Geographical information systems

What Are Geographical Information Systems?

Geographical Information Systems (GIS) are sophisticated computer systems designed to capture, store, manage, analyze, and display all forms of geographically referenced data. Within the broader field of Financial Technology, GIS leverages location data to provide visual and analytical insights, enabling financial professionals to make more informed investment decisions and optimize various operations. By integrating diverse datasets—such as maps, satellite imagery, and statistical information—a GIS provides a comprehensive spatial perspective that traditional numerical data analysis alone cannot offer. This technology helps users visualize and interpret spatial information, revealing patterns, relationships, and trends related to physical locations on Earth's surface.

##⁴⁴, ⁴⁵, ⁴⁶ History and Origin

The conceptual roots of Geographic Information Systems trace back centuries to the art of cartography and mapping. However, the first true GIS as a computerized system began to emerge in the 1960s with advancements in computing technology. A p⁴², ⁴³ivotal moment occurred in 1963 when Canadian geographer Roger Tomlinson initiated a national land use management program for the Canadian government, inventorying natural resources. He is widely credited with coining the term "Geographic Information System" and is recognized as the "father of GIS." Ear⁴¹ly applications primarily focused on land-use planning and resource management, allowing for the creation of maps and the analysis of spatial data to understand patterns and relationships.

Th⁴⁰e Environmental Systems Research Institute (Esri), founded in 1969, played a significant role in developing and commercializing GIS software, with its ArcInfo software, released in 1982, becoming one of the first widely adopted GIS packages. Ove³⁸, ³⁹r the decades, the evolution of GIS has been driven by increased computing power, remote sensing capabilities, and the widespread adoption of Global Positioning Systems (GPS), making the technology more accessible and user-friendly.

##³⁶, ³⁷ Key Takeaways

• Geographical Information Systems (GIS) are computer-based tools that capture, store, analyze, and display location-based data.
• GIS integrates various data sources, including maps, satellite images, and demographic data, to offer spatial insights.
• In finance, GIS aids in strategic planning, risk management, market analysis, and optimizing resource allocation.
• The technology allows financial institutions to visualize complex data relationships, uncovering patterns that are not apparent in traditional tabular data.
• GIS applications are rapidly expanding beyond traditional uses, offering new opportunities for financial analysis, particularly in areas like real estate investment.

Interpreting Geographical Information Systems

Interpreting a Geographical Information System involves understanding how different layers of spatial and attribute data are combined and analyzed to generate meaningful insights. Unlike a simple map that merely displays features, a GIS allows for complex queries and analyses by linking data to specific geographic coordinates. For example, a financial institution might use a GIS to visualize the distribution of its customers (spatial data) alongside their income levels, spending habits, and proximity to competitors (attribute data). This allows for a deeper understanding of market penetration and underserved areas.

Th³⁴, ³⁵e value of GIS lies in its ability to highlight spatial relationships and patterns that are critical for strategic planning. Professionals interpret GIS output by identifying concentrations, gaps, trends, and correlations across geographical areas. This interpretation can inform decisions related to new branch locations, targeted marketing campaigns, or assessing localized risks. By ³¹, ³², ³³providing this context, GIS enhances the depth of market trends analysis and operational efficiency for financial institutions.

##²⁹, ³⁰ Hypothetical Example

Consider a hypothetical retail bank, "DiversiBank," that wants to expand its branch network. Instead of simply opening branches based on population density, DiversiBank utilizes a Geographical Information System.

1. Data Collection: DiversiBank feeds its GIS with various data layers, including existing branch locations, competitor locations, local demographic data, average household incomes, traffic patterns, and public transportation routes.
2. Spatial Analysis: The GIS processes this data, identifying areas with high concentrations of potential customers but low banking service penetration. It highlights neighborhoods where the average income aligns with the bank's target demographic and where accessibility is high.
3. Visualization: The GIS generates a heat map overlaying these data points onto a city map. Green areas indicate high potential for a new branch, while red areas show market saturation or unfavorable conditions.
4. Decision Making: Based on the GIS analysis, DiversiBank identifies a specific neighborhood that appears to be an optimal site selection. The insights provided by the Geographical Information System allow the bank to make an evidence-based decision, maximizing the potential return on its investment in a new branch and tailoring services to local needs.

##²⁷, ²⁸ Practical Applications

Geographical Information Systems have a wide array of practical applications in finance, moving beyond traditional mapping to offer critical insights for complex financial operations:

• Real Estate and Property Investment: GIS is an indispensable tool for real estate investment and management. It enables investors to analyze properties based on location, proximity to amenities, transport links, environmental risks, and local market values. This helps in identifying prime locations, conducting site selection, and assessing the performance of properties over time. Rea²⁴, ²⁵, ²⁶l Estate Investment Trusts (REITs), for instance, use GIS to understand factors driving market growth and to create compelling pitchbooks for investors.
• ²³ Risk Management and Fraud Detection: Financial institutions leverage GIS for sophisticated risk management by mapping flood zones, crime rates, or areas prone to natural disasters. This allows for more accurate credit risk assessment for loans and insurance underwriting. GIS²¹, ²² also plays a crucial role in fraud detection by identifying unusual spatial patterns in transactions.
• ¹⁹, ²⁰ Customer Segmentation and Market Analysis: By combining customer data with geographic information, GIS helps banks and other financial service providers understand local market trends, customer behaviors, and spending patterns. This informs strategies for customer acquisition, product development, and targeted marketing.
• ¹⁷, ¹⁸ Supply Chain Finance and Logistics: GIS supports supply chain finance by optimizing logistics, tracking assets, and identifying vulnerabilities within global supply networks. This includes analyzing transportation routes, warehouse locations, and potential risks from political instability or natural disasters. PwC¹⁵, ¹⁶ highlights how geospatial analytics provides comprehensive spatial insights to enhance the resilience and competitiveness of supply chains.
• ¹⁴ Portfolio Management: GIS can be used in portfolio management to visualize the attributes of an entire portfolio across different regions. This helps in understanding geographic diversification and developing proactive responses to events, thereby mitigating business risks and increasing asset value.

##¹³ Limitations and Criticisms

While Geographical Information Systems offer significant advantages, they are subject to certain limitations and criticisms that financial professionals should consider. One primary concern is the quality and availability of data. The accuracy of GIS analyses heavily relies on the completeness, currency, and precision of the underlying geospatial data. Incomplete or outdated data can lead to skewed analyses and flawed investment decisions.

An¹²other limitation relates to data privacy and security. As GIS integrates highly granular demographic data and personal financial information, ensuring the protection of sensitive data is paramount. The increasing granularity of spatial data raises concerns about exposing sensitive information, which necessitates robust security protocols.

Fu¹¹rthermore, the technical complexity of advanced GIS techniques often requires skilled personnel and robust infrastructure, which can be a barrier for smaller financial institutions. Integrating data from disparate sources can also present challenges due to data silos and a lack of interoperability. Whi¹⁰le GIS is powerful for identifying patterns, it is a tool for analysis, not a predictor of future events. It provides insights into past and present spatial relationships, but future outcomes are also influenced by unpredictable variables. The ISPRS International Journal of Geo-Information discusses how further progress in risk understanding and forecasting is needed to fully disentangle the complex interactions between hazards, exposure, and vulnerability, despite the advancements in geospatial tools for risk management.

##⁹ Geographical Information Systems vs. Geospatial Data Analytics

While often used interchangeably, Geographical Information Systems (GIS) and Geospatial Data Analytics refer to distinct but interconnected concepts. GIS primarily describes the system—a framework of hardware, software, and data—used for capturing, storing, managing, and presenting spatial data. It is the platform or infrastructure that enables the handling of geographic information.

In contrast, Geospatial Data Analytics refers to the process of analyzing geographical data to extract meaningful insights, patterns, and relationships. It is the application of analytical methods and techniques to spatial datasets, often performed using a GIS. Geospatial Data Analytics focuses on the "how" and "what" of deriving intelligence from location-based information, involving statistical analysis, modeling, and visualization to answer specific business questions. For instance, a GIS might store real estate parcel data, while geospatial data analytics would involve using that data within the GIS to predict property values or assess flood risk.

FAQ⁷, ⁸s

What kind of data does a GIS use?

A GIS uses various types of data, including vector data (points, lines, polygons representing features like cities, roads, or property boundaries) and raster data (grids of cells representing continuous phenomena like elevation or satellite imagery). It also integrates attribute data, which is descriptive information linked to these geographic features, such as population density, income levels, or sales figures.

Ho⁶w do financial institutions benefit from using GIS?

Financial institutions benefit from GIS by gaining location intelligence. This allows them to optimize branch and ATM placements, assess credit risk and property valuations, detect fraud detection, perform targeted marketing based on customer demographics, and enhance strategic planning for investments and asset management.

Is³, ⁴, ⁵ GIS only used in real estate and banking?

No, while GIS has significant applications in real estate investment and banking, its use extends to many other sectors. It is widely employed in urban planning, environmental science, logistics, public health, utilities, and emergency services for tasks such as resource management, infrastructure planning, and disaster response.

Can individuals use GIS, or is it only for large organizations?

While complex GIS applications are often used by large organizations, GIS technology has become increasingly accessible to individuals. Many user-friendly online mapping tools and open-source GIS software allow individuals and small businesses to perform basic geospatial analysis and visualize data for personal or professional projects.

How does GIS contribute to risk assessment?

GIS contributes to risk assessment by providing a spatial dimension to potential hazards. It allows for the mapping and analysis of geographical risks, such as natural disaster exposure for properties, crime rates impacting business locations, or supply chain vulnerabilities due to geographical disruptions. By visualizing these risks spatially, organizations can develop more informed financial modeling and mitigation strategies.¹, ²