Analytics And Intelligent Systems

NUS ISS AIS Practice Group

HDI Global Index – Geospatial Analytics — November 10, 2016

HDI Global Index – Geospatial Analytics

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Link : https://mrlanholmes.carto.com/viz/abf65832-766a-11e6-91fa-0e233c30368f/public_map

Insights

1.We see a trend where by mean year of schooling is lower in the lower hemisphere countries such as Africa, South America and parts of South East Asia.

2.We then see this move upwards towards Europe in particular where is clear that the mean year of schooling is higher in Europe.

3.This matches the Polygon colour of countries that the upper hemisphere countries are in a darker shed of orange-red which shows a higher Human Dev. Index score

4.Hence, affirming the correlation of school years to HDI which we will see in the correlation table below.

Our plan:

–To display the change in a factor (HDI, mean schooling years, etc) over a timeline of years.

–This required 3 display variables , Country, time and variable value.

–Carto is not able to show this the way we wanted especially with the fact, timelines cannot be done for Polygon.

Solution:

Using R Shiny

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Cyber Security Agency (CSA) Singapore —

Cyber Security Agency (CSA) Singapore

Abstract

The CSA is the national body overseeing cyber security strategy, education and outreach, and industry development. It reports to the Prime Minister’s Office and is managed by the Ministry of Communications and Information.

Who we are?

  • Engagement and outreach – Nurturing ties with local and global industry and thought leaders
  • Industry development – Developing a robust cyber security ecosystem, i.e. a vibrant industry equipped with the manpower to respond to and mitigate cyber attacks
  • Protecting critical sectors – Strengthening cyber security in our critical sectors, such as energy, water, and banking
  • Operations – Ensuring effective coordination and deployment in our response to cyber threats

 

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What is the current master plan?

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A look at our dashboard to look at the situation

To view this interactively, you will require to have a Qlik account to view this.

Link as below:

https://www.qlikcloud.com/view/57c58566afe2230100cbbd23

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Type of Industries in consideration:

  • Communication
  • Education
  • Energy
  • Financial
  • Government
  • Healthcare
  • Transports
  • Waters
  • Others

Types Of Attack:

  • Eavesdropping
  • Data Modification
  • Identity Spoofing
  • Password-Based Attacks
  • Denial-of-Service (DOS) Attacks
  • Man-in-the-Middle (MITM) Attack
  • Compromised-Key Attack
  • Sniffer Attack
  • Application-Layer Attack

 

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References:

 

Created By:

Alan Rulivan Johan  – A0148385U

Edmond Chin Juin Fung  – A0148408B

Lim Pang Wei Jonathan – A0148419X

Ang Siong Guan – A0150402B

SOIL DEGRADATION – GEOSPATIAL ANALYTICS —

SOIL DEGRADATION – GEOSPATIAL ANALYTICS

Introduction

Global Soil Degradation data for the GIS Analysis. The GLASOD data base contains information on soil degradation within map units as reported by numerous soil experts around the world through a questionnaire. It includes the type, degree, extent, cause and rate of soil degradation. From these data, GRID produced digital and hardcopy maps and made area calculations

The dataset, a shpfile ,  is originally found from United Nations Environment Programme, Environmental Data Explorer. It contains wide range of data from the United Nations Environment Programme including Night-time Lights, Pollutant Emissions, Commercial Shipping Activity, Protected Areas and Administrative Boundaries.

http://geodata.grid.unep.ch/results.php

We have used ARCGIS 10.4 for the analysis part and R for the modelling part.

Abstract

The following sections of the document illustrate the different stages of the analysis performed on the soil degradation data of India. The results of the same help decide on feature engineering. The final set of features so obtained is used to build several statistical models. Ensemble of models is also designed to improve on accuracy of prediction.

Accuracy of the models is measured by the accuracy percentage handling statistical metrics.

Geo-Referencing

The original glasod shpfile did not have any reference to coordinate system when it was added to ARCGIS. As the shpfile is basically a vector dataset consisting of polygons, we did Geo-Referencing, by tagging it to GCS_WGS_1984 coordinate system under Geographic Coordinate System.

Once the Geo-referencing was done, we could view the shpfile global dataset in ARCGIS as below:

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Outcome after Geo referencing

The shpfile contained details of polygons and each polygon represents some reference with respect to Soil degradation in that area.

Geo Processing

Being a Global Dataset for Soil erosion, we tried to study for a particular country, such as India, to have more visibility. As part of this objective, we employed Clipping, Merging as Geoprocessing options to achieve the same.

To start with clipping, we first identified the polygons Geo IDs, Glas_Geo_ID for every polygon in India and selected them using SQL queries at attribute table.

To get our hands on to other Geo-processing options, we used features such as Merge, Buffer, Dissolve etc. but they weren’t handy in outcome.

To study individual features in India according to soil type Degradation we tried to split the attributes. For this operation, we installed a new add-in called X-tools pro.

This tool has facility to perform multiple operations on vector datasets, especially overlay operations. We used this to perform Split by Attributes of India according to TYP1 and TYP2.

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This operation helped in studying features at a deeper level to get more visibility.

For example, split by attribute 3, the soil type which is prevalent across most part of India running from North to South across the East is considered the most fertile and thus is not degraded according to the soil data.

At the same time, the prevalent soil type in the west is degraded mostly due to forest degradation (fg) which correctly represents as loss of top soil (Wt). The same can be confirmed from the expert’s perspective at the below link:

http://ces.iisc.ernet.in/biodiversity/news/12112002.htm

Hot Spot Analysis:

Using ArcMap’s the areas with high likelihood of being impacted are visualized by default. Thus for Hot spots areas with 99% confidence interval are displayed. Similarly, the areas with 90% confidence interval are displayed in cold spots.

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Hot Spot Analysis – Severity – 2

Inference for Sev-2

The Sev-2 means that this is applicable for type-2

In the above figure we have considered the confidence interval as 99%. The red colour area signifies the hot spots. So in these areas the soil degradation is highly possible.

Recommendations for Sev-2

Soil Degradations causes the soil to lose all its nutrients. Using this predictive analysis, we recommend to take measures to control the degradation by the following ways:

  • Afforestation of the area before it gets impacted.
  • Plant grass & shrubs in the area
  • Use mulch matting to hold vegetation of shrubs
  • Improve drainage

Modelling

As a part of modelling, we intended to perform a Multi – Class Classification Modelling on rate of Soil Degradation across the world Degradation data.

Since the target variable is a categorical variable with 4 levels (Rate – 0 ,1 ,2 ,3) we did a multi -class classification using algorithms such as Recursive Partitioning (RPART), Conditional Tree (CTREE) and Adaptive Boosting (ADA Boost –  Ensemble of Trees).

Classification Models

3 types of models are used for predicting the target class.

Accuracy of the models stood as below:

Model Accuracy
RPART 76%
CTREE 78%
ADABOOST 79%

The best performing model achieved around 80% accuracy, which is good. Also all the models had no mispredictions for slow degradation (rate – 0) soil polygon data. Also the other side, there were slight mispredictions for the rapid degradation data. The incorrectly predicted classes were identified, checked in the map and they didn’t follow any pattern.

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Output of RPART
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Variable Importance

 

Summary

  • Geo-Referencing to WCS Geographic Coordinate System.
  • Geo-Processing options such as Clipping, Merging were tried to study a separate country such as India.
  • To study the attributes separately, Split by Attributes from X Tools pro was done.
  • Also to identify the significance of degradation of soils in India, Hot and Cold spot analysis was carried out.
  • Finally to generalize the rate of Soil of Degradation around the model, Classification modelling was done

Created by:

G V Kamaraju A0148570B
Priyak Banyopadhyay A0148379M
Anil Kumar Kondaveeti A0148461A
Mahendra Prakash Subramanian A0148562Y
Smart Nation – KPI Dashboard —

Smart Nation – KPI Dashboard

The purpose of the dashboard is to define KPIs to measure the benefits and success of SMART Nation Program in Singapore. The dashboard covers the KPI from the following perspectives

  1. Investment Perspective
  2. Population Perspective
  3. Business Perspectivesmart-nation-1Each theme KPIs are further analyzed to understand the trends and how possible impact the KPIs

Investment Theme

Investment Theme looks at the Smart Nation Investment and it’s impact on ICT business in singapore. Below trend charts highlights various new smart nation intiatives started by different departments and overall impact on ICT business.

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Population Theme

Population them looks at Population technology use interms of smart services usage , opprortunities for Infocom Professionals, Security Risk preparedness and finally technology availability and adoption by the population.

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Business Theme

Busines theme looks at Smart nation impact on ICT business sentiment, Productivity growth and finally ICT Net Job creations in Singapore.

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G V Kamaraju A0148570B
Priyak Banyopadhyay A0148379M
Anil Kumar Kondaveeti A0148461A
Mahendra Prakash Subramanian A0148562Y

 

Dashboard on Hospital Alliance —

Dashboard on Hospital Alliance

Objective:

Increase accountability, transparency and efficiency

Improve patient satisfaction, outcomes and delivery times

Target Audience:

Patients

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Hospital Alliance Dashboard

5-Questions which can be answered using above dashboard are:

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Data Source:

https://gallery.idashboards.com/preview/?guestuser=webexamples&dashID=228

Team Details:

G V Kamaraju A0148570B
Priyak Banyopadhyay A0148379M
Anil Kumar Kondaveeti A0148461A
Mahendra Prakash Subramanian A0148562Y