S. B. Rabbani, A. A. Ali and M. Zaber, “Does Electric Prepaid Meters Decrease Payment Delinquency? Evidence from Data Centric Analysis of Electricity Consumption in Dhaka, Bangladesh,” in IEEE Region 10 Symposium (TENSYMP), Dhaka, 2020.
Prepaid electricity meters are favored by power distribution companies to improve revenue collection, debt prevention and ensure customer satisfaction. In this paper, we investigate changes in bill payments pattern of customers in Dhaka, Bangladesh who had switched from post to pre-payment meters. We collected postpaid bill and prepaid recharge payment data over there years. We specifically look into payment behavior of residential household customers to understand the effect of prepaid meters on payment delinquency. Analysis of postpaid bill indicate that at least 32% households skipped paying one or more bills over a period of one year. Households consuming 100-300 KWH accounts for almost 51% of the total delinquent households. After prepaid switch, approximately 46% of all households are making prepayments every month. 52% of households who skip paying bill and 45% of households who pay bill after due date, at least once, are now recharging every month. Overall the technological intervention shows considerable reduction of retail debts and a behavioral shift towards on time payment.
Moinul I Zaber, Amin A Ali, Farhad Bhyiyan, Md Abu Sayed, Samiul Islam , Nibras Rakib. Harnessing Data for Policy Good: A Data Driven Exploratory Analysis of Power Consumption of Dhaka, Bangladesh
This report is a case study of electricity consumption pattern of Dhaka city dwellers of Bangladesh. Electricity is a vital resource for country’s development. Efficient management of electricity production, distribution and supply is vital for not only the economy but also for the environment. Bangladesh as a developing country needs well managed electricity-energy eco system to ensure continuous economic development. The prerequisite of a well-managed electricity eco system, are policies driven by robust knowledge of the demand and supply needs. Bangladesh lacks data driven research that sheds light on various aspects of electricity eco system and may help the policy makers. The gap between the need for evidenced based research and policy initiatives is ever increasing. This report aims at reducing the gap.
In order to reduce the gap, the report aims to harness uniquely built dataset based on monthly billing data and hourly supply data at the household level. The underlying assumption of the report regarding better management of public utility requires fulfillment of prerequisites: a. understanding the consumers and their economic health, b. understanding how regulatory decisions impact their behavior and c. forecasting short, mid and long-term demand of the public utility. The uniquely built dataset is examined via various statistical and computational tools to help policy makers gain more insight on these prerequisites.
By analyzing the consumption pattern, we try to indirectly understand the economic health of the households. The report shows, it is possible to use electricity consumption data as a heterogeneous data source for better understanding of country’s economy. The approach can be an effective measure of household level economic condition, especially because various other direct methods such as survey may be expensive and time consuming. This research finds that the number of electricity consumers are rapidly increasing over the year. However, most of them belong to specific groups that have predictable patterns. High consumption users belong to mainly in two specific zones where total numbers of users are very low with respect to other zones. It is found that household consumption pattern is impacted by weather fluctuations. The impact of regulatory intervention is measured by scrutinizing impact of price hike at the household level. Electricity price has been increased multiple times in Bangladesh and this paper is intended to find out the impact of the price hike and impact of weather change on the residential users. Preliminary research indicates that short term impact of price hike on electricity consumption is very small (inelastic). However, impact varies among tariff groups. The research also proposes a computational approach to forecast short term electricity demand at household level.
This report is seminal in the context of Bangladesh energy eco system because of the use of large dataset and pertinent computational tools used to find the policy relevant outcomes.
Islam, S., Ali, A. A., & Zaber, M. A Smart Grid Prerequisite: Survey on Electricity Demand Forecasting Models and Scope Analysis of Demand Forecasting in Bangladesh. The fifth IEEE Region 10 (Asia Pacific) Humanitarian Technology Conference (R10HTC) 2017, December 21-23, 2017, Dhaka, Bangladesh.
Electricity supply via smart grid mechanism is gaining importance in many country’s priority lists. A detailed study on electricity forecasting is required to ensure a smooth transition to the smart grid. Forecasting is evident to ensure better management of generation plants, supply grids, and the transmission system. This article focuses on demand forecasting study as a preparation of smart grid, presents a technical survey/review of several forecasting methods which have been done earlier. This paper also conducts a study highlighting the forecasting scenario, performs scope analysis from developing countries’ context and presents analytical results of a short-term forecasting. As a candidate, these have been discussed on the basis of electricity load data of Dhaka, the capital city of Bangladesh; one of the world’s most highly populated city. Electricity distribution has been handled in Dhaka city by two companies, DESCO and DPDC. This article discusses on few average sized DESCO zones of its total 16 and emphasized more on Shah Ali zone. Two selective feature based forecasting methods have been also proposed and results have been shown to support that forecasting will help to see the unseen.
Education and ICT
Sayed, M. A., & Zaber, M. I. (2020). Just-in-Time Educational Aid to Deliver Instant Help for Students in Developing Countries. Recent Trends in Information Technology and its Application, 3(3).
We recommend an ICT-based education that addresses the student’s needs instantly. Student may need additional assistance when they self-study. In developing countries, most families do not have educated parents, lack economic power to afford external educational assistance with the exception of availing free schools. An affordable ICT solution via mobile to provide educational support just in time will work wonders in order to sustain the student’s motivation. The proposed ICT tool, JINTE guidance system for students, will help maintain the motivation of students to self-study. This, along with Massive Open Online Courses (MOOC) and the traditional educational system, can be a supplementary educational portal. In terms of the system’s efficacy, our studies show promising outcomes. At the same time, by tracking the trends of the questions posed by the students, educators can discover the issues related to topics they teach. The platform can be used as a national educational data collection tool and thorough analysis and processing would make these data can be very useful for policy makers to launch a sustainable long-term plan. The knowledge obtained can help to involve students in the process of learning from distant locations. These ideas of offering educational guidelines can be taken into account by countries who wish to use ICT as an important educational tool.
Sayed, Md Abu, Zaber, Moinul, Ali, Amin Ahsan and Mosharaf, Parvez. Enumerating the obstacles of accelerating the use of digital classroom: Lessons from Bangladesh (August 30, 2017), Communication Policy Research South 2017, Yangon, Myanmar.
The Government of Bangladesh has been pushing the use of ICT (Information and Communication Technology) as an educational tool in the classrooms of the country. Authorities have focused on infrastructure- network, computers, even online content repositories. However, even after constant effort, digital inclusion in the classrooms has not been satisfactory. This paper research endeavors to find the reasons behind this phenomenon. Specifically, we try to find out whether or not the educators are willing to use the new technology, and if not why. We have surveyed educators at a number of institutions around the country and found that just having access to technology does not use one to use that as an educational tool. We have also found that the educators do understand the need for digital inclusion. However, proper technological support in the schools deters them from using the technologies. We also find that in-school third party help in content development than downloading contents from other sources are more appreciated.
A novel use of ICT to deliver just-in-time educational aid to help better learning for the school students in developing countries.Communication Policy Research South 2016, Yangon, Myanmar.
The biggest challenge in the education sector of the developing countries is the high student to teacher ratio. As a result, often times, it is not possible to guide each student equally that it creates a gap among students in terms of understanding of their lessons- some understand, some loses motivation to learn as they miss the rationale behind. This paper proposes a novel model for ICT based education that caters to the need of the student as and when they require. Students as they self-study may stumble into learning challenges that might require external aid.
A. Zaman, S.B. Rabbani, R.R. Haque, M. Zaber. Seasonal, Temporal and Spatial Variation of Particulate Matter Concentration in Bangladesh: A Longitudinal Analysis. TenSYMP 2021
Particulate matters having diameters of 2.5 micrometers or less (PM2.5) have been linked with life threatening health issues worldwide. Data centric approach to ascertain the patterns in the propagation of PM2.5 materials in the atmosphere of a region can help policy makers take informed decisions to take proper action. In this paper, we analyze and identify seasonal, hourly, and regional patterns of PM2.5 propagation in Bangladesh from 2017 to 2020 using the Berkeley Earth dataset. We observe that the concentration of PM2.5 particles has a nationwide median value of about 50 μgm -3 , which is unhealthy for sensitive individuals. The concentration varies seasonally and diurnally. We observe that the concentrations of PM2.5 in the air is around five times more in winter than in summer. The mean PM2.5 concentration inside Dhaka is significantly worse around 70 μgm -3 , which is 1.25 times than the average concentration throughout Bangladesh. We also observe average concentration dropped during the covid-19 pandemic due to lockdown. Using cross correlation analysis, we observed how spikes in PM2.5 concentration levels in one zone may correspond with peaked concentrations in a different zone a few hours later, indicating that air currents may cause the particles to move in certain directions. Our exploratory analysis serves as the first cross-country data centric study of the state and propagation patterns of PM2.5 particles within Bangladesh and our findings can serve as foundation for further research on the topic.
Cheng, Q., Rahman, A. K. M., Sarker, A., Nayem, A. B. S., Paul, O., Ali, A. A., … & Zaber, M. (2020). Deep-learning coupled with novel classification method to classify the urban environment of the developing world. arXiv preprint arXiv:2011.12847.
Rapid globalization and the interdependence of humanity that engender tremendous in-flow of human migration towards the urban spaces. With advent of high definition satellite images, high resolution data, computational methods such as deep neural network, capable hardware; urban planning is seeing a paradigm shift. Legacy data on urban environments are now being complemented with high-volume, high-frequency data. In this paper we propose a novel classification method that is readily usable for machine analysis and show applicability of the methodology on a developing world setting. The state-of-the-art is mostly dominated by classification of building structures, building types etc. and largely represents the developed world which are insufficient for developing countries such as Bangladesh where the surrounding is crucial for the classification. Moreover, the traditional methods propose small-scale classifications, which give limited information with poor scalability and are slow to compute. We categorize the urban area in terms of informal and formal spaces taking the surroundings into account. 50 km x 50 km Google Earth image of Dhaka, Bangladesh was visually annotated and categorized by an expert. The classification is based broadly on two dimensions: urbanization and the architectural form of urban environment. Consequently, the urban space is divided into four classes: 1) highly informal; 2) moderately informal; 3) moderately formal; and 4) highly formal areas. In total 16 sub-classes were identified. For semantic segmentation, Google’s DeeplabV3+ model was used which increases the field of view of the filters to incorporate larger context. Image encompassing 70% of the urban space was used for training and the remaining 30% was used for testing and validation. The model is able to segment with 75% accuracy and 60% Mean IoU.
Md. Rakin Sarder Arko, Mohammed Raihanul Bashar, Amin A Ali, Moinul Zaber, Md Abu Sayed. A Review on The Impact of Satellite Imagery in Urban Policy Planning
This paper reviews some of the works done in the field of satellite image sensing-based policy planning, and how they can be fitted into urban settings for faster and effective decision making. Rapid urbanization due to population growth and migration in the past decades had a consequence on the overall planning of the urban areas. Policy planning schemes are now more developing than the past. Although the demand for new and stable policies regarding in the urban is increasing, proper evidence based on valid studies still needs to keep pace with the policy planning. Satellite remote sensing data can provide enough evidence at a large scale to come to a policy interpretation. Impact analysis of how different researchers in this sector have created a positive impact on different policy planning has been conducted in this research.
Md Maksudur Rahman, S M Ariful Hoque, Moinul Islam Zaber. Understanding Real Time Traffic Characteristics of Urban Zones Using GPS Data: A Computational Study on Dhaka City
Dhaka, the capital of Bangladesh, is badly affected by traffic congestion and the situation is deteriorating day by day. In order to minimize traffic congestion, we need to investigate traffic pattern of this mega city and identify the factors that are responsible for high traffic intensity. However, only a few researches have been conducted to find out the reasons behind Dhaka city’s acute traffic congestion. Most of those researches were survey based, thus, prone to perception and human intervention. In this research, we have used Global Positioning System (GPS) data in order to analyze Dhaka city’s traffic pattern. We have considered 13 DPZ (Detailed Planning Zone) zones proposed by RAJUK, the city development authority of Bangladesh for traffic modeling. Computational methods are employed to find the similarities among clusters. We consider both intra zonal road segments in a zone and inter zonal road segments that connect two or more different zones. Land use pattern is also investigated to find out the traffic variability among zones. A graph is produced to visualize overall traffic scenario of Dhaka city, where the traffic intensity is measured by GPS dataset.
Rahman, M. M., Shuvo, M.M.M., Zaber, M. I., & Ali, A. A. Traffic Pattern Analysis from GPS Data: A Case Study of Dhaka City. 2018 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), 16 th -17 th March, 2018, Bangalore, India.
Traffic congestion is one of the most alarming problems of Dhaka, the capital of Bangladesh. However, not much work had been done on traffic pattern modeling for Dhaka city. In this paper, we analyze traffic intensity pattern computed from GPS data. The data contains traffic intensity information for 11,769 road segments over 15 days. We analyze the impact of
marketplaces, number of road intersections, and having rickshaw free roads on the traffic intensity. In order to analyze the traffic pattern at a macroscopic level, we analyze the traffic pattern of the 13 zones of the city proposed by RAJUK, the authority responsible for the development of Dhaka. For each zone, we investigate the impact of a number of different factors, e.g., land use, number of bus routes, number of road intersections, on the traffic intensity.
Sayed, M. A., Rahman, M. M., Zaber, M. I., & Ali, A. A. Understanding Dhaka City Traffic Intensity and Traffic Expansion Using Gravity Model, 20th International Conference of Computer and Information Technology (ICCIT), 22-24 December 2017, Dhaka, Bangladesh.
Analysis of traffic pattern recognition and traffic congestion expansion in real time is one of the exciting and challenging tasks which help the government to build a robust and sustainable traffic management system especially in a densely populated city like Dhaka. In this paper, we analyze the traffic intensity for small areas which are also known as junction points or corridors. We describe Dhaka city traffic expansion from a congestion point by using gravity model. However, we process real-time traffic data of Dhaka city rather than depend on survey and interview. We exactly show that traffic expansion of Dhaka city exactly follows gravity model. Expansion of traffic from a congestion point spreads out rapidly to its neighbor and impact of congested point decreases as the distance increases from that congested point. This analysis will help the government making a planned urbanized Dhaka city in order to reduce traffic jam.
F. Tabassum, H. Islam, A. A. Ali and M. Zaber, “A Complex Network Analysis of Inland Waterways Port Connectivity of Bangladesh,” in IEEE Region 10 Symposium (TENSYMP), Dhaka, 2020.
A well-structured inland waterways system should help Bangladesh fulfill SDG goals. In this study, we employ complex network analysis methods to analyze the river-port network of the country. We ascertain different types of ports based on their importance and placement in the connectivity network. Data regarding port location, vessel routes, and schedules were collected from governmental resources. Using the data, a connectivity network was built for further analysis. Different measures of network analysis are used to categorize the ports and the network model has been identified. These categories should help transportation planners and policymakers to better design the inland waterways network of Bangladesh.
Chaki, Dipankar and Zaber, Moinul and Ali, Amin Ahsan, Understanding Complex Social Network of Government Officials in Decision Making (August 30, 2017). Communication Policy Research South 2017, Yangon, Myanmar. Available at SSRN: https://ssrn.com/abstract=3041
In the era of technology, social media provides the instruments to expand this network even drastically further. A social media platform like Facebook group provides an indispensable instrument to connect a node with every other node which is the essence of an efficient organization. We have seen the evidence of efficiency in the in solving problems and to disseminate innovative ideas by analyzing “Public Service Innovation Bangladesh” group. This paper shows our analysis on understanding Complex Social Network of Government Officials in Decision Making.
Chaki, D., A. Das, and M. I. Zaber. “A comparison of three discrete methods for classification of heart disease data.” Bangladesh Journal of Scientific and Industrial Research 50.4 (2015): 293-296.
The classification of heart disease patients is of great importance in cardiovascular disease diagnosis. Numerous data mining techniques have been used so far by the researchers to aid health care professionals in the diagnosis of heart disease. For this task, many algorithms have been proposed in the previous few years. In this paper, we have studied different supervised machine learning techniques for classification of heart disease data and have performed a procedural comparison of these. We have used the C4.5 decision tree classifier, a naïve Bayes classifier, and a Support Vector Machine (SVM) classifier over a large set of heart disease data. The data used in this study is the Cleveland Clinic Foundation Heart Disease Data Set available at UCI Machine Learning Repository. We have found that SVM outperformed both naïve Bayes and C4.5 classifier, giving the best accuracy rate of correctly classifying the highest number of instances. We have also found naïve Bayes classifier achieved a competitive performance through the assumption of normality of the data is strongly violated.
Society and Economy
M. Wahed, R. A. Rizvee, R. R. Haque, A. M. Ali, M. Zaber and A. A. Ali, “What Can Nighttime Lights Tell Us about Bangladesh?,” in IEEE Region 10 Symposium (TENSYMP), Dhaka, 2020.
Analyzing the state and growth of various socio-economic indicators is essential for effective developmental planning at a sub-national level. However, in many cases, data regarding such indicators are not publicly available and/or hard to collect. In many other cases, the available data may not be recent enough. In contrast, satellite data is both readily available and up-to-date. In recent times, various studies have been conducted to use different types of satellite data as a proxy to determine the condition of socio-economic indicators in different places around the world. In this paper, we study the efficacy of one such data source, nighttime lights (NTL), for monitoring factors related to sustainable development in the context of Bangladesh.