This is teaches how to implement a data science project using a machine learning approach.

1. Madagascar
Madagascar
Namibia
Namibia
M
ozam
bique
M
ozam
bique
ZambiaZambia
DemocraticDemocratic
RepublicRepublic
of Congoof Congo
AngolaAngola
TanzaniaTanzania
KenyaKenya
EthiopiaEthiopiaSouthSouth
SudanSudan
SudanSudan
EgyptEgyptLibyaLibya
ChadChad
NigerNiger
MaliMali
AlgeriaAlgeria
Morocco
Morocco
TunisiaTunisia
Mauritania
Mauritania
Guinea
Guinea
CoteCote
DevoirsDevoirs
Liberia
Liberia
BeninBenin
GhanaGhana
BurkinaBurkina
FasoFaso
TogoTogo
ZimbabweZimbabwe
Som
alia
Som
aliaCARCAR
Botswana
Botswana
NigeriaNigeria
Cameroon
Cameroon
W
estern
Sahara
EriteriaEriteria
Congo
Uganda
Uganda
M
alawi
Gabon
Senegal
Guinea
Bisau
South AfricaSouth Africa

3. Northern Cape
Western Cape
Eastern Cape
Kwa-Zulu Natal
Free State
North-West
Limpopo
Mpumalanga
Gauteng
South Africa
has nine
provinces

4. Western Cape
Northern Cape
Eastern Cape
Kwa-Zulu Natal
Free State
North-West
Limpopo
Mpumalanga
Gauteng

5. And, more 1000
provinces located
across the nine
provinces

6. Police Stations: Names, Locations and Boundaries

7. Police Stations and Boundaries
South African police service provides data
on the following:
1. Name of the police station;
2. Geo-Coordinates of each station;
3. Geographical boundary of each station;
4. Crime reported during the year;
This information can only be accessed
from census data
But it will be nice to have data on the areas,
which fall under the jurisdiction of each
station.

8. The Datasets
Additional data
(to be sourced on)
Dataset D
!
Data on the location
(i.e. geographical
coordinates) of the
police stations across
the country.
Dataset C
Data on the names of
police station and the
population that fall
under their
jurisdiction.
Dataset B
Data on crime
reported across the
country and the
respective police
stations.
Dataset A
Remember
For this analysis, we already have Dataset A | Dataset B | Dataset C
1. Population
2. Sex
3. Race
4. Education
5. Dwelling Type
6. Location Type
We need to source for more dataset. See dataset D box.

9. The Datasets
Dataset D
Data on the location
(i.e. geographical
coordinates) of the
police stations across
the country.
Dataset C
Data on the names of
police station and the
population that fall
under their
jurisdiction.
Dataset B
Data on crime
reported across the
country and the
respective police
stations.
Dataset A
1. Population
2. Sex
3. Race
4. Education
5. Dwelling Type
6. Location Type

10. StatsSA is in charge of conducting the national
census in South Africa. It makes available data
at the:
1. National Level
2. Provincial level
3. District Level
4. Municipal Level
5. Wards & Small Areas
National MapProvincial MapDistrict MapMunicipality MapSmall Area
Small Areas (SALs) map boundaries are closer to
that of the police stations
There is the need to combine the SALs to match
the police station segment.
The challenge is that no map coming from the
national census matches that of the police
stations.

11. Small Areas Data
PopulationPopulation
SexSex
RaceRace
Location TypeLocation Type
DwellingDwelling
EducationEducation
Male Female
Black African White Coloured Indian
Urban Rural Farm
Black African White Coloured Indian
Primary, Secondary, Tertiary, Certificate/Diploma
Total population

12. The task is to combine and trim the small areas map to match police station
boundaries.By doing that, we will have the data for the total populatio of interest
Let see the process in action.
Data+
Data+
Data+
Data+
Data+
+Data
= Total Data
Kenhardt
Police Station

14. To be continuedTo be continued

15. https://www.youtube.com/channel/UCiUYC06l3X2yQZ8LN5j1sFg?

16. Data Source https://data.code4sa.org
Heart of the Jungle by M. MandelaSound