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Under the SDG of Water Quality, we aim to understand and address issues of water access and equity. This project focuses on analyzing the availability and distribution of quality water sources across different regions and households.
Under the SDG of Water Quality, we aim to understand and address issues of water access and equity. This project focuses on analyzing the availability and distribution of quality water sources across different regions and households.
There is no significant difference in access to quality water among different demographic groups in the Philippines.
There is a significant difference in access to quality water among different demographic groups in the Philippines.
Aggregated data from sources including government reports, NGOs, and international organizations, focusing on water quality, availability, and demographic information.
Compiled a comprehensive database using open-source datasets and publicly available records.
Cleaned and preprocessed the collected data to handle missing values, standardize formats, and ensure consistency.
Applied techniques such as data normalization and transformation to prepare the dataset for analysis
Conducted EDA to uncover patterns, trends, and correlations in the data.
Employed statistical tests to evaluate the significance of observed differences and validate our hypotheses.
Formulated and tested hypotheses regarding the disparities in water access among different demographic groups.
Employed statistical tests to evaluate the significance of observed differences and validate our hypotheses.
Developed predictive models to assess the impact of various factors on water access and quality.
Utilized logistic regression algorithms to identify key predictors of water accessibility disparities.
Analyzed model outcomes to derive insights into the main drivers of water access inequality.
Provided actionable recommendations for policymakers and stakeholders to improve water equity based on the findings.
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Our analysis revealed a p-value of 0.000009 , indicating a statistically significant difference in water access among various demographic groups in the Philippines. This result leads us to reject the null hypothesis and accept the alternative hypothesis , confirming that disparities in water access exist . These findings highlight the need for targeted interventions to address the inequities in water distribution and quality across the population.
Our analysis offers a detailed examination of water access disparities across different settings in the Philippines. Here are the key findings from our Exploratory Data Analysis (EDA) and machine learning models:
Our EDA reveals that in both rural and urban settings , the majority of households have access to improved water sources. The number of households relying solely on unimproved water sources is significantly lower in comparison. However, rural areas still show a higher proportion of households using unimproved water sources compared to urban areas. This indicates that while improved water access is prevalent, there are still notable gaps in rural regions that require attention.
Households in rural or economically underdeveloped regions are more likely to depend on unimproved water sources. This reflects the broader issue of inadequate infrastructure and investment in these areas. Although improved water sources are more common, the persistence of unimproved access points in certain regions highlights the uneven distribution of water quality and accessibility.
Our predictive models identified that geographical location is a crucial factor in determining water access quality. Households situated in rural or underdeveloped regions have a higher likelihood of relying on unimproved water sources. However, our analysis was limited by the lack of detailed data on other significant factors, such as household income levels and the condition of water supply infrastructure , which could provide deeper insights into the disparities.
While our study provides valuable insights, several limitations need to be acknowledged:
The absence of comprehensive data on household income levels and the state of water supply infrastructure limits our ability to fully understand the complexities of water access disparities.
Although we utilized statistical and machine learning techniques, incorporating additional methods could reveal other influential factors that were not captured in our current models.
Our study highlights significant disparities in water access among different demographic groups in the Philippines. By employing data science techniques, we uncovered the socio-economic and geographic factors driving these inequalities. The importance of our study lies in its potential to inform policy and guide interventions aimed at improving water equity .
Prioritize the development and maintenance of water supply infrastructure in underserved regions.
Implement policies and programs that reduce economic barriers to accessing quality water.
Improve the collection and availability of data on water access and quality to support ongoing monitoring and analysis.
Foster partnerships between government, private sector, and communities to address water access challenges collectively.
Water is a fundamental human right and a cornerstone of public health and economic development. Our research underscores the urgent need for targeted actions to address the disparities in water access in the Philippines. By understanding and acting on these issues, we can move closer to a future where every Filipino has reliable access to safe and quality water.
Hello! I'm a 2nd year BS Computer Science at UP Diliman. I like playing video games, watching movies and anime, I also like drawing. I'm interested in making video games and wants to learn more about making video games.
Wassup! Im Seth a 2nd Year student at UP Diliman taking up BS Computer Science. Math was never my forte however data science came off to me as a very interesting subject! Other than that, i love watching kdramas, reading delinquent mangas, and playing video games.
Art is a third-year BS Computer Science major. As an overlapping field with Computer Science, Data Science is something that he has always wanted to do at least once. In his spare time, Art enjoys reading manga and playing video games.