1. ADAMA SCIENCE AND TECHNOLOGY UNIVERSITY
SCHOOL OF APPLIED NATURAL SCIENCE
DEPARTMENT OF GEOLOGY
Msc. Hydrogeology
Seminar Topic: Spatial Data Analysis and Data Visualization
By
Wakgari Yadeta
ID/NO: - PGR/28043/15

2. Spatial Data Analysis
and
Data Visualization

3. Contents
Introduction
Data Science
spatial data science
Spatial data
Spatial Analysis
Data Visualization

4. Introduction
• Spatial analysis the crux of GIS because it includes all of the
transformations, manipulations, and methods that can be applied to
geographic data to add value to them, to support decisions, and to
reveal patterns and anomalies that are not immediately obvious
• Spatial analysis is the process by which we turn raw data into useful
information,
• In a world where decision-making is increasingly influenced by data,
it is important to understand how spatial data science can help.
• Data visualization, the graphic representation of data.

5. DATA SCIENCE
• Data science is the study of information and its source,
collection, organization, processing, and presentation. It is an
interdisciplinary area that incorporates elements of statistics,
computer science, operations research, mathematics, and
programming.

6. SPATIAL DATA SCIENCE
• Spatial data science (SDS) is a subset of Data Science that focuses on the
unique characteristics of spatial data, moving beyond simply looking at
where things happen to understand why they happen there.
• SDS treats location, distance & spatial interactions as core aspects of the
data using specialized methods & software to analyze, visualize & apply
learnings to spatial use cases.

7. • Spatial data science is a subset of data science. It’s where data
science intersects with GIS with a key focus on geospatial data and
new computing techniques.

8. SPATIAL DATA
• Spatial data is any type of data that directly or indirectly
references a specific geographical area or location.
Sometimes called geospatial data or geographic information,
spatial data can also numerically represent a physical object in
a geographic coordinate system.

9. Spatial Analysis
• Spatial analysis is a set of techniques for analyzing
spatial data.
• The results of spatial analysis are dependent on
the locations of the objects being analyzed.
• Spatial analysis or spatial statistics includes any of the
formal techniques which study entities using their
topological, geometric, or geographic properties.

10. • Spatial Analysis includes revealing and clarifying processes,
structures of spatial phenomena that occur on the Earth’s
surface.
Spatial analysis the crux of GIS because it includes all of the
• Transformations,
• Manipulations,
Methods that can be applied to geographic data to add value to
them, to support decisions, and to reveal patterns and anomalies
that are not immediately obvious

11. • It is the process by which we turn raw data into useful
information.
• Spatial analysis is a set of methods whose results change
when the locations of the objects being analyzed.

12. Spatial analysis process
Data modeling is the process of creating a visual representation of
either a whole information system or parts of it to communicate
connections between data points and structures.

13. DATA VISUALIZATION
In short, data visualization is the representation of data in a graphical or
pictorial format.
Data visualization is the visual presentation of data or information.
The goal of data visualization is to communicate data or information
clearly and effectively to readers.

14. Data visualization
• Maps are the primary tools by which spatial relationships
and geographic data are visualized.
• There are several key elements that should be included
each time a map is created in order to aid the viewer in
understanding the communications of that map and to
document the source of the geographic information used.

15. • The basics of map layout elements are:
1. Data Frame
2. Legend
3. Title
4. North Arrow
5. Scale
6. Graticule, Border and Neatlines

16. 1. Data Frame
• The data frame is the portion of the
map that displays the data layers.

17. 2. Legend
• Descriptions detailing any color
schemata, symbology or
categorization is explained here.
• Without the legend, the color
scheme on the map would make
no sense to the viewer.
• The legend tells the viewer that
the lighter the color, the longer
the last recorded date of fire has
been.

18. 3. Title
• The title is important because it instantly gives the viewer a succinct
description of the subject matter of the map.
• 4. North Arrow
• The purpose of the north arrow is for orientation. This allows the viewer to
determine the direction of the map as it relates to due north.
• Most maps tend to be oriented so that due north faces the top of the page.
• There are exceptions to this and having the north arrow allows the viewer to
know which direction the data is oriented

19. 5. Scale
• The scale explains the relationship of the data frame extent to the real
world. The description is a ratio.
• This can be shown either as a unit to unit or as one measurement to
another measurement.
• Therefore a scale showing a 1:10,000 scale means that every one paper
map unit represents 10,000 real world units.

20. 6. Graticule, Border and Neatlines:
• Neatlines are finer lines than borders,
drawn inside them and often intra-
parallelism, rendered as part of the
graticule; used mostly for decoration
• The map border is a line that defines
exactly the edges of the area shown on
the map
• A graticule is a network of lines
overlain on a map to make spatial
orientation easier for the reader. the
lines of a graticule can represent the
earth’s parallels of latitude and
meridians of longitude.

21. • Typically, data is visualized in the form of a chart, infographic,
diagram.
• Data visualization is the practice of translating information into a
visual context, such as a map or graph, to make data easier for the
human brain to understand and pull insights from.
• Data visualization is one of the steps of the data science process,
which states that after data has been collected, processed and
modeled, it must be visualized for conclusions to be made.

22. What is data visualization used for?
• Data visualization can help both you and your audience interpret and
understand data.
• Data visualizations often use elements of visual storytelling to
communicate a message supported by the data.

23. Why is data visualization important?
Data visualization provides a quick and effective way to communicate
information in a universal manner using visual information.
the ability to absorb information quickly, improve insights and make faster
decisions;
an increased understanding of the next steps that must be taken to improve
the organization;
an improved ability to maintain the audience's interest with information
they can understand;
an easy distribution of information that increases the opportunity to share
insights with everyone involved;
eliminate the need for data scientists since data is more accessible and
understandable; and
an increased ability to act on findings quickly and, therefore, achieve
success with greater speed and less mistakes.

24. • Data visualization is one of the most important steps to consider
during any GIS project. The way in which your data is visualized will
directly impact your audience's interpretation of the final product.
Visualization elements include coloring, map extent, labels,
boundaries, interactivity, and more.

25. • There are myriad different types of charts, graphs and other
visualization techniques that can help analysts represent and relay
important data. Let’s take a look at feiw of the most common ones:

26. 1. Bar Graph
• The bar chart or bar graph is one of the most common data
visualizations. Bar graph are used to compare data along two axes.
One of the axes is numerical, while the other visualizes the categories
or topics being measured.

28. 2. Line Graph
• A line graph is designed to reveal trends, progress, or changes that
occur over time. As such, it works best when your data set is
continuous rather than full of starts and stops.
• Like a column chart, data labels on a line graph are on the X-axis while
measurements are on the Y-axis.

30. 3. Pie Chart
• pie charts are similar to bar charts in that they represent categorical
data, this is where the similarities end. The main difference is that bar
charts represent numerous categories of data, while pie charts
represent a single variable, broken down into percentages or
proportions.

32. • 4. Scatter Plot
• This type of visualization is also called a scattergram, and it represents
different variables plotted along two axes.

34. 5.Venn Diagram
• is a data visualization type that aims to compare two or more things by highlighting what they have
in common. The most common style for a Venn diagram is two circles that overlap. Each circle
represents a concept and the area that connects them is what the two have in common.

35. 6.box plot
• A box plot is a chart that shows data from a five-number summary
including one of the measures of central tendency.

37. Generally
• Data visualization is the process of representing data in a graphical or
pictorial format. This allows people to see relationships and patterns
that would be difficult to discern from raw data. Data visualization
can be used to communicate complex ideas quickly and effectively. It
allows people who have never seen raw numbers before or even
understood what they were looking at in a graph to quickly grasp
complex ideas through pictures that really shine lights on
relationships between variables allowing you to make sense out
something whose significance might otherwise go unnoticed by
readers without some sort of background knowledge.