Reprojecting AGEBS with Topological Techniques
What are AGEBS and Why are They Important?
In Mexico, the Basic Geostatistical Areas (AGEB) are fundamental geographical units for the collection, analysis, and presentation of statistical data. Defined and used by the National Institute of Statistics and Geography (INEGI), AGEBS enable efficient and detailed organization of census and socioeconomic information.
There are two main types of AGEBS:
Urban AGEBS: These are used in urban areas and are delimited by blocks. Each urban AGEB groups several blocks and is essential for data collection in high-density population areas.
Rural AGEBS: These are employed in rural areas and are delimited by localities. They group several small and dispersed rural localities, allowing for more accurate data collection and analysis in these areas.
AGEBS are crucial for the planning and execution of public policies, market studies, sociodemographic analyses, and various research projects. Their precision and detail allow decision-makers and researchers to better understand the characteristics and needs of different regions.
Motivation: The Importance of Reprojecting AGEBS Polygons
The urban AGEBS from 1990 present a high degree of error due to misalignments in their delimitation, which can significantly affect the accuracy of historical analyses and comparative studies. In contrast, the AGEBS from 2020 have been elaborated with greater precision, offering a more accurate and reliable reference framework.
Reprojecting the 1990 AGEBS polygons using the 2020 delimitations as a reference is essential to ensure the consistency and accuracy of geospatial data over time. This reprojection allows for:
Improving the quality of historical analyses: Aligning the 1990 data with the 2020 data provides a solid foundation for making accurate and consistent comparisons.
Facilitating informed decision-making: Decision-makers can rely on more precise and up-to-date data, enhancing the effectiveness of public policies and urban interventions.
Optimizing sociodemographic and economic studies: Researchers and analysts can obtain more accurate insights into trends and changes in the population and economy, contributing to a better understanding of urban evolution.
Reprojecting the 1990 AGEBS polygons using the more accurate 2020 polygons not only corrects historical errors but also establishes a reliable foundation for future analyses and developments. This process of updating and correction is crucial to maintaining the relevance and utility of geospatial data in the planning and urban development of Mexico.
Breaking Down The Problem
The AGEBs (Basic Geostatistical Areas) from 1990 present a significant challenge due to a spatial misalignment when compared to current data. Over the past three decades, urban areas have undergone extensive growth and transformation, leading to shifts in the boundaries and characteristics of these units. The 1990 polygons no longer accurately reflect the current urban landscape, which can hinder effective planning and analysis.
To address this issue, the objective is to adjust the 1990 polygons to align them with the 2020 polygons. This adjustment ensures that historical data remains relevant and useful for contemporary applications, allowing for a more accurate analysis of urban development and changes over time.
Let’s focus on this polygon marked in red. We can observe that this polygon aligns with the concatenation of three polygons from 2020.
This situation arises because cities have changed significantly over the years. Urban growth, development, and reorganization have altered the boundaries and structures of geostatistical areas. As a result, the older 1990 polygons no longer match the current urban landscape. To maintain accuracy and relevance in our geospatial data, it is necessary to adjust these outdated polygons to reflect the present-day configurations.
By combining the three 2020 polygons that intersect with the red-marked 1990 polygon, we can create a more accurate representation of the area. This method ensures that the updated polygons account for the changes that have occurred over the past three decades, providing a clearer and more precise depiction of the current urban environment. This approach is crucial for urban planning, resource allocation, and policy-making, allowing for better-informed decisions based on up-to-date spatial data.
Solution Propousal
Data Preparation:
1990 AGEB Polygons: This dataset represents the urban areas as they were in 1990.
2020 AGEB Polygons: This dataset represents the current urban areas, which have likely expanded and transformed significantly over 30 years.
Intersection and Neighboring Combinations:
Identify the 2020 polygons that intersect with each 1990 polygon.
Create combinations of these intersecting 2020 polygons. These combinations are specifically formed between neighboring (adjacent) polygons.
Dice Coefficient for Similarity Measurement:
The dice coefficient is an overlap based metric that compares an automatic segmentation (A) with a reference segmentation (B). The closer the coefficient is to 1, the better the automatic segmentation is considered to be.
For each 1990 polygon, the combination of 2020 polygons with the highest Dice coefficient is selected as the best match.
Replacement:
- Replace the 1990 polygons with the selected combinations of 2020 polygons. This ensures that the reprojected 1990 AGEBs accurately reflect the current urban structure.
Natalia’s Proposal:
For this project, I discussed with Natalia Cadavid, who proposed a simpler yet effective method. She suggested replacing the 1990 polygons with the concatenation of those 2020 polygons that cover at least 80% of the area of the 1990 polygon. This method was successfully implemented for the AGEB of Mexico City and provided a basis for further refinement and automation.
Results
Automatization for other cities
