DESIGN FOR A LARGE STATED OWNED WATER UTILITY THAT

DESIGN FOR A LARGE STATED OWNED WATER UTILITY THAT INCORPORATES

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Task 2 (Worth 30 marks)

Research the relevant literature on how big data analytics capability can be incorporated into a data warehouse architecture. Note Chapter 2 Data Warehousing and Chapter 6 Big Data and Analytics of Sharda et al. 2014 Textbook will be particularly useful for a nswering some aspects of Task 2.

 

Task 2.1 Provide a high level data warehouse architecture design for a large stated owned water utility that incorporates big data capture, processing, storage and presentation in a diagram called Figure 1.1 Big Data Analytics and Data Warehouse Combined.

 

Task 2.2 Describe and justify the main components of your proposed high level data warehouse architecture design with big data capability incorporated presented in Figure 1.1 with appropriate in-text referencing support (about 750 words).

 

Task 2.3 Identify and discuss the key security privacy and ethical concerns for organisations within a specific industry that are already using a big data analytics and algorithmic approach to decision making with appropriate in-text referencing support (about 750 words).

 

Task 3 (Worth 30 marks)

 

LAPD Crime Analytics Unit would like to have a Crime Events dashboard built with the aim of providing a better understanding of the patterns that are occurring in relation to different crimes across the 21 Police Department areas over time in the City of Los Angeles. In particular, they would like to see if there are any distinct patterns in relation to (1) types of crimes, (2) frequency of each type of crime across each of the 21 Police Department areas for years 2012 through to first quarter of 2016 based on the  data set. Note this is a large data set containing over 1 Million records. This Crime Events dashboard will assist LAPD to better manage and coordinate their efforts in catching the perpetrators of these crimes and be more proactive in preventing these crimes from occurring in the first place.

 

The LAPD Crime Analytics Unit wants the flexibility to visualize the frequency that each type of crime is occurring over time across each of the 21 Police Department areas/districts in the City of Los Angeles. They want to be able to get a quick overview of the crime data in relation to category of crimes, location, date of occurrence and frequency that each crime is

 

occurring over time and then be able to zoom in and filter on particular aspects and then get further details as required.

 

LA Crimes Data Set Data Dictionary

variable name

type

Description

year_id

1.   character

Original dataset id

date_rptd

2.   date

Date crime was reported

dr_no

3.   character

Count of Date Reported

date_occ

4.   date

Date crime occurred

time_occ

5.   date

Time crime occurred on a day

area

6.   character

Area Code

area_name

7.   character

Area geographical location

rd

8.   character

Nearby road identifier

crm_cd

9.   character

Crime type code

crm_cd_desc

10. character

Crime type description

Status

11. character

Status code

status_desc

12. character

Status outcome of crime

location

13. character

Nearby address location

cross_st

14. character

Nearby cross street

lat

15. numeric

Latitude of crime event

long

16. numeric

Longitude of crime event

year

17. numeric

Year of crime occurred

month

18. numeric

Month of crime occurred

day_of_month

19. numeric

Day of month crime occurred

hour_of_day

20. numeric

Hour of day crime occurred

month_year

21.

Month and year when crime occurred

day_of_week

22. character

Day of week crime occurred

weekday

23. character

Weekday/weekend classification for crime

event

intersection

24. character

Occurred at an intersection

crime_classification

25. character

subjective binning of crimes

 

Task 3 requires a Tableau dashboard consisting of four crime event views of the LA Crimes

2012-2016 data set.

Task 3.1 Specific Crimes within each Crime Category for a specific Police Department Area and specific year

Task 3.2 Frequency of Occurrence for a selected crime over 24 hours for a specific Police

Department Area

Task 3.3 Frequency of Crimes within each Crime Classification by Police Department Area and by Time

Task 3.4 Geographical (location) presentation of each Police Department Area for given crime(s) and year. Note for this task you will need to make use of the geo-mapping capability of Tableau Desktop.

You should briefly discuss the key findings for each of these four views in your

Crimes Event Dashboard 

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