SYSTEM AND METHOD FOR OBTAINING A DYNAMISM INDICATOR AND AN ECONOMIC POTENTIAL INDICATOR FOR A REGION

Description

1 / 18 SYSTEM AND METHOD FOR OBTAINING A DYNAMISM INDICATOR AND AN ECONOMIC POTENTIAL INDICATOR FOR A REGION FIELD OF APPLICATION [1] This patent application relates to a system and method for obtaining an indicator of the dynamism and economic potential of a region. Advantageously, the system provides the means to monitor and consolidate indicators in an agile and precise manner. The method, in turn, using a multivariate statistical technique, allows the obtaining of indicators that reflect both the dynamism of the studied economic environment and the economic potential of the region, based on the data provided by the system. [2] In this way, the system and the method, together, enable the continuous analysis of the economic conditions of a region. Furthermore, by providing accurate and up-to-date information, the invention acts as a strategic tool for decision-making in public policies, economic planning and investment targeting, ensuring greater efficiency and adaptability to regional socioeconomic dynamics. STATE OF THE ART [3] Historically, monitoring the economic conditions of regions and countries has been fundamental to guiding public policies, investments, and development strategies. However, the tools used for this analysis have evolved slowly over time, reflecting the limitations of the methods and technologies available at each time. In the early days of economic analysis, studies were based on rudimentary and time-consuming statistical information, collected manually and infrequently. This data often reached the Petition 870250005536, dated 01 / 23 / 2025, page 7 / 33 2 / 18 decision-makers experienced significant delays, making it difficult to respond to rapidly changing economic scenarios. [4] As the global economy became more complex, particularly with the advent of the Industrial Revolution, the need for more reliable and comprehensive indicators arose. However, progress was gradual. Economic statistics remained restricted to aggregate data, such as agricultural production or trade indices, and were often calculated with a significant delay, limiting their practical usefulness for strategic decisions. [5] Only in the 20th century, with the advancement of economic theories and the development of statistical sciences, was there a transformation in the monitoring of economic conditions. The introduction of metrics such as Gross Domestic Product (GDP) marked a watershed, offering a consolidated indicator of national and regional economic output. GDP allowed for the standardization of economic analysis, enabling comparisons between countries and regions. However, this metric, despite being widely adopted, has significant limitations, such as the inability to reflect local economic conditions in real time or to capture nuances such as the potential for economic development in specific locations. [6] In the late 20th and early 21st centuries, advances in data processing technologies and globalization brought new demands. Public managers, investors, and economic planners began to require more detailed, up-to-date, and specific indicators capable of assessing both current economic performance and future growth potential. This need resulted in the emergence of multivariate approaches and modeling techniques that integrate multiple socioeconomic variables, allowing for a richer and more contextualized analysis. Petition 870250005536, dated 01 / 23 / 2025, page 8 / 33 3 / 18 [7] However, even with these advances, a gap still persists in the ability to obtain economic indicators with frequent updates and minimal lag. Traditional tools continue to present challenges, such as long data consolidation periods and a lack of integration between structural and cyclical variables. It is in this scenario that the relevance of modern systems arises, such as the one described in the present invention, which uses advanced statistical techniques to generate agile and accurate indicators, reflecting both the dynamism and the economic potential of the regions analyzed. [8] The developed metrics are capable of offering a comprehensive view of the degree of development of a region, providing detailed information about its socioeconomic structure. Furthermore, by integrating trend analysis tools, these metrics can identify whether a region has growth potential, allowing it to predict its capacity to expand and attract new investments. This approach, by evaluating both the current stage of development and its future possibilities, offers an essential strategic perspective for public managers and investors. [9] However, there is little discussion about the concept of economic dynamism, a fundamental dimension that is often not adequately represented in traditional models. Economic dynamism reflects an economy's ability to respond quickly to stimuli, such as changes in demand, investments or public policies, and to adapt to new market scenarios. A city may be highly developed, with advanced infrastructure and a high standard of living, but if it is not very dynamic, it will face difficulties in growing or reinventing itself in the face of economic challenges.

[10] For example, a region with a consolidated industrial base, but heavily dependent on a single sector, may exhibit a high degree of Petition 870250005536, dated 01 / 23 / 2025, page 9 / 33 4 / 18 Development, but low economic dynamism. This occurs because the lack of economic diversification reduces its capacity to adapt to market fluctuations or sectoral crises. On the other hand, less developed regions, but with a more dynamic economic ecosystem, characterized by sectoral diversity and active entrepreneurship, may demonstrate greater growth potential, even starting from a weaker structural base.

[11] Therefore, the analysis of economic dynamism complements the assessment of development, allowing for a deeper and more strategic understanding of regional conditions. Metrics that incorporate dynamism are particularly useful for identifying regions that, although currently underdeveloped, have high growth potential due to their capacity for adaptation and innovation. At the same time, these metrics highlight the vulnerabilities of developed regions that may be economically stagnant due to a lack of dynamism.

[12] The state of the art presents several examples of tools capable of indicating the level of development of a region, city or country. However, it does not present solutions that indicate the dynamism of that region.

[13] An example of the state of the art is the article “Economic Development Index (EDI): Calculation for municipalities in the metropolitan region of Campinas, SP”, published in the Brazilian Journal of Management and Regional Development, which reveals the construction and evaluation of an economic development index (EDI), undertaken in the form of applied research with a quantitative, exploratory and documentary approach due to the type of data collected. The result detected economic fragility in the metropolitan region of Campinas, where only one municipality reaches an “acceptable” level of economic sustainability for a set of 34 indicators. For the calculation of the Economic Development Index Petition 870250005536, dated 01 / 23 / 2025, page 10 / 33 5 / 18 (IDE), a system of 34 indicators on the subject in question, with a high degree of relevance, was used for each city in the Campinas Metropolitan Region (RMC), totaling 680 municipal data points. Initially, the indicators were normalized taking into account their polarity (higher, better, or lower, better). With the normalized values, the IDE was determined by the arithmetic mean.

[14] The article presented as an example of the state of the art performs an analysis based on fixed data, using a set of 34 indicators to calculate the economic development index (EDI) in a static way. This approach offers only a momentary view of economic conditions, without considering the evolution or growth potential of municipalities over time. Furthermore, by using the arithmetic mean, the study assumes equal weights for all indicators, which may not adequately reflect their relevance. Another critical point is the absence of an analysis of the economic dynamism of the municipalities. The article does not assess the capacity of local economies to respond to stimuli, adapt to changes, or sustain long-term growth.

[15] Another example of the state of the art is presented by the article Constructing socio-economic status indices: how to use principal components analysis, published by Oxford University on 09 / 10 / 2006, which addresses the creation of socioeconomic status (SES) indices using Principal Component Analysis (PCA). The study highlights that, although income, consumption, and expenditure are theoretical measures of household wealth, collecting this data is costly. Thus, PCA is presented as an efficient alternative to generate SES indices from asset data, being validated as a method to differentiate socioeconomic status in a population. Petition 870250005536, dated 01 / 23 / 2025, page 11 / 33 6 / 18

[16] Upon analyzing the article, it reveals the same technical shortcomings as several state-of-the-art documents. The article on the use of Principal Component Analysis (PCA) in the construction of socioeconomic status (SES) indices presents several limitations that deserve to be highlighted. One of the main limitations is the static approach adopted, which reflects only a fixed temporal snapshot of the socioeconomic conditions of families. This characteristic prevents the analysis of trends or changes over time, which limits the understanding of the dynamism and evolution of economic conditions.

[17] Another critical point is the choice of variables used in PCA. Although the article recognizes the importance of selecting relevant indicators, the justification for including certain variables is vague, often based on face validity (appearance of relevance). This can lead to distortions in the results, especially when variables such as infrastructure characteristics are included without considering the impacts on the bias of the results, increasing perceived inequalities between the groups analyzed.

[18] Another example of the state of the art is patent document US11127026, filed on 03 / 24 / 2020, entitled Economic Condition Forecasting. This document describes a computational method for forecasting economic conditions, with an emphasis on identifying risks and opportunities in complex economic systems. The method uses advanced statistical techniques, such as Principal Component Analysis (PCA), complemented by additional methods, including random matrix analysis, bandpass filtering, synchronization, and early warning detection. Through risk identification, the method is also able to assess the economic potential of an environment, assisting in decision-making regarding the viability of investments. Petition 870250005536, dated 01 / 23 / 2025, page 12 / 33 7 / 18

[19] In a disadvantageous way, although the method uses PCA for economic data analysis, it appears to work with fixed data in a specific time frame, which can result in static forecasts that do not capture dynamic changes in the economic environment over time. The inability to consider trends or patterns of evolution makes continuous monitoring difficult, and the identification of economic dynamism is also not achieved, something essential for strategic decisions in volatile markets.

[20] Thus, based on the state of the art presented, the need to develop a system that captures recent data related to economic development is highlighted, with a method for calculating an Economic Dynamism Index (EDI) and an Economic Potential Index (EPI). EDI being an indicator of economic activity and adaptability, while EPI is an evaluator of the structure and capacity to attract investment.

[21] Thus, it is an objective of the present invention to provide a system for obtaining an indicator of the dynamism and economic potential of a region, capable of capturing and processing updated data efficiently and accurately. In this way, through the system it is possible to analyze economic performance and identify opportunities and risks in different regions. In addition, the system offers clear and easily interpreted metrics, such as the Economic Dynamism Index (EDI) and the Economic Potential Index (EPI), with frequent updates and minimal lag, promoting more informed strategic decisions aligned with current economic conditions.

[22] Another objective of the present invention is to provide a method for obtaining an indicator of the dynamism and economic potential of a region, capable of integrating structural and conjunctural economic data, processing them in an automated way and generating metrics that reflect both the Petition 870250005536, dated 01 / 23 / 2025, page 13 / 33 8 / 18 economic adaptability as well as the growth potential of the region analyzed. Thus, the method allows for the precise and up-to-date identification of conditions for economic growth and a region's capacity to sustain new investments, providing strategic information for planners, public managers, and investors. BRIEF DESCRIPTION OF THE FIGURES

[23] The objects of the present invention will be better understood in the light of the detailed description that follows in its preferred, but not limiting, embodiment, which is illustrated by the schematic drawings attached.

[24] Figure 1 presents, in a general and visual way, a method (M) aimed at obtaining an indicator of the economic potential of a region, where the following macro steps are presented: • M1 - Data collection: Includes obtaining information such as per capita income and population growth (IBGE), number of companies and sectoral diversity (Rais), broadband access (Anatel), education (MEC), hospital beds (SUS) and fleet of light and heavy vehicles (Denatran). • M2 - Treatment and normalization: In this step, the Min-Max technique is used to normalize the numerical characteristics, adjusting them to a specific range, in this case, from 0 to 100. • M3 - Weight Calculation: This involves calculating the weight of each sub-indicator in the final indicator, performed using Principal Component Analysis (PCA). • M4 - Calculation of the final indicator: Based on the weights calculated using PCA and the normalized values ​​of the sub-indicators, the final indicator is determined using the equation: Indicatori = Xk=i($subindicator:adorK x WeightK); Petition 870250005536, dated 01 / 23 / 2025, page 14 / 33 9 / 18 • M5 - Weighting: Considering the spatial spillovers of larger cities, a weighting was applied based on the geographical distance from the nearest hub city, according to the IBGE's hierarchy of Brazilian urban centers. In this context, municipalities closer to urban hubs have their indicator increased, while those further away have their indicator reduced proportionally to the distance.

[25] Figure 2, in turn, presents in a general and visual way a method (M), aiming to obtain an indicator of economic dynamism of a region, where the following macro steps are presented: • M1 - “Data source and collection”: Collection of data related to the economic dynamism indicator, such as services, commerce, industry, construction, agriculture, savings, and health plans. • M2 - “Treatment and normalization”: For the sub-indicators, the normalization technique with a base of 100 is used, established from a base date. • M3 - “Weight Calculation”: The weight calculation for each sub-indicator in the final indicator is performed using Principal Component Analysis (PCA). To calculate the sub-indicators, the values ​​of the weights estimated via PCA are multiplied by the values ​​of the respective sub-indicators.

[26] M4 - “Calculation of the final indicator”: From the weights calculated via PCA and the normalized series, the indicator is calculated using the equation Indicatori = Xk^Subindicadorf x WeightK).

[27] It should be noted that figures 1 and 2 represent a general and simplistic view of the method (M), in order to facilitate its understanding. Said method (M) is presented in detail throughout the detailed description. Petition 870250005536, dated 01 / 23 / 2025, page 15 / 33 10 / 18

[28] Figure 3 illustrates an example of the invention being implemented, where an economic dynamism indicator (EDI) was obtained for the city of São Paulo, through the temporal analysis of the indicators presented in the figure, based on method (M), briefly presented in Figure 2.

[29] Figure 4 illustrates an example of the embodiment of the invention, where an economic potential indicator (EPI) was obtained for the city of São Paulo, through the temporal analysis of the indicators presented in the figure, based on the method (M), briefly presented in Figure 1.

[30] Figure 5 illustrates the steps that a method (M) comprises. DETAILED DESCRIPTION

[31] The present invention relates to a system (S) and a method (M) employed for obtaining an indicator of economic dynamism (IDE) and an indicator of economic potential (IPE) of a given region.

[32] The system (S) is responsible for providing the means to monitor and consolidate local indicators of the region to be analyzed in an agile and accurate manner. Thus, said system (S) comprises a database (DB), a data extraction and integration module (MEID), a statistical processing and analysis module (MPAE) and a user interface (UI).

[33] The data extraction and integration module (MEID) interacts with the database (DB) in order to ensure that the collected and transformed data is properly stored, accessed and kept organized in an efficient manner. Petition 870250005536, dated 01 / 23 / 2025, page 16 / 33 11 / 18

[34] The database (DB) is structured in a relational model with the data extraction and integration module (MEID), where the data collected by this module are organized into specific tables for each type of economic indicator, such as the Economic Dynamism Indicator (IDE) and the Economic Potential Indicator (IPE). Each of these tables is responsible for storing data from public and private sources, according to the variable and its respective source.

[35] The database (DB) structure is flexible, allowing the segmentation of tables according to the geographic level of the analysis, whether municipality or state. This segmentation allows the database (DB) to store specific data for each region analyzed, which facilitates the comparison and analysis of regional indicators. Each data point entered into the database (DB) is associated with a unique identifier for each region, whether it is a municipality or state code, ensuring efficient segregation of data in different geographic contexts.

[36] With regard to data collection, updating and integrity, the database (DB) is configured to support continuous insertions and periodic updates performed through the data extraction and integration module (MEID). The relational integrity between the database (DB) and the data extraction and integration module (MEID) ensures that data is inserted into tables consistently, respecting defined formats and maintaining referential integrity between different fields and tables. To this end, the data extraction and integration module (MEID) performs consistency checks before inserting or updating data, identifying duplicate or inconsistent records and applying automatic corrections.

[37] The database (DB) also provides efficiency in data processing and retrieval. To this end, indexes are created on the most frequently consulted fields, such as region identifiers. Petition 870250005536, dated 01 / 23 / 2025, page 17 / 33 12 / 18 and the key variables of specific indicators. These indices accelerate database queries, allowing the calculations of IDE and IPE indicators to be performed quickly.

[38] In one embodiment of the invention, in order to provide security to the data stored in the database (DB), using techniques such as encryption for sensitive data and user authentication to ensure that only authorized individuals can make changes or access certain datasets.

[39] The data extraction and integration module (MEID), in turn, interacts with the database (DB) through APIs and structured queries, ensuring the exchange of data between the data extraction and integration module (MEID) and the database (DB).

[40] Furthermore, due to the scalability of the invention, the region analyzed can be a municipality or a state. Thus, in one embodiment of the invention, as long as the region analyzed is a city or a state, the data stored and collected vary.

[41] Thus, in one embodiment of the invention in which the region analyzed is configured by a city, the database (DB) comprises the informational data of the municipality that are presented in table 1. Table 1 - Variables for obtaining the IDE and IPE at the municipal level Variable Source Indicator GDP per capita IBGE IPE Economic Diversity RAIS / MTE IPE Number of firms RAIS / MTE IPE Hospital beds Datasus IPE Petition 870250005536, dated 01 / 23 / 2025, page 18 / 33 13 / 18 Broadband Anatel IPE Light vehicles, heavy vehicles Senatran IPE Population growth and education Inep IPE Number of formal workers in Industry RAIS / MTE IDE Number of formal workers in services RAIS / MTE IDE Number of formal workers in Commerce RAIS / MTE IDE Number of formal workers in Civil Construction RAIS / MTE IDE Number of formal workers in Agriculture RAIS / MTE IDE Number of active health plans Anvisa IDE Revenue from credit and debit cards Banco do Brasil IDE Free credit balance Banco Central do Brasil IDE

[42] In another embodiment of the invention in which the region analyzed is configured by a state, the database (DB) comprises the informational data of the state that are presented in table 2. Table 2 - Variables for obtaining the IDE and IPE at the state level Variable Source Indicator Monthly Trade Survey PMC IBGE IDE PMS, Monthly Industrial Survey PIM IBGE IDE Petition 870250005536, dated 01 / 23 / 2025, page 19 / 33 14 / 18 Monthly survey of services IBGE IDE Annual report of social information - RAIS RAIS / MTE IDE Survey of agricultural production - LSPA IBGE IDE Debit and credit card revenue Central Bank of Brazil IDE Payroll Caged / MTE IDE GDP per capita IBGE IPE Economic Diversity RAIS / MTE IPE Number of firms RAIS / MTE IPE Hospital beds Datasus IPE Broadband Anatel IPE Light and Heavy Vehicles Senatran IPE Population growth and education, public safety Inep IPE Basic sanitation Min. Regional Development IPE

[43] In turn, the statistical processing and analysis module (MPAE) is responsible for constructing the economic dynamism indicators (IDE) and economic potential indicators (IPE), processing data from the database (BD) to extract and consolidate meaningful information. This statistical processing and analysis module (MPAE) performs calculations in real time, ensuring the generation of sub-indicators and the consolidation of the main indicators, based on a robust set of mathematical algorithms and statistical analysis techniques.

[44] As shown in Figure 2, for each variable related to the IDE, the statistical processing and analysis module (MPAE) Petition 870250005536, dated 01 / 23 / 2025, page 20 / 33 15 / 18 using the (M) method obtains sub-indicators, using the observed values ​​for each municipality or state and comparing them with previously dated and defined values. Then, all the calculated sub-indicators are summed in a weighted manner to compose the final IDE. The weights assigned to each sub-indicator are calculated using Principal Component Analysis (PCA), which extracts the matrix of eigenvectors and eigenvalues, justifying the relevance of each variable in the context of the region's economic dynamism.

[45] Similarly, as shown in Figure 1, the IPE calculation is also performed using method (M). The statistical processing and analysis module (MPAE) transforms the data so that each sub-indicator has a standardized scale, essential for comparability between the various regions analyzed. After this transformation, the statistical processing and analysis module (MPAE) continues the process by applying the weights, derived from PCA, to the sub-indicators, thus generating the IPE.

[46] Therefore, the statistical processing and analysis module (MPAE) not only calculates the sub-indicators, but also integrates advanced statistical processes, such as PCA, to ensure that the weights assigned to the variables are mathematically optimized and adequately represent the contribution of each variable to the FDI and the IPE. The robust statistical analysis provided by the statistical processing and analysis module (MPAE) ensures that data transformation is carried out accurately and efficiently, providing sub-indicators that reflect the economic realities of the regions analyzed.

[47] Finally, the user interface (UI) presents the results clearly, allowing detailed visualization of the IDE and IPE of the region. In addition, the user interface (UI) allows the creation of dynamic reports, enabling comparative analysis between different regions and the Petition 870250005536, dated 01 / 23 / 2025, page 21 / 33 16 / 18 visualization of variations over time.

[48] ​​The method (M) comprises the following steps: a. Extract related public and proprietary data related to the dynamism and economic potential index of a region through a data extraction and integration module (MEID); b. Store the data collected in step “a” in a database (DB); c. Calculate a sub-indicator of economic dynamism for each variable by comparing the current monthly value of the variable in the region with the value of the variable in relation to a previously defined date, using a statistical processing and analysis module (MPAE) through the equation Current Value (month).100 . Previous value d. Normalize the variables related to the IPE, in order to guarantee comparability between them, regardless of their units and magnitudes, using the statistical processing and analysis module (MPAE), through the equation x·mmU).ιθθ ; m max(X)-mln(X) e. Using the statistical processing and analysis module (MPAE), generate an eigenvector and eigenvalue matrix using the multivariate technique Principal Component Analysis (PCA), from the values ​​obtained in steps “c” and “d”; f. Obtain the weighted values ​​of each variable using the matrix generated in step “e”; g. Using the statistical processing and analysis module (MPAE), obtain a final IDE calculated by the weighted sum of all sub-indicators, based on the weights determined in step “f” and Petition 870250005536, dated 01 / 23 / 2025, page 22 / 33 17 / 18 in the sub-indicators of stage “c” through the equation IDE = £(Variable weight x Sub-indicator; h. Using the statistical processing and analysis module (MPAE), obtain a final IPE calculated by the weighted sum of all sub-indicators, based on the weights determined in step “f” and the sub-indicators from step “d” using the equation / PF = £(Variable weight x Sub-indicator; i. Present the result in a user interface (UI).

[42] Thus, the present invention stands out by providing an integrated solution for evaluating and analyzing the dynamism and economic performance of different regions. The system (S) and method (M) together, by capturing up-to-date data and processing it efficiently, allow the generation of metrics such as FDI and IPE, which are clear, accessible and capable of accurately reflecting the dynamics of economic adaptation and investment attraction conditions.

[43] Among the advantages provided by the system (S) and method (M), the following stand out: the automation of data processing, the reduction of time lag in analyses, and the ability to integrate structural and conjunctural information into a single process. In addition, the system (S) together with the method (M) allows visualization of the magnitude of the region's economic dynamism, making it possible to assess its suitability for economic growth. This is especially relevant because a highly economically developed region is not necessarily dynamic, and may be saturated and have little growth potential. Thus, these characteristics allow the identification of trends and opportunities for economic growth, as well as signaling risks with greater anticipation and reliability. Petition 870250005536, dated 01 / 23 / 2025, page 23 / 33 18 / 18

[44] The man of the art will readily perceive, from the description and the represented drawings, various ways of carrying out the innovation without departing from the scope of the appended claims. Petition 870250005536, dated 01 / 23 / 2025, p. 24 / 33

Claims

1 / 4 CLAIMS 1 - “SYSTEM FOR OBTAINING A DYNAMISM INDICATOR AND AN ECONOMIC POTENTIAL INDICATOR FOR A REGION” said system (S) having a database (DB), a data extraction and integration module (MEID), a statistical processing and analysis module (MPAE) and a user interface (UI), characterized in that the database (DB) is structured in a relational model; the data extraction and integration module (MEID) stores economic data in tables, segmented by variables and geographic levels (municipal and state); the data extraction and integration module (MEID) collects, normalizes and inserts data into the database (DB); The statistical processing and analysis module (MPAE) will perform real-time calculations and consolidate indicators of economic dynamism (IDE) and economic potential (IPE), and will include mathematical algorithms and statistical analysis techniques;The user interface (UI) will present the results of the Economic Dynamism Indicator (IDE) and the Economic Potential Indicator (IPE), and create dynamic reports for comparative analysis between different regions and visualization of temporal variations. 2 - “SYSTEM FOR OBTAINING A DYNAMISM INDICATOR AND AN ECONOMIC POTENTIAL INDICATOR FOR A REGION” according to claim 1, characterized by the fact that the database (DB) stores data from multiple public and private sources, organized by unique identifiers of the regions analyzed. 3 - “SYSTEM FOR OBTAINING A DYNAMISM INDICATOR AND AN ECONOMIC POTENTIAL INDICATOR FOR A REGION” according to Petition 870250005536, dated 01 / 23 / 2025, page 25 / 33 2 / 4 with claim 1, characterized by the fact that the database (DB) is continuously inserted and periodically updated through the data extraction and integration module (MEID). 4 - “SYSTEM FOR OBTAINING A DYNAMISM INDICATOR AND AN ECONOMIC POTENTIAL INDICATOR FOR A REGION” according to claim 1, characterized in that the database (DB) comprises indexes in the most frequently consulted fields, such as region identifiers and key variables of specific indicators. 5 - “SYSTEM FOR OBTAINING A DYNAMISM INDICATOR AND AN ECONOMIC POTENTIAL INDICATOR FOR A REGION” according to claim 1, characterized in that the data extraction and integration module (MEID) comprises APIs and structured queries to interact with the database (DB) and perform automatic consistency checks before data insertion or updating. 6 - “SYSTEM FOR OBTAINING A DYNAMISM INDICATOR AND AN ECONOMIC POTENTIAL INDICATOR FOR A REGION” according to claim 1, characterized by the fact that the data extraction and integration module (MEID) performs consistency checks before inserting or updating data; identifies duplicate or inconsistent records; and applies automatic corrections. 7 - “SYSTEM FOR OBTAINING A DYNAMISM INDICATOR AND AN ECONOMIC POTENTIAL INDICATOR FOR A REGION” according to claim 1, characterized in that the statistical processing and analysis module (MPAE) performs weighted calculations of the IDE and IPE indicators based on sub-indicators obtained from specific variables, applying weights derived from the Principal Component Analysis (PCA) technique. 8 - “SYSTEM FOR OBTAINING A DYNAMISM INDICATOR AND AN ECONOMIC POTENTIAL INDICATOR OF A REGION” according to claim 1, characterized in that the user interface (UI) is for displaying graphs and reports with temporal comparisons and between regions. 9 - “METHOD FOR OBTAINING A DYNAMISM INDICATOR AND AN ECONOMIC POTENTIAL INDICATOR FOR A REGION” said method (M) being characterized by comprising the following steps: a. Extracting related public and proprietary data related to the dynamism and economic potential index of a region through a data extraction and integration module (MEID); b. Storing the data collected in step “a” in a database (DB); c. Calculating a sub-indicator of economic dynamism for each variable by comparing the current monthly value of the variable in the region with the value of the variable in relation to a previously defined date, through a statistical processing and analysis module (MPAE) using the equation Current Value (month) .100 . Previous Value d.Normalize the variables related to the IPE, in order to guarantee comparability between them, regardless of their units and magnitudes, using the statistical processing and analysis module (MPAE), through the equation x· mmU) .100 . m max(X)-mln(X) e. Using the statistical processing and analysis module (MPAE), generate an eigenvector and eigenvalue matrix using the multivariate technique Principal Component Analysis (PCA), from the values ​​obtained in steps “c” and “d”; f. Obtain the weighted values ​​of each variable using the matrix generated in step “e”; g. Using the statistical processing and analysis module (MPAE), obtain a final IDE calculated by the weighted sum of all the Petition 870250005536, dated 01 / 23 / 2025, page. 27 / 33 4 / 4 sub-indicators, based on the weights determined in step “f” and the sub-indicators of step “c” through the equation IDE = £(Weight of the variable x Sub-indicator; h.Using the statistical processing and analysis module (MPAE), obtain a final IPE calculated by the weighted sum of all sub-indicators, based on the weights determined in step “f” and the sub-indicators of step “d” through the equation / PF = £(Variable weight x Sub-indicator; i. Present the result in a user interface (UI). Petition 870250005536, dated 01 / 23 / 2025, page 28 / 33.

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