How do you support urban planning with data?

1 Data sources

The first step to support urban planning with data is to identify and access the relevant data sources. There are many types of data that can inform urban planning, such as demographic, economic, social, environmental, spatial, and mobility data. Some of these data are available from official sources, such as census, surveys, administrative records, and open data portals. Others can be obtained from alternative sources, such as social media, sensors, satellite imagery, and crowdsourcing. You need to evaluate the quality, reliability, and timeliness of the data sources, as well as the ethical and legal implications of using them.

2 Data analysis

The second step to support urban planning with data is to analyze the data to extract meaningful insights. There are various methods and techniques that can be used for data analysis, depending on the type, size, and complexity of the data. Some of these methods include descriptive statistics, spatial analysis, regression analysis, cluster analysis, and machine learning. You need to choose the appropriate method for your research question, data availability, and analytical skills. You also need to use software tools, such as Excel, GIS, R, or Python, to perform the data analysis.

3 Data visualization

The third step to support urban planning with data is to visualize the data to communicate the findings. Data visualization is the process of creating graphical representations of data, such as charts, maps, graphs, and dashboards. Data visualization can help you to explore, summarize, and present the data in an effective and engaging way. You need to select the suitable type of visualization for your data and audience, as well as the design principles, such as color, shape, size, and layout. You also need to use software tools, such as Tableau, Power BI, QGIS, or D3.js, to create the data visualization.

4 Data storytelling

The fourth step to support urban planning with data is to tell a story with the data to persuade and influence the stakeholders. Data storytelling is the art of combining data, narrative, and visuals to convey a clear and compelling message. Data storytelling can help you to explain the context, relevance, and implications of the data, as well as to inspire action and change. You need to craft a story that has a structure, a plot, a character, a conflict, and a resolution. You also need to use techniques, such as emotion, analogy, metaphor, and humor, to make the story memorable and relatable.

5 Data collaboration

The fifth step to support urban planning with data is to collaborate with other data users and producers to share and learn from the data. Data collaboration is the practice of working together with other individuals or organizations to collect, analyze, visualize, and use data for a common purpose. Data collaboration can help you to access more and better data, to gain new perspectives and insights, and to increase the impact and reach of the data. You need to establish a data culture that fosters trust, transparency, and participation. You also need to use platforms, such as Google Drive, GitHub, or Slack, to facilitate the data collaboration.

6 Data innovation

The sixth step to support urban planning with data is to innovate with the data to create new solutions and opportunities for urban challenges. Data innovation is the process of applying data in novel and creative ways to generate value and benefit for society. Data innovation can help you to discover new patterns and trends, to test and improve existing policies and programs, and to design and implement new interventions and services. You need to adopt a data-driven mindset that encourages curiosity, experimentation, and learning. You also need to use frameworks, such as design thinking, lean startup, or agile, to guide the data innovation process.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?