Geographic Information Systems: Notes from Professor Govorov

arcGIS London Offices Timelapse

Geographic Information Systems: Notes from Professor Govorov

The inception of Geographic Information Systems (GIS) dates back to the early 1960s, marked by the visionary efforts of pioneers like Roger Tomlinson who envisioned the potential of computer-driven spatial data analysis.

Tomlinson’s seminal work culminated in 1963 with the creation of Canada Geographic Information Systems (CGIS), a pioneering effort aimed at aiding the Canadian government in land resource management. CGIS set the stage for subsequent developments in GIS technology.

The ensuing decades witnessed significant strides in GIS adoption, fueled by advancements in computing technology and geographic data collection methods.

This evolution led to widespread utilization of GIS across various sectors including urban planning, natural resource management, and telecommunications.

Q&A with Professor Michael Govorov

What is the role and importance of geospatial mapping in understanding and analyzing spatial data?

Geospatial mapping is integrated throughout the initial, intermediate, and final phases of spatial analytics. If maps are accessible, they can aid in the initial planning of analytical tasks; consequently, the human brain remains the most potent tool for devising spatial modeling approaches through visual analysis of maps.

During intermediate stages, generated maps can display interim findings and validate discoveries. Generally, the results of spatial analyses should be depicted using maps, frequently presented in carefully crafted cartographic layouts.

What are some of the key techniques or methods used in geospatial mapping and analysis?

There exist numerous techniques for geospatial analysis, and the selection depends on the objectives, content, and available datasets for analysis. Defining key techniques is challenging without knowledge of the specific content area.

However, geospatial mapping techniques often derive from traditional cartography’s map design principles. Certainly, these techniques must be suitably adapted for devices that display maps.

Can you share a real-world case study where geospatial mapping and analysis played a crucial role in solving a complex problem?

Based on my consultancy experience, a large food production company intended to construct a plant but then commissioned an investigation into the ecological situation at the potential site.

Geospatial analysis and mapping revealed that concentrations of radon and uranium in groundwater at the sites could potentially pose harm. Consequently, the company decided to cancel the project in that region.

In today’s world, any real-world application that involves datasets with spatial components cannot be reliably solved without the use of geospatial analytics.

Can you provide insights into the role of geospatial mapping and analysis in addressing environmental justice issues and promoting social equity?

The majority of environmental applications involve the use of spatial data. There are several aspects of geospatial mapping and analysis, including the estimation of environmental resources, monitoring and enforcing environmental regulations, assessing risks and preparing for disasters, advocating for policies and decision-making, and empowering communities through participation.

Can you discuss any innovative or cutting-edge research projects you are currently working on in the field of geospatial mapping and analysis?

In a recent project, we used high-resolution satellite imagery from two time periods to automatically delineate destroyed buildings in a Ukrainian city. Despite employing spectral and spatial supervised classification techniques, including elements of object classification, we found that they were unable to precisely solve the task. Only through the use of neural network methods were we able to achieve satisfactory results.

Are there any interdisciplinary collaborations or partnerships that you have been involved in that have pushed the boundaries of geospatial mapping and analysis?

As an academic, one of my significant projects involved providing geospatial training to government servants in Ukraine.

This collaboration included academics from two prominent Ukrainian universities. Training materials from Canadian site were translated into Ukrainian and utilized by Ukrainian instructors to train over 150 government servants from various organizations across Ukraine.

Despite encountering challenges, including the risk of corruption, the project was successfully completed due to the close cooperation between Canadian and Ukrainian instructors.

In your opinion, what are some of the untapped potential applications of geospatial mapping and analysis that have not been explored enough?

Particularly intriguing to me is the utilization of Generative AI models for generating cartographic symbols for specific thematic maps, not just individual symbols but also for the overall design of maps.

How do you see geospatial mapping and analysis evolving in the next decade, and what impact do you think it will have on various industries? Or, are there any emerging technologies or advancements in geospatial mapping that you believe will revolutionize the way we analyze and interpret spatial data?

Hence, while there’s a prevailing sentiment that various AI techniques like GAN and CNN will dominate in generating maps and modeling spatial phenomena, spatial statistical methods such as spatial point pattern analysis, kriging etc. are also undergoing enhancements and are poised to integrate with AI techniques.

These integrated technologies, as a versatile tool, can find utility across various industries reliant on spatial information.

What advice would you give to aspiring students or professionals who are interested in pursuing a career in geospatial mapping and analysis?

Consider this: if you opt for a career in geospatial mapping and analysis, you’ll have the opportunity to secure employment anywhere globally, even in paradise locations like Fiji Islands.

GIS in action

% Access to Electricity in Africa

1998

Map of global access to Electricity in 1998

2008

2021

Access to electricity (% of populations)

Source: World Bank Maps, last updated May 2023.

2D Maps

World Bank Maps use 2D GIS to analyze global spatial data for development projects (such as the timeline shown above).

These maps offer insights into socio-economic trends, environmental factors, and infrastructure needs, aiding decision-makers in addressing challenges like poverty, infrastructure, and sustainability.

2D maps provide a straightforward representation of spatial data without the added complexity of depth perception or perspective. This simplicity makes 2D maps more accessible to a wider audience and easier to use for general navigation and basic analysis tasks.

3D Mapping and ArcGIS

ArcGIS software provides tools for processing and analyzing spatial data. With ArcGIS, you can build geographic information systems with accurate 3D representations of the Earth’s surface and its features, such as a timelapse our offices at Linen Hall on Regent Street in London.

ArcGIS offers a range of visualization techniques such as terrain shading, elevation profiles and 3D symbology to enhance the visual representation of geographic data. Users can also perform spatial analysis in three dimensions, enabling tasks such as line-of-sight analysis, volumetric calculations, and spatial interpolation.

3D geospatial maps are valuable for urban planning, environmental monitoring, disaster management, navigation and more. They provide more accurate and comprehensive representations of terrain, buildings, vegetation and other features compared to traditional 2D maps.

3D Timelapse

arcGIS London Offices Timelapse
ArcGIS 3D timelapse of GIA’s offices at Linen Hall on Regent Street, London, U.K.

3D maps incorporate essential variables for spatial analysis and visualization. These include elevation data like digital elevation models (DEMs) for terrain details.

Additionally, land cover types such as vegetation, buildings, water bodies, and transportation networks are represented in three dimensions to accurately depict surface features.

Socio-economic data like population density or infrastructure distribution can also be added to facilitate comprehensive spatial analysis and decision-making.

Diverse variables layered on 3D maps enable users to gain a holistic understanding of social, environmental, historical, political effects through time. Geographic information systems are only as useful as the variables they represent.

GovorovM

GIS at GIA

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