Our thinking about software is often limited and full of basic assumptions and stereotypes — for most of us, it’s part of computer science and more just programming languages and related math questions. However, with the rapid development of the software industry and the digital revolution, more and more spheres of our lives are becoming interconnected with software in non-obvious ways. This article explores the role of Python in urban planning, highlighting its importance in using data analysis and programming for sustainable city development.
As our cities expand rapidly, it becomes crucial to find new tools to build suitable, sustainable and efficient spaces. The concept of smart cities was created to encompass all these aspects and find solutions using cutting-edge technology. Modern cities should become centers of economic growth, a place to nurture creativity and facilitate innovation. Creating a unique place that is efficient and suitable for its residents is a big challenge for urban planners and policy makers. They should consider the enormity of complications, such as congested roads, polluted air, limited water resources and inefficient waste management, that plague urban landscapes. The best way to achieve this is to connect physical and digital systems using tools such as data analytics, sensors and interconnected devices.
This is where data science engineering comes into play. To extract useful information from the vast amounts of data generated in urban environments, data science engineering combines the strengths of artificial intelligence, machine learning and data analytics. Urban planners interpret complex data sets from a range of sources, such as social media, government documents, IoT devices and sensors. These datasets provide invaluable information about environmental conditions, energy consumption trends, citizen behavior and traffic patterns. Making the most of these insights is critical to making well-informed urban planning decisions. Data-driven models with predictive analytics can help us optimize traffic flow, design energy-efficient infrastructure, waste management techniques, and improve public services to meet changing urban needs.
Application of programming languages in urban planning
- Smart buildings: Connected devices in built environments that detect patterns in daily activities and can be used to make decisions
- Air quality and pollution monitoring
- Traffic control and management: Real-time reporting for residents, intelligent traffic lights, intelligent street lights
- Fault reporting and maintenance: Sensors can detect when critical infrastructure is close to failure and report metrics such as temperature, voltage/current, etc.
- Security: Crime prediction through trend analysis
- Planning: The best place for new roads, new parks, new bus stops, etc
In the modern urbanized world, huge amounts of data are generated every day, from citizens’ complaints and reports to their search queries, daily movements, electricity meter readings, etc. By analyzing the spatial arrangement of buildings, roads and green areas, urban planners can identify patterns and trends within the city. They can detect areas with a high built-up density, locations without green spaces or areas with inadequate transport infrastructure. These insights can inform decision-making and urban development strategies. They can measure distances between buildings and public transport hubs, assess the accessibility of green spaces in residential areas and identify potential areas for urban revitalization or expansion. The extraordinary amount and multidimensionality of urban data require learning new tools and methods for collecting and gathering such data, shaping it into a specific form suitable for analysis and conducting analysis.
Most commonly used languages
- Python: Python is a versatile programming language that is widely used for data analysis and processing, as well as for creating web applications and scripts. It has a large and active community of users, which means that many libraries and tools are available for various tasks. Python is a good choice for urban planners who need to work with large datasets, perform spatial analysis, or build data-driven applications.
- R: R is a programming language and software environment for statistical computing and graphics. It is particularly suitable for data analysis, visualization and machine learning tasks. R has a large and active community of users and developers, and many spatial analysis and visualization packages are available.
- SQL: SQL (Structured Query Language) is a standard language for managing and manipulating data stored in relational databases. It is commonly used by urban planners to manage large data sets and run queries to extract specific information.
- GIS software: Geographic Information System (GIS) software is specialized software designed to work with spatial data, such as maps, satellite images, and other geospatial data. Popular GIS software includes ArcGIS, QGIS, and Google Earth. These tools can be used in conjunction with programming languages such as Python and R to perform spatial analysis and visualization.
Why choose Python as an urban planning tool?
A highly functional language offers several benefits to its users. It helps data analysts make sense of complicated data sets and makes them easier to understand.
Data collection and libraries
Python tools like BeautifulSoup and Scrapy are used by data scientists for data mining or data collection. For example, Scrapy makes it easy to develop systems that collect structured and organized data from the Internet. Additionally, Python users have access to a large library of free libraries. Libraries are even more enticing because they are constantly expanding and providing effective solutions.
The following libraries are particularly useful for urban planners:
- Numpy: A library for numerical calculations with a C interface (very fast!)
- Pandas: Tabular analytics library
- Matplotlib/Seaborn: Data visualization and chart library
- scikit-learn: Machine learning tools
- PyTorch and TensorFlow: Deep learning libraries (neural networks).
- GeoPand: Geospatial extension for Pandas
- ArcPy: A Python library that works with ArcGIS
- netx: Network Analytics Library
- Sheet: An interactive web-based mapping tool
- Floodlit: Dashboard tool
More useful resources for urban and regional planning can be found here.
Data processing
Python libraries are another tool that data analysts can use to organize large data sets and simplify mathematical processes. Working with large data tables is facilitated by a data structure called a data frame, provided by one of the aforementioned libraries, Pandas. Pandas allows you to add additional calculated columns based on a series of simple but effective actions after loading data into a dataframe. Overall, it helps in performing various data analysis tasks to increase productivity.
Data visualization
Data analysts must deliver insightful information in an understandable manner. Our brain processes images better than text. For this reason, data analysts interpret graphs, charts, and other visual aids to provide useful insights. Data analysts can create pie charts, graphs, histograms, and other visual representations of numbers using Python tools such as Matplotlib. This facilitates data analysts’ efforts to present their data in a readable and visually appealing way.
High readability
The great readability of Python is another advantage. When it comes time to adapt to new data sources and requirements, Python code makes it easy to collaborate with other analysts and communicate with other technical stakeholders. It also makes it easier to maintain. Data analysis professionals will find the language useful because it offers strong support and a wide variety of libraries for different tasks.
Easy learning curve
Among the more sophisticated programming languages of the past, Python stands out for its simple syntax and low learning curve. It reduces the amount of time data analysts would typically need to become proficient in a programming language. Therefore, if you are just starting out with the topic of data analytics, you will find that working with a simple but effective programming language is a pleasure. Fortunately, Python offers answers to most problems that arise when working with data.
Well supported
Although Python is a simple language, sometimes you will need help with it. Fortunately, it offers a wide range of handy libraries with helpful documentation. Furthermore, there are no costs associated with their use.
Plus, you have access to user codes, documentation, mailing lists, and more. In addition, users from all over the world can contact experienced developers and ask for help and guidance as needed.
Tools for advanced data analysis
Effective data management is just as important as data collection when it comes to data analysis. Python includes a number of built-in analytical tools that can be used to find trends and highlight relevant data for deeper understanding.
Conclusion
The use of Python in urban planning is expected to grow in the future. Python has the potential to be a key component in defining the next wave of smart cities, given the proliferation of IoT devices, AI-driven solutions, and real-time data analytics. Given Python’s adaptability and scalability, creative solutions for sustainable urban development are expected to emerge as the urban environment becomes more data-driven and networked.