Analytics And Intelligent Systems

NUS ISS AIS Practice Group

Monetary Authority of Singapore Dashboard — November 28, 2016

Monetary Authority of Singapore Dashboard

mas_dashboardThe MAS dashboard focuses mainly on the Singapore nominal effective exchange rate (S$NEER). The Singapore exchange rate policy focuses on trade weighted exchange- Firstly, the value of SGD has to be measured against something. Rather than using one single currency as a benchmark, the MAS uses a basket of currencies of the major trading partners. It is trade weighted such that the currencies of our larger trading partners’ bears more weight and make up a more integral part of the index. This is reviewed periodically and the weight may be changed as out trade pattern changes. This means the SGD is measured against a basket of currencies and not one single currency.

Secondly, unlike most countries which adopt either a float or fixed exchange rate regime, Singapore’s policy is a hybrid of both. The Singapore dollar (SGD) is allowed to float freely, and the MAS will monitor the strength of the currency based on the S$NEER.

MAS focus on three aspects of the band
a) The slope of the band
b) The width of the band
c) The level the band is centred

Within the band, the SGD is subjected to day to day fluctuations just like any other currency. Businesses from overseas can buy or sell SGD to pay local companies for goods required. Institutions can buy or sell the currency to hedge against future movements. Speculators and traders can trade it freely in the Forex market. This freedom is essential for an open economy like ours to flourish.

However, once the SGD is deemed to be trading beyond the band, MAS will step in to buy or sell SGD to maintain its trajectory within the S$NEER band. What MAS is doing is essentially modulating the strength of the SGD against the $SNEER. Hence when the SGD is trading above the upper band the SGD will be sold to bring it below the upper bad and when SGD is trading below the lower band it will be bought to ensure it becomes higher than the lower band.

The band prevents the currency from becoming too strong, making exports more expensive to foreign countries or too weak, which will lead to decreasing purchasing power in the domestic country.

Hence we also have the main trading partners, the trade weights of each partner. The weights are assumed and determine the impact of each of the partners on the SNEER.

We have also visualised the GDP rate, inflation rate and the foreign reserves data, all 3 of which are impacted by the monetary policy.

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Geovisualisation on Human Development Index & Immigration in ArcGIS — November 14, 2016

Geovisualisation on Human Development Index & Immigration in ArcGIS

ArcGIS Visualization on International Immigration and changes in Human Development Index in OECD countries:

About Human Development Index

The Human Development Index (HDI) is a composite statistic of life expectancy, education, and per capita income indicators, which are used to rank countries into four tiers of human development.

Abstract

  • This Geo Visualisation is done to see Human Development Index growth over the years for the several countries from 1980-2010.
  • It is splitted into 5 groups with each group comprising 5 year HDI data.

HDI of 1980-85 :

1980-85

  • We can see that North American Countries and Australia have higher HDI.
  • African Countries and India has lower HDI.

We can see how the HDI is changing over the next 20 years visually below :

HDI changes over the years(1985-2010)

1985-90 :

2

1990-95 :

3

1995-2000 :

4

2000-05 :

5

2005-10 :

6

Summary of changes in words over the 20 years :

Based on the above visualizations,we can conclude that :

  • There is a marginal increase in HDI for India compared to the previous years.
  • Most of the South American countries and Russia has achieved the higher HID over the period of 20 years.
  • Barring few countries, all other African countries still possess a lower HDI. North American nations, European nations and Australia consistently has a higher HDI over the period of 20 years.

 

International Immigration:

ii

 

Effects of Immigration on Human Development Index in OECD countries

  • Thus, from the analysis of the visualizations obtained, it can be visualized that the immigration might be one of the factors contributing to an increase in Human Development Index in most of the OECD countries.
  • We can also infer that majority of immigrants target countries with a high human development index than their countries of origin.

Team Details :

  • Aravind Prabhu(A0148607Y)
  • Bala Gowtham(A0148536X)
  • Herald Nithesh(A0148613E)
  • Vignesh Mohan(A0148543B)

 

Balanced Scorecard : Public Service Division eGovernment Initiative — November 13, 2016

Balanced Scorecard : Public Service Division eGovernment Initiative

Selection of Agency

For our assignment on the Balanced Scorecard, we chose the Public Service Division (PSD) under the Singapore Government Prime Minister’s Office.

slide2

Strategy Map

We looked at PSD’s approach to enable a future-ready Public Service that leverages on technology and data; eventually focusing on eGovernment.   After analysing available eGovernment information online, we attempted to create PSD’s Strategy Map and a identified some possible indicators for this initiative.

We found a lot of data and information across various Singapore Government websites.  Arising from the fact that numerous eGovernment projects are currently ongoing, we were able to create a rich dataset from which to draw our dashboard.  From our four indicators, we developed 12 indicators that would allow our stakeholders to view the progress of eGovernment initiatives from four perspectives:

  • eCitizen
  • eBusiness
  • eGov Capabilities
  • e-Collaboration across agencies

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eGovernment Dashboard

Our initial dashboard was too complex – utilising too many different types of charts, presenting too much information, and inadequately highlighting the important indicators.

To address the above areas for improvement, we reduced our indicators from 12 to seven, simplified the format and the charts used, as well as highlight the more important indicators.  The dashboard was created using Tableau Desktop.

Team Members

Dr Acebedo, Cleta Milagros Libre
Jun Yu, Thomas
Shen Shutao
Sulaiman Ahamed Moosa

Disclaimer:  This project is purely for academic purposes only, in partial fulfillment of the requirements of the NUS ISS MTech Enterprise in Business Analytics – Data Analytics Module. Our work was based on third party information and we do not claim any authority on the subject matter.  Authenticity of third party data was not validated.  The data and resulting dashboard should be treated to contain dummy data for an academic exercise.

Earthquake Patterns and Impact: A Geospatial Perspective —

Earthquake Patterns and Impact: A Geospatial Perspective

A.  Selection of Global Phenomenon

Considering recent series of disasters with global impact, our team was immediately drawn to pursue something related to one of the natural disasters for our assignment on Geospatially Enabled Analysis and Visualisation of a Global Phenomenon.  After exploring the internet for data sources on disasters, and experimenting on various tools (Carto, SPSS, Tableau, and ArcGIS for Desktop), we eventually decided to look at earthquakes – to apply the concepts of geospatial analytics and geovisualisation to analyse earthquake patterns and its impact.

slide3

B.  Geospatial Data Collection

We discovered and downloaded a lot of information and databases on earthquakes, but realised that many of them are not compatible and readily useful.

Using Excel and ArcGIS tools, we did geoprocessing – including cross-validation across sources, conversion of data types, data transformation (geo-referencing, geocoding) as well as creating layers and joining/relating databases.

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C.  Geospatial Analytics & Visualisation

Earthquake Patterns

To analyse patterns, we focused on the geographical location, distribution, and temporal changes of earthquakes.  Using geospatial analytics tools, our analysis enabled us to address the following queries:

  1.    Where do earthquakes usually occur?
  2.    Are they spatially clustered or dispersed?
  3.    Where are earthquakes strongest?
  4.    Are big earthquakes concentrated in specific locations?
  5.    Why do they occur at these locations?
  6.    How has earthquake pattern changed over time?
  7.    How has earthquake pattern changed over space?

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Earthquake Impact

Our study of the impact of earthquakes, using geographical association & interaction tools, allowed us to answer the following queries:

8.     Which countries are more susceptible to big earthquakes?
9.     What are the consequences of earthquakes?
10.   What other hazards occur with earthquakes?
11.    How do we prepare for earthquakes?

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D.  Conclusion

Our study made us realise that earthquakes are ongoing all the time.  Although many are too small to be felt; we need to stay vigilant and know what to do when a big one happens.

Web Map

All maps used in this report were generated by the group.  The map is available at ArcGIS online; it can be downloaded and opened using ArcGIS for Desktop.

Earthquake, an EBAC Geospatial Project 

http://www.arcgis.com/home/item.html?id=8eae0fe44d1e47dca468234da2c9fa54#overview

Team Members

Dr Acebedo, Cleta Milagros Libre
Jun Yu, Thomas
Shen Shutao
Sulaiman Ahamed Moosa

Disclaimer:  The maps are purely for academic purposes only, in partial fulfillment of the requirements of the NUS ISS MTech Enterprise in Business Analytics – Data Analytics Module. Our work was based on third party information and we do not claim any authority on the subject matter.  Authenticity of third party data was not validated.  The maps should be treated to contain dummy data for an academic exercise.

Do Loyalty Programs Really Enhance Behavioural Loyalty? — November 11, 2016

Do Loyalty Programs Really Enhance Behavioural Loyalty?

As part of the Customer Relationship Management elective, our team has come up with a one-page summary of the paper published by Leenheer, J., van Heerde, H.J., Bijmolt, T.H.A., and Smidts, A. (2007)  titled “Do loyalty programs really enhance behavioral loyalty? An empirical analysis accounting for self-selecting members.”.

retention-26-do-loyalty-programs-really-enhance-behavioral-loyalty

Team Members: Chua Jialing Vivien, Liang Jialiang, Prateek Nagaria, Supriyaa

Geo-Visualization of Human Encroachment into Natural Ecosystems —

Geo-Visualization of Human Encroachment into Natural Ecosystems

The growing world population and subsequent diminishing of natural resources is a major concern for humanity.

Gathering data from WWF, NASA and UN, the project aims to show how the increase in population and number of cities has resulted in human encroachment into natural ecosystems.

Using Carto, we plotted 3 categories of regions identified to be of rich biodiversity as well as areas that are still classified as “Wild” and untouched. City location and population data were then overlaid.

In addition to providing geo-visualisations of cities and rich ecological regions, we also managed to use Carto’s Spacial Join Tool to extract and plot cities which lie within the regions to identify places that could potentially endanger biodiversity of the planet.

Our visualisations showed the following:

1. The cities with population more than 1 million (as red dots) overlaid on the ecological regions [change over time]

Picture1.png

2. The cities with current population of more than 300,000 (as black dots) overlaid on the ecological regions, with bubble size representing population.

Picture2.png

3. The overlap of cities with ecoregions with intensity of Purple dots representing the number of ecoregions a city lies in.

Picture3.png

The dashboard has been posted for public viewing and can be found at HERE.

Team Members: Chua Jialing Vivien, Prateek Nagaria, Ravi Malhotra, Supriyaa & Tian Ziqiang

 

GLOBAL TERRORIST ATTACKS — November 9, 2016

GLOBAL TERRORIST ATTACKS

INTRODUCTION

Terrorism has been around since the beginning of time, and has caused empires to rise, fall, and allowed people to gain power. It is a growing problem in this unstable world. A simple act of terrorism can cause tensions between two countries, as seen with Israel and Palestine’s conflicts due to religious beliefs and territory disputes. Among the various potential threats are wars with neighboring countries, missile attacks on cities, biological and chemical terrorism, suicide bombings, and hostage taking.

Owing to the growing global terrorist activities, we wanted to explore how analytics could help in understanding the various patterns and trends in these terrorist activities.

To compare or quantify the effect of terrorism on different countries, we tried to bring in the below-mentioned indices that directly/indirectly affect terrorist activities:

  1. Global Terrorism Index – which provides a comprehensive summary of the key global trends and patterns in terrorist activities for each country.
  2. Human Development Index – which is a composite statistic of life expectancy, education, and per capita income indicators
  3. Political Stability and Absence of Violence Rank – measures perceptions of the likelihood that the government will be destabilized or overthrown by unconstitutional or violent means
  4. Percentage share of Military expenses in a country’s GDP – this is representative of how seriously a country takes its security and how much it’s willing to spend to protect their nation

ABOUT THE DATA:

For our analysis, we took data from Global Terrorism Database which is an open-source database that contains information on global terrorist activities between 2001 and 2015. For each attack, information is available on the date and location of the incident, the weapons used and the target, the number of casualties, and the group, if identifiable.

GEO-REFERENCING:

The latitudes and longitudes which were a part of the original data source and the same was used for tagging cities to represent the number of casualties while the country names were used to tag the various indices.

TOOLS USED:

  • Microsoft Excel : Used to perform initial exploratory analysis and for data preparation for subsequent Geo Visualization.
  • Tableau: To develop insights and analytics on the top 10 ranked countries by GTI scores.
  • Carto : Used to perform geo visualization on this dataset on the global level.

GEO ANALYSIS – TABLEAU

Tableau Dashboard Link

We analyzed patterns using the Top 10 GTI rankings for various countries which have highest terror activities on various levels overtime across the years like

  1. Volume of Attack – Includes No. of causalities, injuries, GTI score and no of attacks
  2. No of attacks vs No of killings
  3. Type of weapons used for terrorist activates
  4. Type of targets for terrorist activities

dashboard-1

 GEO VISUALISATION – CARTO

Carto Link

Carto was used for Geo-Visualization. We have used following 5 layers:

  1. International Logistically: The terrorist activities are divided into two categories, international logistically (which means that the perpetuators are of different nationality than the place of attack or not).
  2. Types of Weapons Used : This layers shows the types of weapons used by terrorist. We can see from the map that firearms and explosives are most common weapons.
  3. Types of Attacks: In this layer we show the different types of attacks by terrorist. Bombing, Armed Assaults and Hostages are most common attacks types.
  4. Intensity of Casualties: The heat map in this layer shows the severity of the casualties in the terrorist attacks.
  5. Suicide Attack: This layer indicates whether the attack was suicide attack or not.

Do let us know in case of any feedback/ suggestions/ queries.

Created By:

  • Disha Grover (A0148618W)
  • Manan Katiyar(A0148510M)
  • Ram Thilak Prem Kumar (A0148522H)
  • Shweta Sharma (A0148486N)
5 Questions – Impact of Climate Change on Winter Tourism —

5 Questions – Impact of Climate Change on Winter Tourism

The winter sports industry/community is deeply dependent upon predictable, heavy snowfall, but climate change is expected to contribute to warmer winters, reduced snowfall, and shorter snow seasons. The estimated $12.2 billion U.S. ski and snowmobile winter sports industry have already felt the direct impact of decreasing winter snowpack and rising average winter temperatures. Warmer climate translates into less snow and fewer people on the slopes, which results in massive economic hardship for resorts, states, local communities, businesses and their employees.

The purpose of the tableau is to help policy makers understand both the ski and snowmobile industry’s current economic scale and the potential economic impacts that climate change may cause.

winter-sports

We have come up with 5 questions using GQM technique directed towards the United States National Tourism Office- Labor and Commerce Department. 

 

winter-sports-goals
Do let us know in case of any feedback/ suggestions/ queries.

Created By:

  • Disha Grover (A0148618W)
  • Manan Katiyar(A0148510M)
  • Ram Thilak Prem Kumar (A0148522H)
  • Shweta Sharma (A0148486N)
Dashboard – 5 Questions — October 30, 2016

Dashboard – 5 Questions

Our chosen dashboard for the “5 Questions” assignment came from the Sales domain. It showed a rich and progressively granular view of a company’s sales performance in the US. We summarized the key questions that this dashboard can answer, as detailed below.

Additionally, we also highlighted two areas where the dashboard can be made more effective. The detailed report on this can be accessed Continue reading

Geo-spatial Visualization and Analytics —

Geo-spatial Visualization and Analytics

We selected Olympic 2016 as our Geo-spatial Analytics and Visualization topic because of its truly global nature encompassing all countries and also since it was a live event during the course of the assignment making our journey an evolutionary one.

We gathered data from multiple sources on the current and last (2012 London) Olympic. To put country performances in perspective and to assess impact of different socio-economic indicators on medals standing, we collected data on several economic and lifestyle indicators from ~10 different online sources. Additionally, we used Twitter data for a global sentiment analysis on Olympic, 2016. Continue reading