a review of chronic diseases map and data trends analysis · visualization is the art of presenting...

International Journal of Computation and Applied Sciences IJOCAAS, Volume2, Issue 2, April 2017, ISSN: 2399-4509

73

A Review of Chronic Diseases Map

and Data Trends Analysis

Mustafa Yousef Mustafa ZGHOUL

Abstract — In this paper, an attempt is made to review

and analysis the status of the chronic disease the gulf

region. In addition, illustrate the methods of visualization

of the chronic diseases information, which are the major

disease burden in the gulf region. This paper, is reviewed

and discussed the methods of analysis the chronic diseases

data with reference to univariate time series model. The

Forecasting model based simple linear regression is

proposed and implemented. The Visualization of digital

data is considering as one of the most important

techniques for presenting the distribution of statistical

data within a small space. An interactive disease map is

proposed and implemented to determine the chronic

diseases numbers and locations.

Keywords: Chronic diseases, visualization, statistical data

analysis, linear regression, time series, forecasting, mapping

I. INTRODUCTION

Chronic diseases (CD) pose a constant danger and a large

force because of their increased risk to human life and the

economy of nations. Statistics issued by the World Health

Organization (WHO) indicate that the number of people living

in chronic disease increasing in all parts of the world as

illustrated in Figure 1.

Fig. 1: The total NCD deaths by region 2012

(AFR: Africa; AMR: America; SEAR: South East Asia; EUR: European;

EMR: Eastern Mediterranean; WPR: Western Pacific; Source WHO)

Masters Student in Computer Science, Faculty of Computing and IT, Sohar

University, Sohar, Sultanate of Oman, [email protected]

In addition, WHO reported that the chronic diseases are the

major disease burden in the gulf region, "the chronic diseases

cause more than 60% of all deaths in the Gulf Cooperation

Council (GCC) countries [1] as shown in Figure2.

Fig. 2: The Diabetes Growth Rate and Affected Population (in ‘000)

during 2000 to 2030, Source WHO.

Moreover, depending on the ministry of health statistics in

the Sultanate of Oman during the period (1990 - 2005),

approximately 75% of diseases burden is attributable to chronic

diseases" [2]. The total number of death because of chronic

diseases are a double number of death caused by infectious

diseases, like pulmonary tuberculosis, viral hepatitis (A),

malaria, and AIDS (HIV). In addition, the chronic diseases

affect women and men at younger ages. There are a number of

risk factors causes the chronic diseases including physical

inactivity, unhealthy diet and use of tobacco. The chronic

disease is a factual dangerous and it is growing rapidly over the

time, Therefore, many governments spent billions of dollars for

controlling and preventing the expansion of CD [3]. WHO

reports indicate that the occurrence of CD will be raised

dramatically in 2023 as shown in Figure 3.

For this reason, one of the solutions to reduce the rate of

chronic diseases is increasing the health awareness in the

community. Therefor, design and implement a web application

for the chronic diseases surveillance is much needed, which

will help in raising the knowledge of culture-related chronic

diseases and methods of reducing its effects.

mailto:[email protected]


74

Fig. 3: Expected rate of CD in 2023 (Source WHO)

The chronic disease (or non-communicable disease) is a

disease that remains or continues for a long period of time,

starts from about three months or more. Generally, these

diseases cannot be prevented using a treatment or take a

medicine. Ministry of health in Sultanate of Oman is working

on educated members of the community against the chronic

diseases like diabetes, blood pressure, etc., in order to increase

the health awareness. The infectious disease (or communicable

disease) is a disease that generated by microorganisms, like

bacteria, parasites, fungi and viruses. In addition, can be

infected other people quickly through sneezing and coughing,

or through physical contact. Examples of infectious diseases

are malaria, pulmonary tuberculosis, viral hepatitis (A) and

AIDS (HIV) [4]. Therefore, the government’s wok hard to

reduce the expansion of the chronic disease and infectious

diseases by offering intensive programs and studies of injuries

prevention methods [5]. The main objective of this paper is to

promote awareness of the expansion of chronic diseases in

GCC. Therefore, design and implement a web application for

analyzing, forecasting and visualizing chronic diseases is much

needed. This system will collect the data of chronic disease and

illustrate the analysis statistics rates in all GCC. In addition, it

will provide a very clear visualization figures based on

interactive disease map.

II. VISUALIZATION METHODS AND TECHNIQUES

Many techniques are used to visualize and analysis the

data. The interactive disease map is one of a powerful method

for data visualization. It is used to illustrate information clearly

and efficiently via plots, statistical and information graphics.

The visualization of statistical data aims to present a large

amount of information in the short time. Numerical data can be

graphed using lines, bars or dots to visual communicate a

quantitative message. Recent studies proved that the interactive

disease map helps the users to analyze and understand the

meaning of complex data easily. Generally, tables are used

where the users look up to a specific measurement, while

charts and maps are used to present patterns or relationships of

multivariate data. The diseases map is a method for

representing different diseases for tracking the expansion of

diseases in real time and understanding the reasons of these

diseases [6]. A time series is a group of observations or

statistics which being recorded or collected at regular intervals

and sorted by the time [7]. Therefore, the proposed system will

provide an accurate analysis for predicting unseen data over

time. Moreover, the trend analysis is a type of technical

analysis that tries to predict the future values of data

(information in sequence over time) based on past data [8],[9].

The analyze of the statistical data based on trends analysis

method is called linear regression analysis and which will help

the decision makers in the ministry of health to determine the

expansion and needs of chronic diseases [10],[11].

Cartographic visualization is used symbolism techniques,

which is referred to locations inside the map and represent their

multiple data values [12]. In addition, it allows the users to

select statistical and geographical subsets. Local statistics of

univariate distributions can be calculated and visualized in a

dynamic figure for exploration like scatterplots, dot-plots,

population cartograms, choropleths, parallel coordinates plots,

and polygon maps as shown in Figure 4.

Fig. 4: Cartographic visualization source [12]

According to Statistics Netherlands (2012), "data

visualization is the art of presenting data in a visual manner.

So the data becomes apparent. Data visualization is a helpful

tool for all phases of the statistical process. It has two goals in

statistics: data exploration and communication" [13].

Diagrams can detect the pattern in large amounts of data. This

method views the major findings in the data to the users. There

are many types of diagrams, like radar plot, bar chart, and line

chart. It used to represent the time series or to make a

comparison between two variables. On the other hand,

proportional symbol map is a good way of viewing statistical

data. It aims to view the characteristics of a subject on the map.

"In a proportional symbol map, a symbol is plotted on the

center of a region, and the surface area of the symbol is scaled

with the value of the variable" [13]. For example, consider the

proportional symbol map as shown in Figure 5.


75

Fig. 5: Proportional symbol map source [13]

According to Heinrich Hartmann (2016), "statistical

techniques are the art of extracting information from data.

Moreover, one of essential data analysis methods is

visualization. The human brain can process geometric

information much more rapidly than numbers." [14]. There are

many methods for visualization, like rug plots, histograms (for

one-dimensional data), scatter plots (for two-dimensional data),

line plots. [14]. as an example, consider the proportional

symbol map as shown in Figure 6.

Fig. 6: Visualization methods source [14]

The histogram is a most popular visualization method. In

addition, it is commonly used to show the distribution of

numerical data. [14] According to Ben Fry (2008), "within a

small space, the visual can communicate (or announce) more

information than the table." [15]. Mapping is one of the

modern techniques, which is used to visualize the information.

The process of mapping starts with collect the data values and

store it inside data set (database). Then design (or draw) the

map to represent the data values on it. After that determine

some locations (or points) on the map, usually these locations

should be the center of each governorate. Afterward use

functions to load the set of data values from the database,

which will show on the map itself. As an example, consider the

visualization of varying data by symbol size on the map as

shown in Figure 7. [15]

Fig. 7: Visualization of varying data by symbol size on map

source [15]

According to Adriana REVEIU, Marian DARDALA

(2011), cartographic visualization provides the facilities to

represent statistical data. It is one of the most important tools in

geographical information systems (GIS). In addition, it aims to

show the distribution of statistical data inside the regional map.

Moreover, it uses different symbols to show more information

at the same time inside the map. These symbols view

quantitative details for the users. [16]

III. ANALYSIS OF CHRONIC DISEASES

The analysis of chronic diseases often depends on

longitudinal cohort (or group) studies, and there are many

methodological issues related to chronic disease studies, the

first one is based on changing definitions of risk factors and

outcomes over time. Moreover, the second issue is missing

data.

To perform analysis in the existence of missing data, there

are many procedures to do that: the first one, make analysis to

individuals which data is complete. The second one, insert the

existing values to individuals which data is incomplete and

then analyzing the dataset. Moreover, the second analytical

technique is most appropriate. There are many analytic

techniques for chronic diseases modeling: [17]

1. Logistic regression analysis: this can examine the effects

of risk factors on the development of the disease.

2. Survival analysis (time to event data): this deal with time

until the event of interest occurs.

3. Neural networks.

4. Tree-based classification methods.

5. Longitudinal data analysis (mixed models, generalized

linear models and generalized estimating equations).

The formula for representing the regression analysis model

is:

Yi = a + b * Xi

Where the regression parameters, a: is the intercept (on the

y axis) . And b: is the slope of the regression line


76

The Least Squares method used to estimate the slope and

intercept regression parameters, as the following:

The formula for calculating slope regression parameter (b)

is:

The formula for calculating intercepts regression parameter

(a) is:

IV. LITERATURE SURVEY

This section presents the literature survey of the chronic

diseases data analysis. The researchers were implemented

different computing techniques for forecasting and visualizing

the unseen data. It will cover the expansion rate of chronic

diseases in the global. In addition, the forecasting models and

visualization techniques are included. (WHO) global report [3]

statistics shows that the number of deaths in 2005 is about (58)

million, around (35) million approximately of deaths resulted

by chronic diseases. It represents about 60% of global deaths,

which caused by chronic diseases, "only 20% of chronic

disease deaths occur in high-income countries, while 80%

occur in low and middle-income countries, where most of the

world’s population lives". The chronic diseases will rise about

70% of the total deaths in the world at 2030. Khatib O. [18]

stated, "chronic diseases are the major disease burden in the

Eastern Mediterranean Region. There are many risk factors

associated with chronic diseases, most of them are related to

the lifestyle and can be controlled. Such as low vegetable and

fruit intake, physical inactivity, high fast food consumption and

high cholesterol are dominant causes of cardiovascular disease

and some types of cancer. Also obesity and overweight can

raise the risk of chronic diseases, like heart disease and

diabetes". There are many approaches can help to deal with this

problem such as developing national strategies, policies, and

plans for prevention and health care. Also, implement and

enhance the community participation in prevention and health

care. Most of the solutions for preventing chronic diseases are

expensive [18]. Gregory Hartl & Menno van Hilten (2012) [1],

explains that the non-communicable diseases (NCDs) are

caused more than 60% of all deaths in the Gulf Cooperation

Council (GCC) countries. The risk factors are an unhealthy

diet, the use of tobacco and the physical inactivity. The Gulf

Cooperation Council (GCC) countries (Saudi Arabia, Bahrain,

Sultanate of Oman, Kuwait, Qatar, and United Arab Emirates)

are adopting a regional strategy to address, prevent and control

of non-communicable diseases (NCDs), like diabetes, cancer,

and chronic respiratory disease. It aims to reduce exposure to

people from different risk factors and improving the services of

preventing and treating the health problems [1]. Hill A.G., et

al. (200), mentioned that the awareness about the chronic

diseases has been grown in Oman. Morbidity for diagnosis

related to chronic diseases, especially cancer, cardiovascular

disease, and the endocrine disease was growing and becoming

a significant share of Oman's burden of disease [19]. Jawad A.

Al-Lawati et al. (2008), believes that the chronic diseases are

posed the main challenge for Omani population [2]. Depending

on the ministry of health statistics in Oman during the period

(1990 to 2005), approximately 75% of diseases burden is

attributable to chronic diseases. "The distribution of chronic

diseases and related risk factors among the general population

is similar to that of industrialized nations: 12% of the

population has diabetes, 30% is overweight, 20% is obese,

41% has high cholesterol, and 21% has the metabolic

syndrome". They conclude that the chronic diseases are the

major exhaustion on human and financial resources for

Sultanate of Oman, and this will affect the advances in the

health care system that has been achieved. Similarly, some

related works focus on using visualization techniques like

interactive map. Jason Dykes (1998), implemented a

cartographic visualization for locating symbols on a plane to

show the statistical distributions of one or more variables" [12].


77

V. RESULTS AND DISCUSSIONS

Depends on statistical data which collected from ministry

of health in sultanate of Oman, it shown that the prevalence of

diabetes in Oman is increasing over years. as illustrated in

Figure 8.

Figure 8: Diabetes in Oman (source MoH Oman)

After applying the equation of simple linear regression, the

equation will be (Y = 4319.3 + 127.9 * X). and the value of R-

squared variable equals to (0.46), this value will describe how

data fitted to the regression line. As shown in Figure 9.

Figure 9: Forecasting results for diabetes in Oman after 5 years

The results of this forecasting model shown that the

prevalence of diabetes in sultanate of oman will increase. And

this will help the decision makers to take attention about spread

of chronic diseases.

This research paper will implement the simple linear

regression method for statistical data analysis and forecasting

because the collected data is one variable changed over time. In

addition, this paper will select the cartographic visualization

method to show the distribution of the data among different

geographic locations. In addition, it uses symbols to represent

the data; this symbol is scaled with the value of the variable.

CONCLUSION & FUTURE RESEARCH DIRECTION

This paper provided an overview of the chronic diseases in

GCC. First, it presents the risk factors that causes the chronic

diseases. Then it explains the different methods for statistical

data analysis and forecasting models. Moreover, presents

different methods for data visualization techniques is presented

and reviewed. The Cartographic technique is appropriate for

visualization of Chronic Diseases data because it illustrates the

distribution of the data among different geographic locations.

The literature survey proved that a simple linear regression is

an appropriate method for data analysis and forecasting data

with one variable only.

REFERENCES

[1] Gregory Hartl, Menno van Hilten (January 2012). “WHO recognizes

progress of Gulf States for adopting regional strategy to address non-communicable diseases", [Online], available at:

http://www.who.int/mediacentre/news/statements/2012/ncds_20120106/en/

(accessed 15 September 2016)

[2] Al-Lawati JA, Mabry R, Mohammed AJ (July 2008), “Addressing the

Threat of Chronic Diseases in Oman", CDC, VOLUME 5: NO. 3.

[3] World Health Organization (2005), WHO global report: "Preventing

chronic diseases: A vital investment". Geneva: ISBN 92 4 156300 1.

[4] “What are infectious diseases?", [Online], available at: http://www.yourgenome.org/facts/what-are-infectious-diseases (accessed 29

July 2016)

[5] “Directorate General for Disease Surveillance and Control in Ministry of

Health - Sultanate of Oman”, [Online], available at:

https://www.moh.gov.om/en/web/directorate-general-of-disease-surveillance-control/ (accessed 02 June 2016)

[6] “Mapping Disease", [Online], available at: http://www.the-scientist.com/?articles.view/articleNo/35349/title/Mapping-Disease/ (accessed

23 July 2016)

[7] "Time Series", [Online], available at: http://www.statslab.cam.ac.uk/~rrw1/timeseries/t.pdf (accessed 03 October

2016)

[8] "Trend Analysis", [Online], available at:

http://pubs.usgs.gov/twri/twri4a3/pdf/chapter12.pdf (accessed 15 June 2016)

[9] “Time trends analysis", [Online], available at: https://ec.europa.eu/jrc/sites/default/files/epaac-wp9-session2-crocetti.pdf

(accessed 02 July 2016)

[10] Jabar H. Yousif, Classification of Mental Disorders Figures based on Soft

Computing Methods. International Journal of Computer Applications

117(2):5-11, May 2015.

[11] Jabar H. Yousif and Mabruk A. Fekihal. Neural Approach for

Determining Mental Health Problems. Journal of Computing, Vol.4, Issue 1,

pp6-11 ISSN 2151-9617 ,NY, USA, January 2012.

[12] Jason Dykes (1998), "Cartographic visualization: exploratory spatial data

analysis with local indicators of spatial association using Tcl/Tk and cdv", UK, The Statistician, 47, Part 3, pp. 485-497.

[13] Edwin de Jonge (2012), "Data Visualization", Netherlands, Statistics

Netherlands, ISSN: 1876-0333.

[14] Heinrich Hartmann (2016), "Statistics for Engineers", USA, ACM Queue

10.1145/2890780.

[15] Ben Fry (2008), "Visualizing Data", USA, O’Reilly, ISBN-10: 0-596-

51455-7.

[16] Adriana REVEIU, Marian DARDALA (2011), "Techniques for

Statistical Data Visualization in GIS", Romania, Informatica Economica, vol.

15, no. 3/2011

[17] Ralph D'Agostino, Lisa M. Sullivan (January 2002), "Chronic Disease

Data And Analysis: Current State Of the Field", Boston (US). Digital Commons. Vol. 1: No 2, 228-239, Article 32.

[18] Khatib O. (2004), "Non-communicable diseases: risk factors and regional

strategies for prevention and care", East Mediterr Health J 2004; 10(6):778-88.


78

[19] Hill AG, Muyeed AZ, Al-Lawati JA. (2000), "The mortality and health

transition in Oman: patterns and processes". Muscat (OM): World Health

Organization, Regional Office for the Eastern Mediterranean, UNICEF Oman.

[20] “Definition of Chronic disease", [Online], available at: http://www.medicinenet.com/script/main/art.asp?articlekey=33490 (accessed

07 June 2016)

[21] "Histogram", [Online], available at: http://searchsoftwarequality.techtarget.com/definition/histogram (accessed 03

October 2016)

[22] Astrid Schneider, Gerhard Hommel, and Maria Blettner (2010), "Linear

Regression Analysis", Germany; Medicine. 107(44): 776–82.

[23] Christoph Klose, Marion Pircher, Stephan Sharma (May 2004), "Univariate Time Series Forecasting", UK.

[24] Choong-Yeun Liong and Sin-Fan Foo (2013), "Comparison of Linear Discriminant Analysis and Logistic Regression for Data Classification",

Malaysia, LLC 978-0-7354-1150-0.

[25] Suzilah Ismaila, Rohaiza Zakariaa and Tuan Zalizam Tuan Mudab

(2014), "Univariate Time Series Forecasting Algorithm Validation",

Malaysia, LLC 978-0-7354-1274-3.

[26] Adela SASU (2013), "A Quantitative Comparison of Models for

Univariate Time Series Forecasting", Romania, University of Brasov, 117-

124.

[27] Kelly H. Zou, Kemal Tuncali, Stuart G. Silverman (2003), "Correlation

and Simple Linear Regression", USA, Radiology; 227:617–628.

[28] Mohammad S. Alam (2013), "Analytical Review of Data Visualization

Methods in Application to Big Data", Russia, Journal of Electrical and

Computer Engineering, Article ID 969458, 7 pages.

[29] Peter Filzmoser, Karel Hron, Clemens Reimann (2009), "Univariate

statistical analysis of environmental (compositional) data: Problems and possibilities", Austria, ScienceDirect, STOTEN-11466.


79

S Author year Place Method of

study Major Findings Merits Limitations

1 Hill A.G., Muyeed

A.Z., Al-Lawati J.A.

[19]

2000 Oman Numerical and

Analytical

Diabetes has become a

major chronic (or non-

communicable) disease

problem in Oman. The

prevalence of diabetes

varies among different

governorates in Oman; the

percentage for diabetes in

2000 is 13%.

The awareness about

the chronic disease has

been grown in Oman.

The percentage for

diabetes in Oman was

the highest reported in

the arab countries.

2 Khatib O. [18] 2004 Oman Analytical

The incidence of chronic

diseases is rising in the

middle east region. In 2000,

47% of the region’s load of

disease is due to Non-

communicable diseases and

it is expected that this will

rise up to 60% by the year

2020.

There are many

strategies: developing

a national strategies,

policies and plans for

prevention and care,

also implement and

enhance community

participation in

prevention and care.

The risk factors

associated with chronic

diseases are: physical

inactivity, unhealthy

diet and smoking.

3 World Health

Organization [8] 2005 Geneva Analytical and

Numerical

The number of deaths in

2005 is about (58) million,

around (35) million

approximately of deaths

resulted by chronic

diseases. It represents about

60% of global deaths which

caused by chronic diseases.

If the current trends

continue, chronic diseases

by 2030 will rise about

70% of the total deaths

globally.

Many governments

around the world spent

billions of dollars for

medication to control

and prevent the spread

of chronic diseases

Chronic diseases are a

threat and it is growing

over the time and it will

affect all countries.

4 Jawad A. Al-

Lawati, Ruth Mabry

2008 Oman Analytical The chronic diseases pose

the main challenge for

Health planners and

decision makers in

Chronic diseases are the

major exhaustion on


80

and Ali Jaffer

Mohammed [20] Omani population. During

the period (1990 to 2005),

approximately 75% of

diseases burden is

attributable to chronic

diseases.

Oman have greater

commitment to the

provision of services

for people with

chronic diseases.

human and financial

resources for Oman,

and this will affect the

advances in the health

care system that has

been achieved.

5 Gregory Hartl,

Menno van Hilten [1]

2012 Geneva Analytical

The chronic diseases cause

more than 60% of all deaths

in the Gulf Cooperation

Council (GCC) countries.

All the GCC countries

adopting regional strategy

to address prevent and

control of chronic diseases.

The regional strategy

in all the Gulf

Cooperation Council

(GCC) countries aims

to reduce exposure of

people to risk factors

and improving the

services to prevent and

treat these health

problems.

There are many risk

factors related to

chronic diseases, like:

unhealthy diet, the use

of tobacco and the

physical inactivity.

6 Ralph D'Agostino,

Lisa M. Sullivan

[17]

2002 Boston

(US)

Logistic Regression

Analysis, and

Survival Analysis

The model for chronic

disease consists of four

stages or phases: disease

free, pre-clinical (latent

period), clinical

manifestation, and follow-

up. Good statistical

approaches involve

hypothesizing models for

these stages, collecting

appropriate data, and then

fitting and testing the

appropriate models. The

risk factors are: age,

gender, smoking status,

blood pressure and

cholesterol.

The logistic regression

analysis can examine

the effects of risk

factors on the

development of

disease. And the

survival analysis (time

to event data) deal

with time until the

event of interest

occurs.

There are many

methodological issues

related to chronic

disease analysis, the

first one is based on

changing definitions of

risk factors and

outcomes over time.

And the second issue is

missing data.

7 Kelly H. Zou,

Kemal Tuncali,

Stuart G. Silverman

[27]

2003 USA Simple Linear

Regression

Simple linear regression

measure the linear

relationship between a

predictor variable and an

-The values of

dependent variables

can be estimated from

the observed..

- missing values is a

common problem.


81

outcome variable.

- It summarise the real

observations of model.

8 Christoph Klose,

Marion Pircher,

Stephan Sharma

[23]

2004 UK Autoregressive

moving average

(ARMA) linear

model

Forecasting of time-series

can be done with different

linear models, including

Autoregressive (AR),

Moving average (MA), and

ARMA model. For

building ARMA model,

there are three phases:

model identification,

estimation (or fitting) and

validation.

- The ARMA model

process is stationary.

- Also it can minimize

the number of

parameters.

- ARMA model is

suitable only for the

time series that is

stationary (for example

its mean and variance

should be constant over

time).

- It is required that there

are at least 40

observations in the

input data.

- It is also supposed that

the values of the

estimated parameters

are constant during the

series.

9 Peter Filzmoser,

Karel Hron,

Clemens Reimann

[29]

2009 Austria multivariate data

analysis

The process of statistical

data analysis starts with

looking at the data with

appropriate graphical tools.

For instance, histogram is

giving us an idea about the

data distribution.

It can estimate and

view the relationship

between different

variables.

- This method is

complex and involves

high level of

mathematical

calculations.

- The results are not

sometimes easy to

interpret.

- It needs a large

amount of data.

10 Astrid Schneider,

Gerhard Hommel,

and Maria Blettner

2010 Germany Linear Regression

Analysis

There are three types of

regression analysis: Linear

regression, Logistic

- Describe the

relationships between

dependent and

The most common

problem in medical data

analysis is missing


82

[22] regression, and Cox

regression. And the Linear

Regression is the most

common approach or tool

used for modelling

univariate time-series. And

this tool is used for

statistical and predictive

analysis.

independent variables.

-The values of

dependent variables

can be estimated from

the observed.

- The risk factors that

affect the outcome can

be identified.

- It summarise the real

observations of model.

values.

11 Choong-Yeun Liong

and Sin-Fan Foo

[24]

2013 Malaysia logistic regression

(LR) analysis and

Linear discriminant

analysis (LDA)

There are two methods used

for classifying data: logistic

regression (LR) and Linear

discriminant analysis

(LDA), these methods of

classifying data depends on

set of predictor variables.

The Logistic regression

(LR) is more robust than

the Linear discriminant

analysis (LDA).

- Logistic regression

(LR) method performs

better distribution of

the data.

- Linear discriminant

analysis (LDA) needs

short computing time

with large sample size.

- Logistic regression

(LR) needs long

computing time with

large sample size.

- The Linear

discriminant analysis

(LDA) will give low

performance when the

prior probability is

equal for all groups.

12 Adela SASU [26] 2013 Romania ARIMA

Linear Regression

(LR(

Multilayer

perceptrons network

(MLP)

The purpose from the

forecasting process is to

observe or model the

existing data series to

predict accurately the future

unknown data values, for

example: after five years.

The Linear regression

(LR) and Multilayer

perceptrons network

(MLP) give more

accurate predictions.

- ARMA model is

suitable only for the

time series that is

stationary.

- ARIMA gives less

accurate predictions

results.

13 Suzilah Ismaila,

Rohaiza Zakariaa

2014 Malaysia Quantitative

(Projective)

The process of forecasting

is not simple, because it

- This technique gives

valuable judgment.

- This technique require

large amount of data in


83

and Tuan Zalizam

Tuan Mudab [25]

forecasting

technique

requires expert and implicit

knowledge in producing

precise forecast values. For

statistical data, the

quantitative forecasting

technique is suitable.

- Also it giving more

weight to recent data.

order to formulate the

mathematical model.

14 Jason Dykes [12] 1998 UK Cartographic

visualization

Cartographic visualization

is a map based method.

And it is used by locating

symbols on a plane to show

the statistical distributions

of one or more variables.

Also local statistics and

indicators can be calculated

and visualized in a dynamic

fashion for exploration.

- Cartographic

visualization use the

locations inside map to

show data values.

- Interactive way.

- Very hard for

automatically created

dynamic maps.

15 Ben Fry [15] 2008 USA Mapping The visual can

communicate or announce

more information than the

table within a small space.

- make comparison

between different

areas.

- present the

geographic location

and the distribution of

the data.

- The size of symbol

may hide the location

on map.

- It consumes more time

to execute, depends on

size of data.

16 Adriana REVEIU,

Marian DARDALA

[16]

2011 Romania Cartographic

visualization

Cartographic visualization

provides the facilities to

represent statistical data

inside the map.

- Show the distribution

of statistical data

inside the regional

map.

- use different symbols

to show more

information at the

same time inside the

map.


to execute, when the

volume of data increase.


84

17 Statistics

Netherlands [13]

2012 Netherla

nds

Proportional symbol

map

Proportional symbol map is

a good way of viewing

statistical data. It aims to

view the characteristics of a

subject on the map.

- present the

geographic location

and the distribution of

the data.

- make comparison

between different

areas.

- summarise large

amounts of data.

- It is difficult to

calculate the actual

value (if not shown).


to execute.

- The size of symbol

may hide the location

on map.

18 Mohammad S.

Alam [28]

2013 Russia - Tree-map

- Circle Packing

- Sunburst

- Circular Network

Diagram

One of the most

requirements that the

analyst tools should meet is

to show more than one

view per representation

display. Circular Network

Diagram is the most

appropriate method for

visualizing the statistical

data.

- Tree-map:

hierarchical grouping

clearly shows data

relations.

- Circle Packing:

space-efficient.

- Sunburst: easily

perceptible by most

humans.

- Circular Network

Diagram: allows us to

make relative data

representation. And

inside the circle, the

resolution varies

linearly, increasing

with radial position.

- Tree-map: not suitable

for examining historical

trends and time

patterns.

- Circle packing: same

as for Tree-map

method.

- Sunburst: same as for

Tree-map method.

- Circular Network

Diagram: objects with

the smallest parameter

weight can be

suppressed by larger

ones.

19 Heinrich Hartmann

[14]

2016 USA Histograms

Line plots

One of the most essential

data analysis methods is

visualization. The

Histogram is a

popular visualization

- Histogram view

number of values

within interval.

- Histogram used only

for numerical.

- Line plot is difficult to


85

Rug plots

Scatter plot

method. It is commonly

used to show the

distribution of numerical

data.

- Line plot show

changes in data over

time.

- Scatter plot show the

relationship between

two variables.

read accurately, if there

is a wide range of data.

- Scatter plot cannot

show the relation of

more than two

variables.

a review of chronic diseases map and data trends analysis · visualization is the art of presenting...

Documents