Sustainable cities and 365 Data Science — Day 25
Sustainable Cities and a green economy
While I am doing my research on Smart Cities, I followed a program from Lund University in Sweden via Coursera.
What I found interesting with this course was the example of cities that are challenging general opinions and actually explore potential projects. — Some of you may know the term “living labs”, which in some cities are experimental projects, where products or procedures form part of an experiment, that may or may not result in a successful outcome.
Many cities in Europe seem to have adopted a strategy to include nature in this approach and build more green areas and parks. Moreover, the examples from countries like Netherlands and Denmark with extended bicycle lanes, provide a safer way for citizens to transport themselves by bicycles and hereby contribute to the reduction of carbon emissions.
In Singapore, where I live, the government set up the Green Plan which is supporting policies and strategies for a more sustainable city.
Like many other cities in Southeast Asia, Singapore is challenged with air pollution and carbon emission.
The current energy source is mostly based on fossil fuel and natural gas, and with high daily temperatures, most buildings are cooled with air-conditioning, which consumes a large amount of energy. The number of vehicles is increasing in the traffic, despite government regulations to enforce taxes on cars.
As a city-state, Singapore does not have a lot of land that can be cultivated as nature reserves. Nevertheless, the state is known as the green city, of urban development, which includes parks and green areas. The question is if this effort is enough to regulate the increasing temperature in housing and building spaces.
The Green Plan presents a range of policies and project engagement to reduce emissions by enforcing business policies, creating more green parks, and transforming the vehicle fleet to EVs.
There is a development of solar energy in the city. These installations are placed on a highrise rooftop or as experiments on one of the reservoirs.
Some startups are focusing on urban produces and other offerings to local food security. This has resulted in some urban garden development; mostly on rooftops and used by startups or Hotels.
The Green plan is a great initiative, and it will surely bring benefits to the city and the citizens.
There is still a significant dependency on personal vehicles, which hopefully will change over the years, as public transportation and other innovative transport offerings will be available.
With that in mind, it would be interesting to experience how Singapore could collaborate and learn from some of the European cities regarding reducing city traffic and providing more space for pedestrians and bicycles.
The pandemic has shown that it is possible to increase the bicycle fleet in Singapore, as more citizens take up this transportation mode. The risk is the dangerous road infrastructure, which is not designed for bicycles. It may introduce more accidents and reduce the trend to better health with zero-emission bicycle transport.
As mentioned earlier, Singapore is a city-state and is not affected by a lot of political challenges at the municipal level. This means that decisions can be taken faster, and projects or urban living labs can be initiated to explore and roll out Sustainability solutions for the city. This advantage places Singapore as a leader in Smart City projects, where other cities can collaborate and exchange experiences.
365 Data Science — Day 25
On day 25, the first milestone for 365 Data Science has been achieved. I completed the data literacy course exam, and am now ready for to next step. What was interesting with the last part of the course, was the methods of understanding data from a logical perspective, by getting a better understanding of statistics.
Some examples were brought to attention in order to explain correlation analysis and the significance of p-value and r-squared.
The p-value is the probability value, which describes how random the data occurs. The lower the p-value, the stronger evidence to reject the null hypothesis, and the more likely the data are purely random.
The r-squared can be used when validating the variances in linear regression, by indicating how well the regression model fits the observed data. Contrary to the p-value, the r-square must be higher, or closer to 1 rather than closer to zero in order to express the significance of the model fit.
Other interpretations models covered classification, forecasting, and accuracy.
Having a basic understanding of these interpretation processes gives a good basis to discuss data analytics and ask the right questions. Though it is important to keep practicing and keep up with the terms in data science.