, used in IPL 2023 which can be used for spatial analysis and visualisation which is also used in Map Academy's YouTube video Use an svg point marker and size by value in QGIS. This is created using Google Maps and news articles from Cricbuzz, Wikipedia and other sources for data entry. I have compiled this data for free-to-use purposes and for any kind of work, commercial or otherwise. You just need to properly address the credit to the source of your data.