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INFS5710 Information Technology Infrastructure for Business Analytics

创建日期：2024-08-12 17:38:26

所属分类：经济Economics

标签： INFS5710

Information Technology Infrastructure for Business Analytics

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INFS5710 Information Technology Infrastructure for Business Analytics

Project Statement

This project accounts for 30% of the total marks for this course.

The deliverable is a PowerPoint file with video narration and speaker notes, and an appendix file.

Bike sharing has become increasingly popular across the globe. Today, such programs operate in more

than 1,000 cities, with more than half a million bicycles in use. The principle of bike sharing is simple:

individuals use bicycles on an as-needed basis without the costs and responsibilities of bike ownership.

It is short-term bicycle access, which provides its users with an environmentally friendly form of public

transportation. This flexible scheme targets daily mobility and allows users to access public bicycles at

unattended bike stations; bicycle reservations, pickup, and drop-off are all self-service. Commonly

concentrated in urban settings, bike sharing programs also provide multiple bike station locations that

enable users to pick up and return bicycles to different stations.

This project is about Capital Bikeshare (CaBi) in the metropolitan area of Washington DC (DC), which

covers not only the DC area, but also some parts of two nearby states, Maryland (MD), and Virginia (VA).

You are a business consultant working for the bike-sharing program.

Bike-sharing data

Your manager just referred you to download historical bike-sharing data by first visiting the following

site https://ride.capitalbikeshare.com/system-data; then click “downloadable files”. This would direct

you to the following site https://s3.amazonaws.com/capitalbikeshare-data/index.html, which contains

data of millions bike trips from July 2010 – 2022 September. Since the data come from the US, please

be aware of the difference in date formats between the US (mm/dd/yyyy) and Australia (dd/mm/yyyy).

It is also known that CaBi has changed the format of the data files recently. It is part of this project that

you need to decide how to consolidate tables coming from different sources and/or with different

formats.

The bikeshare data online are stored in comma-separated values (CSV) files. The first task that you need

to do before tackling any analyses is to place these datasets to your SAS folder (e.g., OrionDB on your H

drive) and convert them to the required SAS format. To do so, you will use SAS Enterprise Guide (EG) to

open each dataset by using File > Import Data to import the csv dataset. You will then be asked to

specify data; please try to understand what each step means. Most likely, you may just click next, ok, or

finish. After a dataset is loaded to SAS EG, you need to use File > Export to save it as a SAS dataset in

your SAS folder to be retrieved by your queries.

Regional factors

As said, CaBi not only serves DC, but some cities in MD and VA. Even within DC, the district is divided

into four quadrants of unequal areas: Northwest (NW), Northeast (NE), Southeast (SE), and Southwest

(SW). Each city and DC quadrant presents distinct characteristics (e.g., some are culturally rich, some are

more populated, and some have more crimes). Therefore, different regions may reveal different bike-

sharing use patterns. You may download detailed information of all CaBi bike stations from

https://opendata.dc.gov/datasets/capital-bike-share-locations/, in which the last column (attribute)

REGION_NAME shows whether a station is in DC, VA, or MD. If a station is within DC, the attribute

NAME would reveal the corresponding quadrant that it is located.

In the above file for station locations, you can find the locations of bike stations in the GPS coordinate

system. For example, the coordinate of a station is (x, y), where x is the longitude coordinate and y is the

latitude coordinate. The following link helps you to understand more about the GPS coordinate system:

https://www.ubergizmo.com/how-to/read-gps-coordinates/. If you want to locate a place on Google

Map by its latitude and longitude, you can also do it. For details, see the following link

https://support.google.com/maps/answer/18539.

If you are interested in estimating the distance traveled for a ride, assuming that a bike rental starts

from (1, 1) and ends at (2, 2), it is recommended that you estimate it using the so-called taxicab

distance, which is |1 ? 2| + |1 ? 2|. See the following figure for interpretation. For more

information, please see https://study.com/academy/lesson/taxicab-geometry-history-formula.html.

Note that whether the distance is in degrees (without any conversion from longitude-latitude

coordinates), miles, or kms, your findings, interpretations or insights should not change. It is

recommended that you just quote the distances in degrees.

Weather data

Weather plays an important role when people decide whether or not to use bike-sharing. You are

required to explore the relationship between weather (e.g., temperature, wind speed and humidity) and

the bike-sharing rentals in this project. There are two known ways to download free historical weather

data. The first way is to manually capture weather data month by month from Weather Underground

(wunderground.com).

First visit https://www.wunderground.com/ and try to search the weather condition in DC.

(There are other locations in MD and VA that you may also try, where there are many bike

stations as well.)

You will be led to the site of a nearby weather station, which may be different from time to

time.

Click the History tab on the page, and then choose to view Monthly weather data. Once you

choose a month, click View. For example, the following link shows the weather data of Oct 2011

measured at the Ronald Reagan Washington National Airport station (within DC):

https://www.wunderground.com/history/monthly/us/va/arlington/KDCA/date/2011-10

Scroll down the page, and you will see the table of Daily Observations. Use your mouse to copy

the table and paste it to an Excel spreadsheet.

Another way, as suggested by a former student, is to download weather data from NOAA. Try:

https://www.ncdc.noaa.gov/cdo-web/search. Search for "Daily Summaries" at relevant weather stations

for a time period then "Add to Cart" - NOTE that this is a free service, but you'll have to type in an email

address so that you can get the data download link once it processes.

Holiday data

Another factor that influences the bike-sharing rentals is holidays. You can easily search the dates of the

US federal holidays and/or MD, and VA state holidays each year.

The Task

Your manager asked you to collect and analyze the data and “let the data speak.” You understand that

the company wants to further grow the market and induce more users. Before they do it, they want to

have some insights from the data.

In this project, you are expected to manage and clean the data collected; some of them may contain

missing data, different formatting, and incomplete information. The goal is to overcome such obstacles

commonly encountered in reality to derive business insights from the datasets that can be used to

promote CaBi’s bike-sharing business.

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MAS8383 ECO 512 STAT 8310 CMN3102 MA577 CMPT 225 COMP0045 Methods Equations MATH 113 Math 3283W ICS-33 EN4302/ENT603 BIA B350F MA318 MTHM506 MECH 5315M ECONOMICS MAS8382 COM 2004 PX3142 XJEL 3662 STAT 231 MGT B399F PX4131 COM2108 30AM MSCI521 ECONM1015 ACFI304 GR5241 MATH10101 ECON 2070 COMS31900 EFIM20011 CSE 250B ACSE-5 STAT4207 SOFT2412 ELEC 331 AY2020 MSIN0209 FIT2094 CS 241L ST404 CS 411 3D CA3 INF4002 COMP3506 SIT718 CS 188 CS 170 MATH 138 CSE 251A CMT118 DATA5207 CMPT307 CMPT459 T1 PGEE11108 COSC 3P32 ME 163 MATH96007 7QQMM203 EE6227 BA 574 STA 4234 ECO206Y1Y ECO2008 MT2300 MATH 11158 CS 527 MACM 316 CSE474 2DI70 ISE 525 COMP3411 EMAT30007 7CCSMBDT MFIN704 MFIN704W21 MfIB ECE 438 CS 436 F36 CS5344 FE 621A FE621 TBA 612 ECM3151 ECMM422 MF1 MSIN0107 ESSMENT CS 5854 MFIT 5008 MIPS R4000 ENG5274 BU52018 ENG2079 BIOA02 STA2570 F0 INF553 STAT 440 STAT3008 COMP9417 COMP6451 6COM1042 BU52006 ST303 HW-1 CISC-235 CS 510 MATH3161 ECN 140 ECE391 Comp 3613 CVEN 9625 PRG455 UESTION 1 EARN 5 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1300A ENGG 1300 OLET1139 STAT 3503 STAT 206 FEM11149 ELEN 4720 COMP20003 SOLA1070 CS574 C11CS LINB29H3 MAST90083 ECON7200 STAT 610 LINB29 DSM-5 COMP3161 EEEN40010 MATH 4431 K353 STA 3386 IERG4300 COMS W4167 ENG5298 AMME 2302 CS3910 COMPSCI 320 STATS 369 GR5242 COSC 328 CSCI435 MGTS1301 INFS2200 COMP3900 CIS 375 Module AE03 COMP3121 CS260 HAM503 MGMT5050 INT3075 COSC 285 GEOL0029 GEGE2X01 STAT1201 MATH1061 MMF1941H STAT 5060 CS 520 COMP 4320 MSIN0104 000T FINS3648 ECMT2160 ECON4003 Phys 129L CSE 142 CS 3130 MGEB01 EECS 340 Finance (20443/20444) ECMM149 COMP5310 EEEN60180 CSC 111 AT2 CSCI 4430 STAB57 MA/CSC 427 MATH322 D100 CSCI544 CITS3004 CI6227 CSE 565 APCO 1P01 852L1 ELEC S411F COMP3207 STATS330 ECON7530 FIT5149 CSI4104 CMPT 371 COMP201 SEHS4696 FI4003 MATH6182 CG1 WS MATH401 MGT 205 QTM 110 COMP 0137 MACE12201 FIT5210 ECE 544 BISM7206 HUDM 6055 ECON3203 STATS 210 MSCI 570 MATH6173 COMPSCI4039 Accounting COM3503 AOE2024 ECON213 PSYC10004 Control Data 100/200A PHIL2617 PHYS3116 MIE250 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