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Urban Well-being Factors Dataset
Mental Health & Wellness
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About
This dataset explores factors influencing urban well-being and satisfaction across various cities globally. It compiles crucial features and measurements to offer insights into living conditions and population contentment in urban settings. The data aims to facilitate the analysis of relationships between diverse urban elements and city happiness. It is also designed for use with a Deep Q-Network model, PIYAAI_2, which employs Reinforcement Learning to provide accurate predictions and enhance performance over time as it adapts to new data and environmental changes. This dataset is an original and exclusive creation by Emirhan BULUT.
Columns
- City: The name of the particular city.
- Month: The month in which the data was recorded.
- Year: The year in which the data was recorded.
- Decibel_Level: The average noise levels in decibels, indicating citizens' auditory comfort.
- Traffic_Density: The level of traffic density (options include Low, Medium, High, Very High), which may affect citizens' daily commute and stress.
- Green_Space_Area: The percentage of green spaces within the city, which contributes positively to inhabitants' mental well-being and relaxation.
- Air_Quality_Index: An index that measures air quality, a vital factor impacting citizens' health and overall satisfaction.
- Happiness_Score: The average happiness score for the city, rated on a 1-10 scale, representing the subjective well-being of the population.
- Cost_of_Living_Index: An index measuring the cost of living in the city, relative to a reference city, potentially influencing citizens' financial satisfaction.
- Healthcare_Index: An index measuring the quality of healthcare in the city, an essential part of the population's well-being and contentment.
Distribution
The dataset is provided in a CSV format, specifically 'test.csv', with a file size of 2.47 kB. It consists of 10 columns and 51 records. All data pertains to the year 2030, with various months represented.
Usage
This dataset is ideal for:
- Urban Planning and Policy Making: Gaining insights into factors that influence city happiness to inform policy decisions and urban development strategies.
- Academic Research: Analysing the relationships between urban factors and population well-being.
- Data Analysis: Exploring trends and patterns in urban living conditions and satisfaction.
- Machine Learning Model Development: Training and evaluating predictive models, such as Deep Q-Network models like PIYAAI_2, for forecasting city happiness and adapting to environmental changes.
Coverage
The dataset covers 51 unique cities from various locations around the world. The time range for the recorded data is specifically the year 2030, with data recorded across nine different months within that year.
License
CC BY-NC-SA 4.0
Who Can Use It
- Urban Planners: To develop strategies that improve city living conditions and citizen satisfaction.
- Government Bodies and Policymakers: To make informed decisions regarding public health, infrastructure, and environmental policies.
- Researchers and Academics: To study urban sociology, environmental science, public health, and artificial intelligence.
- Data Scientists and Machine Learning Engineers: To develop and refine AI models for predicting and understanding urban well-being.
- Non-governmental Organisations (NGOs): To advocate for improvements in urban environments and public services.
Dataset Name Suggestions
- Global City Happiness Metrics 2030
- Urban Well-being Factors Dataset
- City Quality of Life Index
- Metropolitan Satisfaction Indicators
- Future Cities Happiness Data
Attributes
Original Data Source: Urban Well-being Factors Dataset
QUALITY