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CSI4142 Crime Data Mart
创建日期：2024-07-30 16:45:34
标签： CSI4142
Crime Data Mart
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CSI4142 
Crime Data Mart
This document contains the requirements for the data staging part of the project.
Instructions
1. Complete this project in a group of (3) students.
2. Submit your deliverable before the due date using the group locker in the Virtual
Campus.
3. Demonstrate your project to the corrector, in a 15 minute time slot during the week of
24 February, in SITE 4-010. (We will create a google sheet for this purposes.)
4. Use a database management system (DBMS) such as PostgreSQL to complete this
project.
Deliverables:
You are asked to submit the following details using your group locker:
- i) the scripts to create the database schema
- ii) the scripts to stage the data into the dimensional tables and the fact table
- iii) a document containing your one-page high level data staging plan, similar to the
Electricity data mart example covered in class.
All group members should attend the demonstrations.
Note that you will be asked to rate one another’s participation, and this rating will be
reflected in your final mark. For instance, if a group obtains 100% and a team member A
receives an average rating of 8/10 from the other two members, A’s mark will be 80%.
Your task:
Your task is to create the crime data mart, i.e. complete the physical design and data
staging. Use the data from the Denver and Vancouver datasets that cover the same time
period, as contained in:
You should augment these sources with additional information about Events and
Demographic information about neighborhoods.
2
Here is the dimensional model of the proposed data mart. You may use this model “as
is”, or modify or extend it as you see fit.
1. Remember to create your own surrogate keys. Refer to the slides and/or the book by
Kimball et. al. that explain how to stage the data for surrogate key lookup.
2. Supplement the original data with enriched data from other sources, such as e.g.
population statistics, notably when considering locations.
3. Use the full Date dimension from Kimball, as discussed in class.
4. The data mart contains concept hierarchies on the Date, Crime, Event and Location
dimensions.
5. Note that the Vancouver data lacks details of the crime-start-time, crime-end-time and
crime details. It is important to map the two sources to ensure that the crime types and
crime categories are similar.
6. The Event dimension tracks events that are categorised as types, such as music festivals,
sports events and family activities.
7. We maintain three facts, namely Is-Traffic (y/n), Is-Fatal (y/n) and Is-Nighttime (y/n)
where “yes” is staged as “1” and “no” is staged as “0”. Implicitly, Is-Nighttime can also
be used to run queries when determining whether the crime was committed during the
day.
8. In this model, we add an attribute crime severity to the Crime dimension, where we rate
an offense as “violent, non-violent, youth”, etc. It follows that this could also have been
implemented as a fact/measure, in cases where the want to count the number of crimes
by severity. 
9. The crime rate of a city is calculated by dividing the number of reported crimes by the
total population; the result is multiplied by 100,000. The information is stored in the
Location dimension.

Mark Allocation (100 marks in total)
1. (10 marks) Submit a one-page high level schematic detailing the high level plan you
followed.
2. (20 marks) Physical Design: Create the physical schema of the data mart using the DBMS of
your choice.
3. (70 marks) Data staging: Extract and transform the data and load all rows into the data mart.
a. (15 marks) Staging of Denver data, including surrogate key generation and referential
integrity enforcement.
b. (15 marks) Staging of Vancouver data, including surrogate key generation and
referential integrity enforcement.
c. (20 marks) Mapping of Denver and Vancouver data – fusion of categories, types, and
so on; handling of NULL values.
d. (10 marks) Staging of Event data.
e. (10 marks) Staging of statistical data involving neighborhoods, crime rates and crime
severity indexes.
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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 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