What is Data Analytics?
The field of data analytics is the set of techniques used to analyze raw data (unprocessed data) in order to extract relevant information, trends, and insights. This process includes collecting data, organizing it, and storing it, then performing statistical analysis on the data. Once the information is collected, conclusions can be drawn from it, which can be presented visually to both those working within an organization as well as to external stakeholders, who can then apply it to problem-solving, business processing, decision-making, and predictions that can inform what a company’s next steps should be. This process relies on disciplines like mathematics, statistics, and computer programming.
How is Data Analytics Used for Urban Planning?
Over the past several decades, more people have been relocating to urban areas. This trend has increased the stress on various aspects of urban infrastructure, such as public transportation, community facilities, and housing. Those working with urban planning are faced with the daunting task of helping to design cities that are capable of supporting so many people. With the help of data analytic tools and techniques, Urban Planners can create urban environments that are cleaner, safer, and even more productive than ever before.
The following are some of the ways urban planning is benefitting from the field of data analytics:
- Enhancing communications between developers and those living in cities. In order to provide better options for those living in urban areas, it’s vital for Urban Planners to understand the issues people who live in the city face. New data analytic technologies can be applied to increase public engagement with initiatives, as well as to provide important information about their needs. Those living in cities can now offer thoughts on waste disposal and public transportation, which can be used for future improvements.
- Creating more efficient cities. With the growing use of IoT devices like smartphones, Urban Planners can now draw from a vast wealth of data, which helps to paint a clear picture of living arrangements in cities, the use of various facilities, and the popularity of public transportation options. By leveraging predictive analytics along with artificial intelligence, it’s now possible to execute simulations designed to accurately represent how a given urban development proposal would perform in the real world. Virtual models of transportation infrastructure can simulate traffic patterns in order to offer predictions about how incorporating a new system would affect city-wide traffic. This information is valuable because it helps Urban Planners to put infrastructure into place that can mitigate traffic and make travel more convenient for commuters.
- Providing better resource management. In order for a city or urban area to successfully grow, it’s crucial for resources to be managed appropriately. Data analytics helps Urban Planners gather information about how a city’s resources are used, as well as to spot areas that are not receiving a proper allocation.
- Utilizing geospatial data. In Singapore, Urban Planners are constantly searching for innovative ways to optimize the limited amount of space while at the same time providing a livable environment. Geospatial technology allows planners to gather information not only about current scenarios but also to make plans for future improvements that consider the needs of the Singapore people. Geospatial data helps Data Analysts to spot gaps in amenities and infrastructure in order to better allocate resources during a given time period.
- Addressing pollution. The health of those who live in a city is an important consideration for Urban Planners. Air pollution levels can pose a significant risk to air quality and overall health of residents. Over 80% of cities currently exceed the air quality targets established by the World Health Organization. Data analytic models can be used to address issues pertaining to air quality. For example, in Dublin, automatic air quality monitors gather data pertaining to the concentration of pollutants in the air, as well as how these levels compare to target levels. Google also offers an air mapping tool so that users can see the air quality level on a given street.
- Combining data sources for traffic incidents. When reviewing Naorobi’s traffic incident reports, researchers learned that of the 10,000 police records, 70% of accidents involved pedestrians and occurred in locations where the bus system was the main means of transportation. Simply looking at X (formerly known as Twitter) data would not have provided this important insight. However, by combining administrative data with public data, it was possible to gather more helpful information that can then be used by policymakers to make more informed decisions about how to address traffic fatalities.
- Crowdsourcing for better city planning. The idea behind crowdsourcing is that a group of people can provide information much faster than any one person. For example, tweets about tornadoes or hurricanes can provide policymakers with valuable insights into how the weather is affecting people in real-time. This is not possible with traditional data analytic tools. When researchers applied a machine learning model to over 800,000 tweets about traffic in Nairobi, they could then analyze these tweets and have a real-time, interactive map indicating the location and time of crashes. Data such as this, which is freely shared by the public, plays an important role in helping plan infrastructure.
In order to guarantee that researchers are using the best data available, and using it in a way that most serves the greater good, data must be checked. For example, with high-frequency data, such as information pertaining to the occurrence of car crashes over a holiday weekend, it’s possible to notice sharp peaks that indicate outliers rather than a consistent state. To prevent reaching conclusions that are driven by outliers, Data Analysts should clean data carefully.
The Future of Urban Planning & Data Analytics
As the population around the world continues to grow, developers are tasked with creating cities that are able to support the needs of residents. According to the United Nations Department of Economic and Social Affairs, 68% of the world’s population will dwell in urban areas by 2050. In the future, data analytics is expected to continue to play an important role in developing safer, greener, and smarter cities that can handle the influx of residents.
Get Started Learning Data Analytics with Hands-On Classes
Are you interested in learning more about data analytics? If so, Noble Desktop’s data analytics classes are a great starting point. Courses are currently available in topics such as Excel, Python, and data analytics, among others skills necessary for analyzing data.
In addition, more than 130 live online data analytics courses are also available from top providers. Courses range from three hours to nine months and cost from $219 to $60,229.
Those who are committed to learning in an intensive educational environment may also consider enrolling in a data analytics or data science bootcamp. These rigorous courses are taught by industry experts and provide timely instruction on how to handle large sets of data. Over 90 bootcamp options are available for beginners, intermediate, and advanced students looking to master skills and topics like data analytics, data visualization, data science, and Python, among others.
For those searching for a data analytics class nearby, Noble’s data analytics Classes Near Me tool provides an easy way to locate and browse the 400 or so data analytics classes currently offered in the in-person and live online formats. Course lengths vary from three hours to nine months and cost $119-$60,229.