From Data to Maps: A Beginner’s Guide to Geo-Visualisation

Introduction: My Journey into Geo Visualisation

When I began my studies as a rural engineer, I quickly realised the immense role geography plays in shaping our world. Understanding landscapes, water flows, soil types, and infrastructure is crucial for sustainable development and efficient land use. But while traditional maps and datasets provided valuable insights, they often felt static and disconnected from the real-world complexity I was trying to grasp.

That's when I discovered the power of 3D visualisation. Seeing terrain, infrastructure, and environmental data in a three-dimensional space made everything come to life in a way that flat maps never could. I was fascinated by how topography could be represented with precision, how urban planning could be simulated in real-time, and how spatial data could tell compelling stories when visualised dynamically.

What started as an academic interest soon turned into a passion. Whether it was exploring GIS (Geographic Information Systems), experimenting with satellite imagery, or diving into interactive mapping tools, I found myself drawn to the endless possibilities of geo visualisation. Over the years, it became clear that this technology was more than just a visual enhancement - it was a powerful tool for decision-making, capable of transforming industries from agriculture to disaster management.

In this blog, I want to share why geo visualisation is such a game-changer, what tools are available to explore it, and some real-world examples that highlight its impact. Whether you're a professional working with geographic data or simply curious about how maps and spatial analysis can shape our world, I hope to show you the power and potential of geo visualisation.

The Power of Geo Visualisation

Geo visualisation is more than just placing data on a map - it's about transforming complex geographic information into clear, interactive, and insightful representations that drive better decision-making. Whether in urban planning, environmental monitoring, or business intelligence, the ability to visualise spatial data unlocks patterns and relationships that might otherwise go unnoticed.

Understanding patterns and trends, such as shifts in population density, deforestation rates, or traffic flow dynamics, allows for deeper insights into complex systems. These insights support data-driven decision-making in areas like infrastructure development, logistics optimisation, and climate change mitigation. Additionally, presenting data in a clear and engaging way enhances communication and collaboration, making it easier for stakeholders to interpret and act upon key findings.

In urban planning for instance, 3D city models allow architects and policymakers to simulate future developments, analyse sunlight exposure on buildings, and optimise public transportation networks. Instead of working with static blueprints, they can explore dynamic environments, test different scenarios, and engage the public through interactive platforms.

One of the most exciting developments in geo visualisation is how accessible and versatile the tools have become. Not long ago, creating detailed maps or 3D geographic models required expensive software and specialised expertise. Today, thanks to a variety of powerful open-source tools, cloud-based platforms, and APIs, anyone - whether a researcher, developer, urban planner, or data enthusiast - can create stunning and interactive geographic visualisations.

In my work, I have predominantly used tools like QGIS (Quantum GIS: a free and open-source GIS tool) , Kepler.gl (a web-based tool by Uber for easy drag-and-drop geospatial analysis and visualisation), and OpenStreetMap (A collaborative mapping platform that provides free, editable map data used in many applications) for various geo-visualisation tasks. However, many other options are available, including commercial solutions (e.g. ArcGIS), which offer additional functionalities depending on specific requirements.

Examples in Kepler.gl: A Simple Yet Powerful Tool for Geo Visualisation

When it comes to ease of use without sacrificing power, Kepler.gl is one of the best tools available for geo visualisation. Whether you're a complete beginner or an experienced data analyst, this browser-based platform allows you to create stunning, interactive maps with just a few clicks - all without requiring coding or advanced GIS knowledge.

One of the biggest advantages of Kepler.gl is that it processes data locally in your browser. This means that the data you upload is not shared with external servers, keeping it private and secure. Despite running in a web environment, it efficiently handles large datasets and offers a range of advanced visualisation options, making it a go-to solution for quick and impactful geo analysis.

Kepler.gl supports multiple file formats, making it highly flexible for different types of geo visualisation projects. Two of the most commonly used formats are:

• CSV Files: A simple and quick way to import data, especially if you're working with structured datasets like those from Pandas DataFrames. You can load CSVs containing longitude and latitude values for point-based visualisations or polygon/multi-polygon data for regional mapping.
• GeoJSON Files: More suitable for complex geographic datasets, particularly if you need to visualise large polygon data. Instead of duplicating coordinate data within a CSV file, GeoJSON optimises memory usage while keeping spatial relationships intact.

Choosing Between CSV and GeoJSON:
- If your dataset contains relatively small polygons (e.g., city boundaries, small regions), CSV is a great choice for a quick setup.
- If your dataset includes large, detailed polygons (e.g., country borders, extensive land areas), GeoJSON can help prevent excessive memory consumption while maintaining efficiency.

In the following example we use data from the international disaster database covering earthquakes, floods, hurricanes, wildfires, etc. This dataset covers disasters of the past twenty years categorised by type including various other information as affected number of people by the disaster, country where it happened, year and duration, description of exact location, longitude and latitude etc. 

Before loading these data into Kepler.gl we need to prepare them as follows:

1. For time based animations, Kepler.gl requires the timestamp format (ISO 8601)
2. For items where the geo information is incomplete, we can use geocoding to derive coordinates from location descriptions.

The following two code snippets perform the required preparation steps:

Snippet 1 - converting a date string into ISO 8601 timestamp format

def parse_date_string(date_str):

    # Remove non-digit characters and split into parts
    date_parts = re.findall(r'\d+', date_str)
    if len(date_parts) < 3:
        raise ValueError("Not enough components found in date string.")
    
    # Separate the four-digit year
    year = next((int(part) for part in date_parts if len(part) == 4), None)
    if year is None:
        raise ValueError("No four-digit year found in date string.")
    
    # Collect remaining parts and convert to integers
    non_year_parts = [int(part) for part in date_parts if int(part) != year]
    
    # Identify month and day
    if len(non_year_parts) != 2:
        raise ValueError("Could not determine month and day from remaining parts.")
    
    # Month should be <= 12, day should be <= 31
    month, day = sorted(non_year_parts, key=lambda x: (x > 12, x))
    
    if not (1 <= month <= 12) or not (1 <= day <= 31):
        raise ValueError("Invalid month or day values found.")
    
    # Return a date object
    return date(year, month, day)


df['Time'] = '31.12.' + df['Year'].astype(str)
df['Time'] = df['Time'].apply(lambda x: parse_date_string(x).strftime("%Y-%m-%dT%H:%M:%SZ"))

Snippet 2 - evaluating longitude and latitude from location description using OpenStreetMap

# Initialize geolocator with user-agent (set your own user-agent string)
geolocator = Nominatim(user_agent="geo_locator")

# Function to get latitude and longitude with rate limiting
def get_lat_lon(location):
    try:
        time.sleep(1)  # Respect OpenStreetMap's 1 request per second limit
        geo_result = geolocator.geocode(location, timeout=10)
        if geo_result:
            return geo_result.latitude, geo_result.longitude
    except GeocoderTimedOut:
        return None, None
    return None, None

# Apply the function to the Location column
df["Latitude"], df["Longitude"] = zip(*df["Location"].apply(get_lat_lon))

Once you have applied above transformations, you can simply upload the dataset and perform your analysis. Below screenshot shows a categorisation by people affected. With the playback feature you can observe the development over time.

Another visualisation example is the population growth & development of Italy over time. The dataset uses polygon data for regions to visualise density shifts dynamically. Here again we can animate changes over years using the time-series playback feature.

These are just a few examples, but there are countless other applications across industries, each leveraging geospatial data to enhance decision-making and understanding:
- Analyse infrastructure development, tracking new roads, housing, or urban expansion.
- Map business locations and customer distributions for retail insights.
- Visualise climate change effects, such as shrinking ice caps or deforestation areas.

Conclusion

Geo visualisation is more than just a way to display geographic data  it's a powerful tool for understanding, decision-making, and storytelling. From my early fascination with 3D visualisation as a rural engineering student to seeing its transformative applications in industries like urban planning, disaster response, and logistics, it's clear that geographic data, when visualised effectively, can drive smarter choices and better outcomes.

With the increasing availability of free and open-source tools, anyone can now harness the power of geo visualisation - whether to track environmental changes, optimise infrastructure, or create interactive maps that enhance communication.

If you’ve never explored geo-visualisation, now is a great time to start. Consider how it could enhance a project or problem you’re working on—whether it’s mapping commute patterns, tracking environmental changes, or analyzing real estate trends, there’s a tool and dataset to support your needs. You can begin by experimenting with free tools like QGIS, exploring Kepler.gl’s intuitive interface, or creating a simple interactive map using Leaflet. With so many accessible options, getting started is easier than ever.

Links and Resources

• https://kepler.gl
• https://qgis.org/
• https://www.openstreetmap.org/
  
  

Author

Alberto Desiderio is deeply passionate about data analytics, particularly in the contexts of financial investment, sports, and geospatial data. He thrives on projects that blend these domains, uncovering insights that drive smarter financial decisions, optimise athletic performance, or reveal geographic trends.