Summative Assessment 1 - Portfolio based on the 4 formative assessments.
Paul Owhenagbo Alemoh
UEL-DS-7002-46288
Module: Spatial Data Analysis (SDA)
12th February 2023.
Table of Contents
WEEK 1- FORMATIVE ASSESSMENT .............................................................................................. 1
Question 1.1 Think about analyzing a recent digital photograph you took. In what range would
you say its spatial resolution falls? ..................................................................................................... 2
Question 1.2 How would you go about encoding a raster data file that detailed the various landuse options in your country? How would you go about encoding a map of the major roads/rivers in
your county, and why would you choose that method? ...................................................................... 2
Question 1.3 What exactly is topology? ............................................................................................ 2
Question 1.3b To what extent do topological and non-topological data structures differ?................ 3
Question 1.4 To begin, let's define raster and vector GIS models. Can you think of some tangible
objects that stand in for a point, a line, or a polygon? ........................................................................ 3
Question 1.5: What exactly are geospatial models? Describe the differences between raster and
vector GIS models............................................................................................................................... 4
Question 1.6: Explain raster and vector GIS model pros and cons. compare models. ...................... 5
Question 1.7 What is Data structure .................................................................................................. 7
WEEK 2- FORMATIVE ASSESSMENT .............................................................................................. 8
Question 2.1 Which spatial relation cannot be utilized in spatial queries? ........................................ 9
Question 2.2 You want to choose POIs in a GIS dataset that are within a pre-defined study area.
What's your spatial query? .................................................................................................................. 9
Question 2.3 What does the spatial information in a GIS mainly consist of? .................................... 9
Question 2.4 How do you split space-related data? ........................................................................... 9
Question 2.5 Which approaches can be used to formulate a query? ................................................ 10
Question 2.6 Describe the inputs and outputs for answering these questions: "Find all buildings on
properties with a minimum area of 1000 m2 and more than 250m to the highway". Give an
example. ............................................................................................................................................ 11
WEEK 3- FORMATIVE ASSESSMENT......................................................................................... 12
Question 3.1 Make a map with the centroids of each municipality in Säo Paulo state and the state's
outer boundary. ................................................................................................................................. 13
Question 3.2 What is the mean Human Development Index of Brazilian municipalities by state? . 16
Question 3.3 Make a polygon/shapefile of the municipality "Gaucha do Norte" in "Parque do ..... 17
Xingu" indigenous territory. ............................................................................................................. 17
Question 3.4: Which two Acre social housing (MCMV) projects are closest? Each housing project
needs a 10km buffer. ......................................................................................................................... 19
Question 3.5 Which Brazilian municipalities have the fewest and most MCMV housing units
(UH)? Map total dwelling units by municipality. ............................................................................. 20
WEEK 4- FORMATIVE ASSESSMENT..................................................................................... 22
Question 4.1 Compare the benefits and drawbacks of different thematic maps. ............................. 23
Question 4.2: Discuss the best population density mapping approaches for thematic maps. .......... 26
Question 4.3: Choropleth maps usually show derived data. Why? Map the exception to this rule. 27
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Question 4.4 Why classify certain thematic map types but not others? ........................................... 27
Question 4.5: What visual variables and symbol dimensionalities are used for each thematic map
type and how do these differences impact their design and use? ...................................................... 27
Question 4.6: Make a choropleth map of a country's unemployment rate. Create a suitable map
legend. ............................................................................................................................................... 29
Question 4.7 Compare proportional and graduated symbol maps for the same dataset. Create a
suitable legend for both maps. .......................................................................................................... 30
Question 4.8 Choropleth, proportional symbol, graduated symbol, isoline, dot density, dasymetric,
and flow thematic maps require legends. .......................................................................................... 31
Reference .......................................................................................................................................... 33
The table of Figures
FIGURE 1THE VECTOR AND RASTER DIAGRAM SHOWING THE POINT, LINE AND POLYGON ........................................................... 4
FIGURE 2-THE BRAZILIAN MUNICIPALITIES MAP ............................................................................................................... 13
FIGURE 3-HE SELECTED MUNICIPAL................................................................................................................................ 13
FIGURE 4-THE NEW LAYER SELECTED ............................................................................................................................. 14
FIGURE 5-THE NEW LAYER ........................................................................................ ОШИБКА! ЗАКЛАДКА НЕ ОПРЕДЕЛЕНА.
FIGURE 6-THE NEW LAYER WITH CENTROID .................................................................. ОШИБКА! ЗАКЛАДКА НЕ ОПРЕДЕЛЕНА.
FIGURE 7-THE CENTROID ......................................................................................... ОШИБКА! ЗАКЛАДКА НЕ ОПРЕДЕЛЕНА.
FIGURE 8-THE SP_BORDER AND SP_CENTROIDS ........................................................... ОШИБКА! ЗАКЛАДКА НЕ ОПРЕДЕЛЕНА.
FIGURE 9-THE MEAN OF THE MUNICIPALITIES.............................................................. ОШИБКА! ЗАКЛАДКА НЕ ОПРЕДЕЛЕНА.
FIGURE 10-THE NEW LAYER OF THE DISSOLVE .............................................................. ОШИБКА! ЗАКЛАДКА НЕ ОПРЕДЕЛЕНА.
FIGURE 11-THE FILTER MEAN FROM THE SELECTED ........................................................ ОШИБКА! ЗАКЛАДКА НЕ ОПРЕДЕЛЕНА.
FIGURE 12-SHOWING THE STATES AND MHDI OF BRAZIL ............................................... ОШИБКА! ЗАКЛАДКА НЕ ОПРЕДЕЛЕНА.
FIGURE 13-THE INDIGENOUS DIAGRAM ...................................................................... ОШИБКА! ЗАКЛАДКА НЕ ОПРЕДЕЛЕНА.
FIGURE 14-THE GAUCHA AND XINGU NEW LAYER ......................................................... ОШИБКА! ЗАКЛАДКА НЕ ОПРЕДЕЛЕНА.
FIGURE 15-THE AREA OF THE TWO REGION GAUCHA AND XINGU ....................... ОШИБКА! ЗАКЛАДКА НЕ ОПРЕДЕЛЕНА.
FIGURE 16-THE NEW LAYER FROM THE HOUSING AND AC_STATE .................................... ОШИБКА! ЗАКЛАДКА НЕ ОПРЕДЕЛЕНА.
FIGURE 17-THE DISTANCE METRIC ............................................................................ ОШИБКА! ЗАКЛАДКА НЕ ОПРЕДЕЛЕНА.
FIGURE 18-SHOWING THE CLOSEST MUNICIPAL ............................................................ ОШИБКА! ЗАКЛАДКА НЕ ОПРЕДЕЛЕНА.
FIGURE 19-JOIN ATTRIBUTE BY LOCATION.................................................................... ОШИБКА! ЗАКЛАДКА НЕ ОПРЕДЕЛЕНА.
FIGURE 20-MAP OF THE DISTRIBUTION OF HOUSING...................................................... ОШИБКА! ЗАКЛАДКА НЕ ОПРЕДЕЛЕНА.
FIGURE 22-THE UNEMPLOYED RATE OF NIGERIA IN 2020 USING THE CHOROPLETH ............. ОШИБКА! ЗАКЛАДКА НЕ ОПРЕДЕЛЕНА.
FIGURE 23-THE UNEMPLOYED RATE OF NIGERIA 2020 USING THE PROPORTION: DATA NBS DATA ............ ОШИБКА! ЗАКЛАДКА НЕ
ОПРЕДЕЛЕНА.
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WEEK 1- FORMATIVE ASSESSMENT
Paul Alemoh
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Question 1.1 Think about analyzing a recent digital photograph you took. In what range
would you say its spatial resolution falls?
The term "spatial resolution" measures how well one can distinguish between two points in an
image and the level of clarity at which an image can be examined (Gonzalez and Faisal, 2019).
However, In the case of digital images, it counts the individual pixels that make up the image.
Hence, the number of pixels in an image decrease as its spatial resolution does.
To calculate the spatial resolution, spatial resolution = pixels / size, if the dimensions of an
image 3000 pixels by 2000 pixels and the size of the image is 3 inches by 2 inches, the spatial
resolution would be 1000 pixels per inch.
Question 1.2 How would you go about encoding a raster data file that detailed the various
land-use options in your country? How would you go about encoding a map of the major
roads/rivers in your county, and why would you choose that method?
As for land use, the run-length method is a simple way to represent its use, habitat, and land
cover. The encoding of cell values is performed using this raster encoding method in several
different iterations. Hence, in each iteration, each pixel has the same value.
When dealing with roads and rivers.
Rivers and roads can be depicted with the vector lines. This is because the linear nature of such
features makes it easy to color certain area types to represent them.
We use rivers and roads because they stand in for velocity in the real world. Vectors can be
used to accurately portray the direction and magnitude of forces and velocities.
Question 1.3 What exactly is topology?
The topology is a set of rules for how vector entities (points, lines, polygons) relate to one
another in space and time (Theobald, 2001). According to another definition, topology is "the
science and numerical methods correlations used to validate entities through vector geometry
and a series of operations like network analysis and proximity (Longley et al., 2015).
Furthermore, they can be classified into the following, Arc-node, Polygon-arc, right-left
topology.
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Arc-node topology- method for recording the connections between arcs. The topology here
reveals information on the arcs' lengths, directions, and interconnections. Two arcs are joined
if their nodes are the same.
Polygon-arc topology: Establish connections between the arcs and polygons you create and
the surrounding area and neighborhood data. When two polygons share an arc, we call them
neighbors.
Right-left Topology- This is in reference to the local area and the connections between each
polygon. Each arc in this scenario is accompanied by a list of pre-existing polygons on either
side.
All this are because of the relationship between the spatial objects and other objects. such as,
the connection, proximity, inclusion, neighbourhood, Relative direction.
Question 1.3b To what extent do topological and non-topological data structures differ?
Using concepts from topology, topological data structures encode the relationships between
data elements in a way that is consistent with those topological properties. Graphs and
networks are examples of topological data structures because they both represent data as a
collection of nodes that are connected to one another.
When compared to topological data structures, non-topological ones don't use topological
properties to encode information about the relationships between data elements. Arrays, lists,
and tables are all examples of non-topological data structures because of the linear or twodimensional data storage and organization they provide. Hence, when it comes to storing and
organizing information, non-topological data structures shine when the data in question lacks
a complex or hierarchical relationship structure.
Question 1.4 To begin, let's define raster and vector GIS models. Can you think of some
tangible objects that stand in for a point, a line, or a polygon?
When it comes to GIS, both raster and vector data model are right up there in terms of age
and prevalence (Tomlin, 1990; Goodchild, 1992)
Raster data represents spatial features as a grid of cells, with each cell containing a value
that represents the feature at that location in form of an image or lattice (Lovelace, Nowosad
and Muenchow, 2019). For example, a satellite image is a raster dataset, with each pixel
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representing the ground surface at that location. Furthermore, continuous data, such as
altitude, temperature, reflected or emitted electromagnetic radiation, are ideal candidates for
its capture, analysis, and visualization.
Vector model represents spatial features as points, lines, and polygons. Moreover, it is a data
model that uses points and their x-, y- coordinates to construct spatial features.
Examples of objects representing points in a GIS dataset might include individual locations
such as cities or landmarks. Examples of objects representing lines might include roads,
rivers, or flight paths. Examples of objects representing polygons might include lakes,
countries, or land use zones.
Figure 1-The vector and Raster diagram showing the point, line and polygon
Question 1.5: What exactly are geospatial models? Describe the differences between raster
and vector GIS models.
Geospatial models are mathematical models that are used to analyze and predict patterns and
trends in data that have a geographical or spatial component. These models can be used to
analyze data such as population density, land use, and the distribution of natural resources.
They are often used in fields such as geography, environmental science, and urban planning.
Geospatial models can be created using a variety of techniques, including statistical analysis,
machine learning, and spatial analysis.
Geographic Information System (GIS) models can be broadly classified into two categories:
raster and vector.
Raster models represent the earth's surface as a grid of cells, known as a raster, with each
cell containing a value such as elevation or land cover type (Bernhardsen, 2002). Hence,
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Continuous phenomena like surface elevation or temperature are well represented (White,
2020).
Uses of Raster data models
A good example is the encoding of the GIS data (Chaudhuri and Clarke, 2013)(Slocum et
al., 2004).
Such as;
 it shows how the land is shaped. Example is the digital elevation model.
 Visualizations and products based on elevation.
 Information gathered by means of remote sensing instruments, as in aerial and radar
photography.
 It is use in meteorological in predicting the amount of rain or any other weather
condition.
 It is use in the population world dataset in the area of Administrative boundaries. .
 Cellular automata models such as SLEUTH
Vector models, instead, use points, lines, and polygons to depict Earth's surface. Discrete
phenomena, such as political boundaries or land use classifications, are frequently
represented by vector data.
Both raster and vector models have their own strengths and weaknesses, and the choice
between the two depends on the specific needs of the analysis. Raster data is generally easier
to work with, but vector data provides more precise representations of features on the earth's
surface.
Question 1.6: Explain raster and vector GIS model pros and cons. compare models.
Advantages of raster models:

It is use in representing continuous occurrences such as surface elevation or
temperature.

With the right software, raster data may be swiftly and easily evaluated.

Raster data can be easily displayed and visualized using maps or 3D models.
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Disadvantages of raster models:

Raster data can be large and unwieldy, especially when representing fine-scale
features or large areas.

Raster data is not as precise as vector data, especially when representing features with
complex shapes or small size.
Advantages of vector models:

Vector data is more precise than raster data, especially when representing features
with complex shapes or small size.

It is well-suited for representing discrete trends such as political boundaries or land
use categories.

Vector data can be easily edited and updated.
Disadvantages of vector models:

Vector data can be more difficult to work with than raster data, especially when
performing complex analyses.

Vector data can be more computationally intensive to process than raster data.

Vector data is not as well-suited for representing continuous phenomena such as
surface elevation or temperature.
Compare raster Model and Vector Model

Raster data is a grid of cells, each of which has a value. It is good for continuous data,
such as elevation, and is easy to display and analyze. Vector data is a series of points,
lines, and polygons. It is good for discrete data, such as land use, and is more precise
than raster data (Bernhardsen, 2002).

Raster data is well-suited for representing continuous phenomena, such as surface
temperature or elevation, while vector data is better suited for representing discrete
phenomena, such as political boundaries or land use categories (Longley et al., 2005).

Vector data models center on the geographic feature, while raster data models center
on the location.
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
The question, "What do I know about this geographical feature?" lends itself better to
the vector data model. The raster data model explains "what geographical
phenomenon happens here."

While raster store values in rows and columns of cells, the vector model uses x and y
coordinates to represent geographical features.

The vector data model delimits what's possible. The raster data model does not have
any predetermined limits.
Question 1.7 What is Data structure
There are several types of data structures used in GIS (Geographic Information Systems),
including vector data structures, raster data structures, and topological data structures.
Vector data structures represent geographic features as points, lines, or polygons, and store
the coordinates of these features. Vector data structures are used to represent discrete
features, such as roads, buildings, or land parcels. (Longley et al., 2015)
Raster data structures represent geographic features as a grid of cells, with each cell
containing a value that represents an attribute of the feature. Raster data structures are used to
represent continuous surfaces, such as elevation or land cover (DeMers, 2008).
Topological data structures are used to represent the spatial relationships between
geographic features. These data structures store information about how features are connected
and how they share common boundaries.
GIS software typically includes tools for creating, storing, and manipulating these data
structures, as well as tools for analyzing and visualizing the data they contain.
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WEEK 2- FORMATIVE ASSESSMENT
Paul Alemoh
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Question 2.1 Which spatial relation cannot be utilized in spatial queries?
◼ Distance-based relation ◼ Spatial autocorrelation ◼ Topological relation ◼ Direction
relation
Spatial autocorrelation is not a spatial relation that can be used in spatial queries. Spatial
autocorrelation refers to the statistical dependence of spatial attributes and is a measure of the
similarity between features based on their spatial location (Diniz‐Filho, Bini and Hawkins,
2003).
Question 2.2 You want to choose POIs in a GIS dataset that are within a pre-defined study
area. What's your spatial query?
SELECT * FROM POIS WHERE ST_Within (pois.geometry, study_area.geometry) =true;
The purpose of this query is to select all the point of interest in the table that are within the
study area. While the ST_within function returns true if and only if the condition of the
geometry argument is met.
Question 2.3 What does the spatial information in a GIS mainly consist of?
Spatial information in a GIS mainly consists of location-based data, such as coordinates,
points, lines, and polygons, that represent geographical features on the Earth's surface, such
as:
i.
Physical features, such as mountains, rivers, lakes, oceans, and coastlines.
ii.
Human-made features, such as roads, buildings, bridges, airports, and infrastructure.
iii.
Administrative features, such as political boundaries, census tracts, and voting
districts.
iv.
Natural resources, such as forests, minerals, and wildlife habitats.
v.
Demographic features, such as population density, income, and education levels.
Question 2.4 How do you split space-related data?
Spatial information can be divided in various ways, depending on the nature of the data and
the goals of the analysis. Here are a few examples of how spatial information can be divided:
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1. By feature type: Spatial information can be divided by the type of feature it
represents, such as points, lines, or polygons. For example, a GIS dataset might
contain separate layers for roads (lines), buildings (polygons), and rivers (lines).
2. By attribute: Spatial information can also be divided by its attribute data. For
example, a GIS dataset might contain separate layers for different land use categories,
such as residential, commercial, and industrial areas.
3. By scale: Spatial information can be divided by the scale at which it was collected or
is being used. For example, a GIS dataset might contain separate layers for different
scales, such as a global layer showing country boundaries and a local layer showing
street names and addresses.
4. By time: Spatial information can also be divided by the time- period it represents. For
example, a GIS dataset might contain separate layers for different time periods, such
as one layer showing land use patterns in the year 2010 and another layer showing
land use patterns in the year 2023.
5. By geography: Spatial information can be divided by the geographical region it
covers. For example, a GIS dataset might contain separate layers for different
countries, states, or provinces.
Question 2.5 Which approaches can be used to formulate a query?
There are several approaches that can be used to formulate a query in a Geographic
Information System (GIS). Here are a few examples: (GIS Geography, 2018).
1. Structured Query Language (SQL): SQL is a standard programming language used
to communicate with databases, including GIS databases. SQL can be used to
formulate complex queries that specify the criteria for selecting, sorting, and
organizing data.
2. Query builders: Many GIS software applications include graphical query builders
that allow users to construct queries using a point-and-click interface. These query
builders typically provide a range of options for specifying the criteria for selecting
and organizing data.
3. Spatial operators: Many GIS software applications also include a range of spatial
operators that allow users to specify spatial relationships in their queries. For
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example, a query might use a spatial operator to select all features that are within a
certain distance of a specific point, or that intersect with a specific polygon.
4. Map selection tools: Some GIS software applications allow users to select features
on a map and use the selection as the basis for a query. For example, a user might
select all the buildings in a specific area of a map and then use the selection to retrieve
attribute data for those buildings.
5. Predefined queries: Some GIS software applications come with a set of predefined
queries that users can run with a single click. These queries might be designed to
retrieve commonly needed data or to perform common analysis tasks.
Question 2.6 Describe the inputs and outputs for answering these questions: "Find all
buildings on properties with a minimum area of 1000 m2 and more than 250m to the
highway". Give an example.
Firstly, Find all buildings which are located on parcels with a minimal area of 1000 m2 and a
distance of more than 250m to the highway, the inputs are:
1. A GIS dataset containing building locations and attributes, including the parcel on
which each building is located. For example, this dataset might include a point feature
class representing the location of each building, with attributes such as building name,
type, and year constructed.
2. A GIS dataset containing parcel boundaries and attributes, including the area of each
parcel. For example, this dataset might include a polygon feature class representing
the boundaries of each parcel, with attributes such as parcel ID, owner, and area.
3. A GIS dataset containing the location and shape of the highway. For example, this
dataset might include a line feature class representing the location and shape of the
highway, with attributes such as road name and speed limit.
The output of the query would be a list or map of the buildings that meet the specified
criteria. The output could be presented in various formats, such as a table showing the
attributes of the selected buildings, or a map with the selected buildings highlighted. The
output could also include additional information, such as the distance of each building to the
highway. For example, the output might include a column in the table or a label on the map
indicating the distance of each building to the nearest point on the highway.
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WEEK 3- FORMATIVE ASSESSMENT
Paul Alemoh
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Question 3.1 Make a map with the centroids of each municipality in Säo Paulo state and the
state's outer boundary.
Figure 2-The Brazilian municipalities map
Figure 3-The selected municipal
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Figure 4-The new layer Selected.
Figure 5-The new layer with centroid
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Figure 6-The new layer with centroid
Figure 7--The Sp_border and sp_centroids
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Question 3.2 What is the mean Human Development Index of Brazilian municipalities by
state?
Figure 8-The Mean of the Municipalities
Figure 9-The new layer of the dissolve
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Figure 10-The filter mean from the selected
Figure 11-Showing the states and MHDI of Brazil
Question 3.3 Make a polygon/shapefile of the municipality "Gaucha do Norte" in "Parque do
Xingu" indigenous territory.
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Figure 12-The Indigenous diagram
Figure 13-The gaucha and Xingu new layer
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Figure 14-The area of the two region gaucha and xingu
Question 3.4: Which two Acre social housing (MCMV) projects are closest? Each housing
project needs a 10km buffer.
Figure 15--The new layer from the housing and AC_state
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Figure 16-The Distance Metric
Figure 17-Showing the closest municipal.
Question 3.5 Which Brazilian municipalities have the fewest and most MCMV housing units
(UH)? Map total dwelling units by municipality.
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Figure 18-Join Attribute by location
Figure 19-map of the distribution of Housing
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WEEK 4- FORMATIVE ASSESSMENT
Paul Alemoh
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Question 4.1 Compare the benefits and drawbacks of different thematic maps.
Thematic maps, also known as subject maps or special purpose maps, are tailored to depict a
certain topic or theme. Instead of serving as a general reference or overview of a physical area,
the primary goal of thematic maps is to convey information about a specific topic. Such as,
population density, political boundaries, and vegetation (Slocum et al., 2004).furthermore, the
information depicted on these maps is conveyed using symbols, colors, and patterns. We begin
by listing the several themed map types that are often utilized (Slocum et al., 2004; Tyner,
2010). Moreover, it will serve to illustrate the wide range of subject matter covered by
cartographic materials, such as Choropleth, Proportional, Dot map, Isopleth, Flow Map
Choropleth
This are maps where information is represented graphically by means of color or pattern at the
level of a geographic unit such as a census tract or county. Class intervals are a great way to
make data values easier to read by dividing the data into three or more groups, and by using
natural breaks (MacEachren, 1995) or quantiles (Brewer and Pickle, 2002). Example of such
map can be seen in fig
Uses of Choropleth

The Measurement of Human Population Size and Density

Estimating Revenue and Sales Volume Through Visualization

Estimating Population (Education, Housing, Per Capita Income, Labor, etc.)
Advantage

By visually grouping your data into distinct colors, choropleth maps make it easy to
grasp massive datasets in a short amount of time, even when spanning multiple
geographic regions.

Given their widespread adoption, choropleth maps require almost no introduction or
specialized expertise.

With tools like Maptive, creating a choropleth map is simple.

Highly effective in arranging variables into administrative units.
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Disadvantages

Choropleth maps work best with broad overly general information, rather than discrete
numbers or other details.

Choropleth maps can be used to manipulate data and mislead people if the wrong
individuals get their hands on them because the data classes are set by the map's
designer.

Maps assume on enumeration area has the same value despite possible fluctuation.

The rapid change at the enumeration area boundary could be deceiving (Mwaikusa,
2022)
Dot Map
This are maps in which the number of dots used to represent a commodity's distribution is
thought to correspond to a certain value (Dent, 1999). Hence, they are usually based on spatial
point rather than areas. Example of such Map is seen below
Figure 20-- the Dot density map of the Unemployed in Nigeria 2020
Uses of Dot Map

Presenting Massive Amounts of Data that Are Scattered Across Multiple Locations

Density and spatial pattern analysis

Utilizing Monochrome Maps for Publication or Display
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Advantage
Dot density has an advantage over other methods of presenting variation and geographical
density in that, it is simple to understand by map users and provides a clear representation of
the underlying data.

For people who are colorblind or prefer black-and-white maps, dot density maps are an
excellent alternative.

Dot density maps provide a more accurate representation than other maps such as heat
maps.

One can visualize either specific numbers or categories, unlike choropleth maps.

A clear depiction of the dots on the map makes it simpler to draw the original data, and
in some cases, multiple data sets can be displayed at once.
Disadvantage:
Dot density maps have a flaw. since some software randomly places the dots inside the
enumeration area, which means the dots are likely to be placed far from the phenomena that
they are meant to depict. In addition, picking a single dot value that is visually appealing in
both high-density and low-density regions is challenging due to the wide range of data points
(Dent, 1999). Moreover, understanding context is necessary for dot density maps. As a result,
it is crucial to think about readability and border display when adjusting Dot density map.
Proportional Map
Symbols with sizes proportional to a variable's value are used to represent data points (Slocum
et al., 2004). Gradient symbols, whose sizes are proportional to the values they indicate, are
used to denote categories rather than single numbers (Mapbox, 2022).
They can be helpful for illustrating the regional distribution and magnitude of a variable.
One of the benefits of using a proportional symbol map is that it can display the magnitude of
a variable in relation to other variables, and it can also process enormous volumes of data.
However, when the data is not appropriately scaled, symbols not properly sized, and the scale
does not contain a zero value, proportional symbol maps can be deceptive. Example as shown
in figure 23
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Isopleth maps are one of two types of isorhythmic mapping; their values reflect a standard rate
per unit area and the data are obtained from this (Dent et al., 2009). Lines are used in this
method to show groups of the same value for a variable. The spatial distribution of continuous
variables like height, temperature, and precipitation can be displayed effectively using them.
Question 4.2: Discuss the best population density mapping approaches for thematic maps.
Choropleth map
Population density can be shown in several different ways on thematic maps. Many different
types of maps can be made, but choropleth maps are a popular choice. The National bureau of
Statistics can provide accurate population estimates since they are calculated at the
administrative level. To show population density, choropleth maps employ color. This
approach is not only user-friendly but also effective at rendering visible patterns of population
density across geographic regions. However, if the data is not properly scaled or if the area is
not consistent, a choropleth map might be deceiving. As seen in Figure 22
Dot Map
The usage of a dot map represents the second approach. Dot maps, also known as point density
maps, depict the distribution of a population by using a grid of dots to denote individual
residents. A clearer picture of the frequency of uncommon occurrences can be obtained using
this strategy. Large datasets are no problem for dot maps, and neither is pinpointing their
precise location. However, point maps can become unintelligible if there are too many points
of interest displayed. This can be shown on figure 20
Proportional Map
Population density can be depicted on maps by means of proportional symbols. This technique
use geometric shapes like circles and squares to graphically express the density of a region's
human population. This approach can handle vast volumes of data and is useful for visualizing
population densities across regions. However, if the data is not scaled properly or the symbols
are not the right size, a map with proportional symbols can be misleading. Figure 22
Isoline Map
Isoline is a map with a three-dimensional perspective also called an isoline map. In this
technique, lines are used to depict regions with constant population densities. This approach is
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effective for depicting population density patterns over space, and it can process massive
volumes of information.
Question 4.3: Choropleth maps usually show derived data. Why? Map the exception to this
rule.
Since choropleth maps are typically used to display aggregated data down to the level of a
geographic unit such as a census tract or county, derived data should nearly always be displayed
(Slocum et al., 2004). This is done so that data may be accurately compared across the entire
map, despite differences in the size of the geographic units. A choropleth map depicting
population density for instance, might designate the density of a certain population in terms of
inhabitants per square kilometer or square mile across various census tracts or counties. This
provides for a more precise comparison of population density over the entire map as opposed
to simply comparing raw population statistics. Additionally, putting derived data on choropleth
maps helps to accommodate for differences in the size of the geographic units, and permits
more accurate comparison of the data across the map.
Question 4.4 Why classify certain thematic map types but not others?
Classification is necessary for certain types of thematic maps, such choropleth and isorhythmic
maps, because they depict data that has been aggregated to the level of a geographic unit, like
a census tract or county. Classification aids in organizing data into meaningful groups for
simpler analysis and comparison. It also facilitates reliable comparisons throughout the entire
map by controlling for differences in the size of the geographic units.
To create a choropleth map, for instance, data is categorized by clustering census tracts or
counties into distinct population density categories. This provides for a more precise
comparison of population density over the entire map as opposed to simply comparing raw
population statistics.
Question 4.5: What visual variables and symbol dimensionalities are used for each thematic
map type and how do these differences impact their design and use?
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Choropleth maps are type of statistical visualization in which data is color- or pattern-coded
to show aggregated findings at the level of a geographic area, such as a census tract or county.
In most cases, color is utilized as the visual variable, and the symbol's dimensionality is area;
this means that the size of the geographic unit is held constant while the color or pattern varies
to convey the data. The number of classes, the color scheme, and the data classification
technique all influence choropleth map creation and interpretation. Lighter colours typically
represent smaller numbers, while darker colors represent larger numbers when displaying
quantitative data. (White, 2017). However, if the data is not appropriately scaled or if the
regions are not homogeneous, the resulting choropleth map could be deceptive. As seen in
figure 22
Proportional Maps
Circles and squares, among other symbols, are used to represent the absolute value of a variable
at a given point on a map that employs proportional symbols. Position, size, and color are the
visual factors exploited. To create a proportional symbol map, simply position a symbol at the
location of the variable and scale it accordingly. Each symbol's size is often determined such
that its area is proportionate to the variable, while other, less direct approaches (such as
categorizing symbols as "small," "medium," and "big") are also employed (Dent et al., 2009).
The relative magnitude of a variable throughout a region can be shown using location, size,
and color; however, this can be deceptive if the data is not appropriately scaled or if the symbols
are not properly proportioned. Figure 23
Dot maps
Dot maps, also referred as dot density maps, are maps in which individual dots reflect the
prevalence of some variable across a certain area. Positional visual variables are designed by
putting dots in the spots where the variable is present. On dot maps, the occurrence of a
phenomenon is represented by "more" dots, rather than the size of a symbol, as is the case with
proportional maps (GIS Geography, 2018). The use of position and size allows for the depiction
of the distribution of a variable throughout a region and is particularly effective for
demonstrating the distribution of unusual events. However, if there are too many dots on the
map, it can be difficult to read and interpret.
isopleth maps, which are a type of contour map, uses lines to indicate areas of equal value of
a variable. As the sign only has one dimension, the only visual variable that can be changed is
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its location. Furthermore, Isopleth maps are used to visualize the spatial distribution of a
variable by drawing lines between locations with the same value of that variable. However, if
the lines on an isopleth map are too close together, it makes the map hard to read and interpret.
Flow maps, often called streamlined maps, are used to depict the flow of people, goods, or
information between different geographic areas. Position and orientation are the only visual
variables, and the symbol is flat (Dent et al., 2009). Drawing lines from origin to destination
on a map, and varying line thickness to show the magnitude of the variable in question, is a
common practice when creating a flow map. Although location and direction can be used to
visually represent the direction and magnitude of a variable, maps with too many lines or close
lines can be difficult to read and interpret.
Overall, the design and functionality of various thematic map types are affected by the
employment of a variety of visual factors and symbol dimensions. Depending on the
information at hand and the nature of the inquiry, different data points and symbols will be
prioritized. Various combinations are best suited for various kinds of information and analyses.
Figure 21--Example of a flow diagram using Nigeria.
Question 4.6: Make a choropleth map of a country's unemployment rate. Create a suitable
map legend.
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Figure 22-The unemployed rate of Nigeria in 2020 using the Choropleth.
Question 4.7 Compare proportional and graduated symbol maps for the same dataset. Create
a suitable legend for both maps.
Figure 23-The Unemployed rate of Nigeria 2020 using the proportion: data NBS data.
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Figure 24-The Unemployed rate of Nigeria 2020 using the Graduated: data NBS data
Question 4.8 Choropleth, proportional symbol, graduated symbol, isoline, dot density,
dasymetric, and flow thematic maps require legends.
Choropleth
Graduated
0-50%
Proportionate
0-50%
25%
51-100%
51-100%
50%
101-150%
101-150%
75%
Isoline
55m
60m
65m
100%
Dot Density
10,000
1,000,000
2,000,000
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Dasymetric
Flow
1-100
0-100 Cars
101-300
101-200 Cars
301-1,000
201-300 Cars
1001-3,000
301 cars and above
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