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Public Data Analytics: Community Problem Solving

VOLUME 2 of the OCDex Public Data Analytics Series

Data science and analytics has demonstrated its power in informing decision-making and problem-solving. Data can reveal trends and insights that would have otherwise been obscured. It can give decision-makers key information needed to craft effective and optimal solutions to organizational problems. It can help predict potential bottlenecks and challenges, so that organizations may come prepared when it happens. Data science and analytics is a sought out
skill in the digital age.

The Covid-19 pandemic and its resulting limitations on mobility has forced many transactions and communications to migrate from the physical space to the digital space. This sudden global digitalization resulted in an increase in data produced and a subsequent increase in the potential game-changing insights that these data may be hiding.


While many in the private sector have been seen leveraging the power of data for business insights and maximization of revenue, the public sector is yet to catch up in terms of digitalization and data utilization, especially in developing countries. The power of data would especially help communities and local governments in coming up with efficient, effective, and inclusive policies and solutions to problems.


The aim of the 2022 OCDex project run is to bring data scientists and analysts together, and demonstrate how analysis of government data can be used to help solve problems in local communities. The project aims to demonstrate how it can help inform local policymaking and project planning, and how citizens and researchers can participate and help their respective local government units in overcoming community challenges hand-in-hand. This handbook hopes to convince local governments and authorities to invest in good data housekeeping and integrate data science and analytics into their decision-making.


This handbook features how academics and data enthusiasts used public data to help inform solutions to various community problems such as healthcare, inclusivity, and accessibility for persons with disabilities, fairness, and transparency in public procurement, and ensuring enough supply of utilities. Lastly, this handbook presents a replicable model of cooperation between local governments and their local researchers and data enthusiasts toward the effective use of data science and analytics for community building.

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For more information, questions, suggestions, and submissions, please e-mail learning@layertechlab.com

Reflections: How the Academe can Leverage Data and Analytics to Help LGUs

Author: Mia Amor C. Tinam-isan (Mindanao State University – Iligan Institute of Technology)

Data analytics has been the talk of the town. It finds its way across agencies (government or private), businesses, or various institutions in discovering valuable information about the existing and overwhelming amount of data. Data in these times exponentially increase and extracting valuable information from these data is essential. Information from the analysis can be used for decision making, coming up with a good marketing strategy, or even in establishing policies and guidelines from a broad test base. However, local government units per se in various parts of the Philippines, have not yet maximized the impact of data analytics. Varying factors might contribute to this such as unorganized data and non-computerized processes.

As part of the academic community, we can implement programs, webinars, and workshops that will empower our LGU to exploit their available data. It is imperative to partner with NGAs, and LGUs, and develop a working strategy for local government digitization. We can propose a simple initiative and start from the automation of LGU processes to organize and ensure the quality and the integrity of data for analysis. We can also venture into partnering with private agencies such as OCDex which has experienced in partnering with LGUs and had already developed numerous government data analytics applications. As of the moment, the College is continuously having a dialog with different offices of LGU-Iligan in crafting MOA for the digitization of the city government.

This article is the author’s reflection on the insight gained from the recently concluded OCDex 2022 Public Data Analytics Fellowship Trainings.

For more information about the article, please reach out to the author: miaamor.catindig@g.msuiit.edu.ph or Layertech labs support at learning@layertechlab.com

Educated Spending: An Insight on SUCs Utilization of MOOE

Author: Sittie NB Pasandalanb (Mindanao State University – Iligan Institute of Technology)

Despite the reality of reduced budgets of the education sector especially of state universities and colleges (SUCs), institutions of learning have a good share of the annual National Expenditure Plan of the government. Generally, appropriations of SUCs can be categorized into three: Personnel Services (PS), Capital Outlay (CO), and Maintenance and Other Operating Expenses (MOOE). Of these three categories, the MOOE should be of great interest as this gives insight to how a higher education institution (HEI) of the government utilizes government funds, ergo tax payers’ money.

MOOE is funds to be used for necessities (such as electricity and water) and for activities. HEIs are institutions expected to promote conservation of energy, it is worthy to look into the electricity and water bills of HEIs as these would speak of how HEIs are walking the talk. As employees of HEIs are taxpayers themselves, spending for activities should be examined to determine judiciousness in utilizing government funds, ergo taxpayers’ money. 

There is the disconnect between spending from one’s own pocket to spending from another’s pocket. Most likely, one finds it easier to spend from another’s pocket than one’s own. This begs the question of whether employees in HEIs (taxpayers in HEIs) can connect with the funds allocated to HEIs as taxes they have paid to the government, ergo their money. 

In the case of MSU-IIT, the MOOE for 2021 amounted to Php 297,321,732.24 (from Php 222,402,237.68 in 2020). The question on how the amount was utilized and if the expenses are necessities or mere expenditures to utilize funds allocated by the government begs to be answered. 

This article is a reflection on the OCDex 2022 Fellowship Programme for Researchers

For more information about the article, please reach out to the author: sittie.pasandalan@g.msuiit.edu.ph or Layertech labs support at learning@layertechlab.com

Predicting Public Procurement Irregularities in the COVID-19 Response of Local Government Units (LGUs) in the Philippines

Authors: Barajas, J.R., Aspra, N., Gealone, P.J., Lucero, A., Padua, O., Ramos, M.

Motivated by ensuring transparency, fairness, and efficiency in public procurement at the time of the COVID-19 pandemic, a team of engineers and faculty from Bicol Region (Region 5) in the Philippines collected, digitized, and analyzed public procurement data to inform the COVID-19 response of select Local Governments in the country.

Highlights of the Report:

• On average, only 2 out of 10 LGU contracts have been awarded in
2020.

• For every Php1 spent, approximately Php1 remains unspent in
the procurement of goods and services made by LGUs.

• A total of Php481 billion were distributed across all LGUs in the country
for 2020 but only 10% of this budget was allocated for the procurement
of drugs and medicines. 40% of this budget went to construction
projects.

• Excluding LGU contracts not posted in the PhilGEPS website, only Php10
billion (2.16% of the total LGU budget) was allocated for COVID-
19 related contracts.

• An equivalent amount of Php720 million was potentially lost from 786
LGU contracts flagged as irregular.

• Audit findings for LGUs were primarily centered on directing accountable
officers to comply to documentary requirements mandated by existing
circulars, memorandums, and Philippine laws.

• A logistic regression model with an accuracy of 91.29% was developed
to identify contracts that are potentially irregular.

Short Summary of the Report:

From examination of 296,220 local government unit contracts, this project was able to develop a logistic regression model capable of predicting potentially irregular LGU contracts posted on the PhilGEPS website for the fiscal year 2020 at an accuracy of 91.29%. Validation of the model using metrics derived from the confusion matrix revealed that the developed model had a recall score of 1.0 and a precision score of 0.029. While the precision of the model may be
low, the high recall score is deemed more important in this use-case since it would be more costly for an LGU to miss out on irregular contracts. Overall, the developed prediction model is seen to be highly beneficial as a decision
support tool for LGUs since this could potentially narrow down the number of awarded LGU contracts to be legally reviewed resulting in a faster turnover of review cycles conducted within a given fiscal year.

The team’s collected datasets are available for download in the OCDex open data portal, attribution to the authors and contributors is required for use.

DOWNLOAD FULL REPORT HERE:

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Are you interested in this report? Do reach out to us at learning@layertechlab.com so that we can directly connect you with the authors, as well as the documentations they submitted.

Tracking Population Growth and its effects to Philippines’ Carrying Capacity in selected indicators

By: Team Data Hoarders

“TEAM DATA HOARDERS” Explored the Population Growth and its Effects to Philippines’ Carrying Capacity in Selected Indicators. They gave recommendations and insights both for data scientists to explore further on, and the government!

The presentation of Team Data Hoarders for the Datathon2020: Evidence-Based Lobby and Advocacy with Data Analytics, on February 22-23, 2020 held at Bicol University CSIT.

Please note that this presentation may be updated, modified, substantiated, in the following days. Please check this page for updates.

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This document is CC3.0 – Use with proper attribution
(https://creativecommons.org/licenses/by/3.0/ )

How Much is the Budget for HIV treatment in the Philippines?

By Team Data Miners

“TEAM DATA MINERS” Explores budget trend of HIV-related commodities, versus the number of newly diagnosed cases of HIV per month from 2017 to 2019, as well as insights, comments and recommendations!

This is Team Data Miners’ presentation at the Datathon2020: Evidence-Based Lobby and Advocacy competition and workshop, held at Bicol University College of Science, February 22-23, 2020.

low this page for more updates, as this study gets further substantiated. The contents of this page may be modified, updated, and further substantiated.

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This document is CC3.0 – Use with proper attribution
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Roles and Issues of the Sangguniang Kabataan

By Team: BATAG

“TEAM BATAG” or Team “Builders And Technology Advocates Group”, our young team from CSO and and Computer Science and IT Students,
presents the current issues that the youth are facing, and measures youth participation in governance.

trivia: ‘Batag’ is a bicol word for ‘banana’ pun so not intended! 😀

This is Team BATAG’s presentation at the Datathon2020: Evidence-Based Lobby and Advocacy competition and workshop, held at Bicol University College of Science, February 22-23, 2020.

Follow this page for more updates, as this study gets further substantiated. The contents of this page may be modified, updated, and further substantiated.

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HIV/AIDS in the Philippines

By Team Anata

“TEAM ANATA”, a mix of advocates and Computer Science and IT Students,
presents and discusses how they used data to identify key issues in HIV/AIDS in the Philippines, its urgency, and comes up with data-backed recommendations for government budgeting for HIV-related kits, trainings, and anti-retro-viral drugs.

This is Team Anata’s presentation at the Datathon2020: Evidence-Based Lobby and Advocacy competition and workshop, held at Bicol University College of Science, February 22-23, 2020.

Follow this page for more updates, as this study gets further substantiated. The contents of this page may be modified, updated, and further substantiated.

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This document is CC3.0 – Use with proper attribution
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