Data visualization ananlysis_lecture 7

Computer Aided
Design and Data
AnalysisCHAIR OF Computer Aided Design AND GEO-INFORMATICS
Data, Data Types, Data Visualization and Data Analysis

CHAIR OF Computer Aided Design AND GEO-INFORMATICS
So far….

Part 1
Introduction to Computing, History of
Communication
https://zealvalid.wordpress.com/courses-and-trainings/upload-related-models-here/computer-aided-drafting-and-data-analysis-cotm2081/module-part-1/

Part 2
Introduction to Building Information Modeling
(BIM)

Computer Aided Drafting
ar

Data
And now:
Part 4
Data Analysis

What is Data?
‘’Data is a set of values of qualitative or quantitative variables.’’
‘’Data and information are often used interchangeably; however, the
extent to which a set of data is informative to someone depends on
the extent to which it is unexpected by that person. The amount of
information content in a data stream may be characterized by
its Shannon entropy’’ Wikipedia
‘’Shannon’s entropy quantifies the amount of information in a
variable, thus providing the foundation for a theory around the
notion of information.’’ Sriram Vajapeyam ,24 March 2014
See the link from your course content site for more reading abut Shannon Entropy
Data

What is Data?
While the concept of data is commonly associated with scientific
research, data is collected by a huge range of organizations and
institutions, including businesses (e.g., sales data, revenue,
profits, stock price), governments (e.g., crime rates, unemployment
rates, literacy rates) and non-governmental organizations (e.g.,
censuses of the number of homeless people by non-profit
organizations). Source: Wikimedia
Data

What is Data?
"Research data, unlike other types of information, is collected,
observed, or created, for purposes of analysis to produce original
research results." University of Edinburgh
"Research data is defined as recorded factual material commonly
retained by and accepted in the scientific community as necessary to
validate research findings; although the majority of such data is
created in digital format, all research data is included irrespective of
the format in which it is created.“Engineering and Physical Sciences
Research Council (EPSRC)
Data

Quantitative Data
Qualitative Data
Geographic Data
Temporal
Media
Entities
Data
Types

Quantitative Data
Quantitative data is any data that is in numerical form. It is often compared
to qualitative data that includes information expressed in a natural
language such as English or Japanese. The following are common types of
quantitative data.
Types include:
Measurements
A measurement of something physical. For example, a food safety
inspection that measures the temperature of food stored in a restaurant
refrigerator.
Sensors
Sensors are devices that automatically measure the physical world to create
streams of data. For example, a digital camera turns electromagnetic
radiation into a series of numbers using a color model.
Data
Types

Quantitative Data
Counts
Counting things. For example, a train station that counts the number of
passengers who enter the station in real time.
Quantification
Converting qualitative human judgments into numbers. For example, asking
customers to rate their satisfaction on a scale of 1 to 4.
Calculations
Mathematical calculations such as calculating gross margin based on
monthly sales figures.
Estimates
Producing numerical estimates as opposed to exact calculations using
algorithms, artificial intelligence, business rules or human judgment. For
example, a garbage sorting robot may estimate the probability that a
particular object is recyclable plastic.
Data
Types

Quantitative Data
Predictions
Predictions produced by algorithms, artificial intelligence or people. For
example, a stock analyst may predict the future revenue and net earnings of
a company.
Data
Types

Qualitative Data
Information is described, and classified in a nominal structure called
nominal categories like gender and religion. At times the groups can be
ordered, where variables are categorical and display attitudes as shown by
the phrases; robustly tired and strongly allowed.
This type of data cannot be measured but can be observed. Examples of
qualitative data include colors, feels, smell as well as tastes. A real life
example of information about qualities would be the color of your eyes. One
cannot use numerical figures to describe this, but they can say that their
eyes are blue. This type of data does not have a pre-defined ways of
presenting its research findings. It is characterized by heavy text but may
contain little data in numerical formats like dates.Information in this format
is difficult to analyze. There are numerous advantages associated with
structured data like the ease of use, storage, and analysis.
Source: https://ivypanda.com/
Data
Types

Qualitative Data
…It , therefore, becomes prudent for organizations to make use of
structured data formats especially when they have challenges in the high
cost of storing such data as well as analysis for the same. The above
explanation can clearly explain the phrase “what is qualitative data” in any
applied environmental phenomenon.
Examples include:
• Structured text, This is the text written during surveys and includes
comments, observations as collected on site or within a particular
environment.
• Unstructured text, This text describes interviews and conversations
during information gathering.
• Audio recordings
• Video recordings
• Multimedia data
Source: https://ivypanda.com/
Data
Types

Geographic Data
Geographic data and information are defined in the ISO/TC 211 series of
standards as data and information having an implicit or explicit association
with a location relative to the Earth.
It is also called geospatial data and information, georeferenced data and
information,as well as geodata and geoinformation.
Approximately 90% of government sourced data has a location component.
The claim that geographic information science is a distinct field of study
implies that spatial data are somehow special data. Goodchild (1992) points
out several distinguishing properties of geographic information. I have
paraphrased four such properties below. Understanding them, and their
implications for the practice of geographic information science, is a key
objective of this text.
Data
Types

Geographic Data
Geographic data represent spatial locations and non-spatial attributes
measured at certain times.
Geographic space is continuous.
Geographic space is nearly spherical.
Geographic data tend to be spatially dependent.
Data
Types

Temporal
Temporal data is simply data that represents a state in time, such as the
land-use patterns of Hong Kong in 1990, or total rainfall in Honolulu on July
1, 2009. Temporal data is collected to analyze weather patterns and other
environmental variables, monitor traffic conditions, study demographic
trends, and so on. This data comes from many sources ranging from manual
data entry to data collected using observational sensors or generated from
simulation models. Below are some examples of temporal data.
Source: http://desktop.arcgis.com/en/arcmap/10.3/map/time/what-is-temporal-
data.htm
Data
Types

Temporal
Source: http://desktop.arcgis.com/en/arcmap/10.3/map/time/what-is-temporal-
data.htm
Data
Types
The 1992 time stamp of the change in the percentage
of cropland (per grid cell) worldwide from 1700 to 1992
in ArcMap. When visualized over time, the percentage
of cropland in some areas increases as time passes
The time stamp from April 18,
1997, of sea surface-
temperature change in
ArcGlobe. The data spans
1997–1998, an El Niño year.
When visualized over time, the
sea surface temperature
changes with each successive
month.
The 1994 time stamp of the oil and gas
production of a production field in
Wyoming in ArcMap. When visualized over
time, the pie charts on the map indicate
the changing oil and gas production rates
from each producing well (red is gas in
barrels of oil equivalent, and green is oil in
barrels). The graph shows production
through time for the entire field: gas (red),
oil (green), and water (blue).

Temporal
A temporal database stores data relating to time instances. It offers
temporal data types and stores information relating to past, present and
future time. The temporal database has two major notions or attributes. 1.
valid time. 2. transaction time. More specifically the temporal aspects
usually include valid time and transaction time. These attributes can be
combined to form bitemporal data.
Valid time is the time period during which a fact is true in the real world.
Transaction time is the time period during which a fact stored in the
database was known.
Bitemporal data combines both Valid and Transaction Time.
Data
Types

Temporal
It is possible to have timelines other than Valid Time and Transaction Time,
such as Decision Time, in the database. In that case the database is called a
multitemporal database as opposed to a bitemporal database. However, this
approach introduces additional complexities such as dealing with the
validity of (foreign) keys.
Temporal databases are in contrast to current databases (not to be confused
with currently available databases), which store only facts which are
believed to be true at the current time.
Source: Wikipedia
Data
Types

Media
Media data is the entirety of a video broken down to text form. Transcripts,
captions, topics, keywords, subjects and tier 2 AdWords, Everything.
Data
Types

Entities
People, places, events, concepts, things
Data
Types

Data Visualizations
What is Data Visualization?

Data Visualizations
Types of Data Visualizations
Map Pie Bar Chart Scatter Plot
Table Timeline Gallery List Tag Cloud

Data Visualizations
Types of Data Visualizations
Slider Range

Data Visualizations
Students should refer to provided links on the course content site
(https://ethiopluginblog.wordpress.com/upload-related-models-here/computer-aided-drafting-and-
data-analysis-cotm2081/ ) for information and tutorials on data visualizations
The Seven Stages of Visualizing Data
Acquire
Obtain the data, whether from a file on a disk or a source over a network.
Parse
Provide some structure for the data's meaning, and order it into categories.
Filter
Remove all but the data of interest.
Mine
Apply methods from statistics or data mining as a way to discern patterns or place the data in
mathematical context.
Represent
Choose a basic visual model, such as a bar graph, list, or tree.
Refine
Improve the basic representation to make it clearer and more visually engaging.
Interact
Add methods for manipulating the data or controlling what features are visible.
(Any given project might require all of the above steps or some of the above steps )
The greatest value of a picture is when it forces us to notice what we never expected to see.
—John Tukey
Source: Visualizing Data by Ben Fry

Data Visualizations
Examples of Data Visualizations
10th century planetary movement

Data Visualizations
1780 time serious line chart William Play fair

Data Visualizations
1786 Bar Chart William Play fair

Data Visualizations
1801 Pichart William Play fair

Data Visualizations
1858 Nightingale Rose polar area diagram

Data Visualizations
Airen Coblen Amsterdam sms messages

Data Visualizations
Airen Coblen usa flight pattern

Data Visualizations
Ben Fry genetics data

Data Visualizations
Ben Fry zip dicod

Data Visualizations
in the air pollution in Spain Victor and Nuria

Data Visualizations
Jawji Chan twits about rail system in France

Data Visualizations
mores and Christopher research network

Data Analysis
"Data analysis is the process of bringing order, structure and meaning to the
mass of collected data. It is a messy, ambiguous, time consuming, creative,
and fascinating process. It does not proceed in a linear fashion; it is not
neat. Qualitative data analysis is a search for general statements about
relationships among categories of data."
Marshall and Rossman, 1990
"…the ways in which the researcher moves from a description of what is the
case to an explanation of why what is the case is the case.“
Hitchcock and Hughes 1995
Data
Analysis

Data Analysis
Data Analysis is the process of systematically applying statistical and/or
logical techniques to describe and illustrate, condense and recap, and
evaluate data. According to Shamoo and Resnik (2003) various analytic
procedures “provide a way of drawing inductive inferences from data and
distinguishing the signal (the phenomenon of interest) from the noise
(statistical fluctuations) present in the data”..
Source: https://ori.hhs.gov/education/products/n_illinois_u/datamanagement/datopic.html
Data
Analysis

Types of Data Analysis
Six Types Of Analyses Every Data Scientist Should Know
•Descriptive
The discipline of quantitatively describing the main features of a collection
of data. In essence, it describes a set of data.
•Exploratory
An approach to analyzing data sets to find previously unknown
relationships.
•Inferential
Aims to test theories about the nature of the world in general (or some part
of it) based on samples of “subjects” taken from the world (or some part of
it). That is, use a relatively small sample of data to say something about a
bigger population.
Source: https://datascientistinsights.com/2013/01/29/six-types-of-analyses-every-data-scientist-
should-know/
Data
Analysis
Types

Types of Data Analysis
Six Types Of Analyses Every Data Scientist Should Know
•Predictive
The various types of methods that analyze current and historical facts to
make predictions about future events. In essence, to use the data on some
objects to predict values for another object.
•Causal
To find out what happens to one variable when you change another.
•Mechanistic
Understand the exact changes in variables that lead to changes in other
variables for individual objects.
Source: https://datascientistinsights.com/2013/01/29/six-types-of-analyses-every-data-scientist-
should-know/
Data
Analysis
Types

Demonstrate Excel In class
(https://ethiopluginblog.wordpress.com/upload-related-models-
here/computer-aided-drafting-and-data-analysis-cotm2081/ ) to Excel
reading materials and tutorials.
Excel

Demonstrate Processing In class
(https://ethiopluginblog.wordpress.com/upload-related-models-
here/computer-aided-drafting-and-data-analysis-cotm2081/ ) to Processing
reading materials and tutorials.
Processing

Data visualization ananlysis_lecture 7

Data visualization ananlysis_lecture 7

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