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Stephy Wong

Edited

Mar 8, 2025

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HinKiuWongProjectDataSelection

Project Description

My project is about the impact of electric vehicles on sustainability in BC, specifically in Vancouver. In addition to examining the implications and trends in EV production, I aim to investigate how the adoption of EVs affects the natural environment, for example effects on greenhouse gas emissions, air quality, and energy consumption compared to traditional gasoline-powered vehicles. This project seeks to understand both the potential benefits and challenges associated with the transition to electric vehicles in contributing to a more sustainable transportation system.

Design Brief

Code Segments & Functionality

There are 3 important questions to address, each in its own section and aimed to do the following things in order:

1️⃣ Data Loading & Preprocessing

• Reads data from a CSV file.
• Uses D3 to parse and structure the data.
• Uses D3’s autoType() or similar methods to convert raw values into numerical and categorical data.
• Handles potential errors related to file access or format issues.

2️⃣ Data Cleaning & Transformation

• Organizes data properly according to its unique key
• Verifies data for missing and bad (out of range, out of type) values.
• Potentially reformat data for improved clarity.

3️⃣ Basic Operations

• Find the maximum and minimum.
• Sum values by a specified dimension.
• Average a set of values.
• Count how many records match a particular dimension criterion.
• Generates histogram using D3 or other visualization libraries.

1st Important Question

What is the current distribution of vehicle fuel types within the city?

Vehicle Population Dataset

Dataset Link: https://catalogue.data.gov.bc.ca/dataset/icbc-vehicle-population-vehicle-policies-in-force

Import D3

d3 = Object {format: ƒ(t), formatPrefix: ƒ(t, n), timeFormat: ƒ(t), timeParse: ƒ(t), utcFormat: ƒ(t), utcParse: ƒ(t), Adder: class, Delaunay: class, FormatSpecifier: ƒ(t), InternMap: class, InternSet: class, Voronoi: class, active: ƒ(t, n), arc: ƒ(), area: ƒ(t, n, e), areaRadial: ƒ(), ascending: ƒ(t, n), autoType: ƒ(t), axisBottom: ƒ(t), axisLeft: ƒ(t), …}

vehicle_population_file = FileAttachment {name: "Vehicle Population - Vehicle Policies in Force_Ful_data.csv", mimeType: "text/csv"}

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Since the text was not correctly formatted, I have reformatted it by removing all null characters () and replaces all tab characters ( ) with commas (,)

vehicle_population_text = `��Veh Pop - Criteria Selector,Vehicle Count Year,Anti Theft Device Indicator,Body Style,Electric Vehicle Indicator,Fleet Vehicle Indicator,FSA,Fuel Type,Hybrid Vehicle Indicator,Municipality,Owner Type,Region,Vehicle Type,Vehicle Use,Vehicle Count Vancouver,2019,No,Backhoe,No,No,V5K,Diesel,No,Vancouver,External organization,Lower Mainland,Commercial,Business,4 Vancouver,2019,No,Backhoeloader,No,No,V5K,Diesel,No,Vancouver,External organization,Lower Mainland,Commercial,Business,5 Vancouver,2019,No,Box,No,No,V5K,Diesel,No,Vancouver,External organization,Lower Mainland,Commercial,Business,3 Vancouver,2019,No,Bus,No,No,V5K,Diesel,No,Vancouver,External organization,Lower Mainland,Commercial,Business,1 Vancouver,2019,No,Dump,No,No,V5K,Diesel,No,Vancouver,External organization,Lower Mainland,Commercial,Business,2 Vancouver,2019,No,Flatdeck,No,No,V5K,Diesel,No,Vancouver,External organization,Lower Mainland,Commercial,Business,4 Vancouver,2019,No,Forklift,No,No,V5K,Diesel,No,Vancouver,External organization,Lower Mainland,Commercial,Business,1 Vancouver,2019,No,Loader,No,No,V5K,Diesel,No,Vancouver,External organization,Lower Mainland,Commercial,Business,1 Vancouver,2019,No,Pickup,No,No,V5K,Diesel,No,Vancouver,External organization,Lower Mainland,Commercial,Business,1 Vancouver,2019,No,Pumper,No,No,V5K,Diesel,No,Vancouver,External organization,Lower Mainland,Commercial,Business,1 Vancouver,2019,No,Trucktractor,No,No,V5K,Diesel,No,Vancouver,External organization,Lower Mainland,Commercial,Business,1 Vancouver,2019,No,Van,No,No,V5K,Diesel,No,Vancouver,External organization,Lower Mainland,Commercial,Business,8 Vancouver,2019,No,Box,No,No,V5P,Diesel,No,Vancouver,External organization,Lower Mainland,Commercial,Business,4 Vancouver,2019,No,Bus,No,No,V5P,Diesel,No,Vancouver,External organization,Lower Mainland,Commercial,Business,1 Vancouver,2019,No,Cabover,No,No,V5P,Diesel,No,Vancouver,External organization,Lower Mainland,Commercial,Business,1 Vancouver,2019,No,Crewcab,No,No,V5P,Diesel,No,Vancouver,External organization,Lower Mainland,Commercial,Business,2 Vancouver,2019,No,Dump,No,No,V5P,Diesel,No,Vancouver,External organization,Lower Mainland,Commercial,Business,6 Vancouver,2019,No,Flatdeck,No,No,V5P,Diesel,No,Vancouver,External organization,Lower Mainland,Commercial,Business,2 Vancouver,2019,No,Loader,No,No,V5P,Diesel,No,Vancouver,External organization,Lower Mainland,Commercial,Business,2Show 56118 truncated lines

Data cleaning

vehicle_population_lines = Array(56137) ["��Veh Pop - Criteria Selector,Vehicle Count Year,A…ype,Region,Vehicle Type,Vehicle Use,Vehicle Count", "Vancouver,2019,No,Backhoe,No,No,V5K,Diesel,No,Vanc…organization,Lower Mainland,Commercial,Business,4", "Vancouver,2019,No,Backhoeloader,No,No,V5K,Diesel,N…organization,Lower Mainland,Commercial,Business,5", "Vancouver,2019,No,Box,No,No,V5K,Diesel,No,Vancouve…organization,Lower Mainland,Commercial,Business,3", "Vancouver,2019,No,Bus,No,No,V5K,Diesel,No,Vancouve…organization,Lower Mainland,Commercial,Business,1", "Vancouver,2019,No,Dump,No,No,V5K,Diesel,No,Vancouv…organization,Lower Mainland,Commercial,Business,2", "Vancouver,2019,No,Flatdeck,No,No,V5K,Diesel,No,Van…organization,Lower Mainland,Commercial,Business,4", "Vancouver,2019,No,Forklift,No,No,V5K,Diesel,No,Van…organization,Lower Mainland,Commercial,Business,1", "Vancouver,2019,No,Loader,No,No,V5K,Diesel,No,Vanco…organization,Lower Mainland,Commercial,Business,1", "Vancouver,2019,No,Pickup,No,No,V5K,Diesel,No,Vanco…organization,Lower Mainland,Commercial,Business,1", "Vancouver,2019,No,Pumper,No,No,V5K,Diesel,No,Vanco…organization,Lower Mainland,Commercial,Business,1", "Vancouver,2019,No,Trucktractor,No,No,V5K,Diesel,No…organization,Lower Mainland,Commercial,Business,1", "Vancouver,2019,No,Van,No,No,V5K,Diesel,No,Vancouve…organization,Lower Mainland,Commercial,Business,8", "Vancouver,2019,No,Box,No,No,V5P,Diesel,No,Vancouve…organization,Lower Mainland,Commercial,Business,4", "Vancouver,2019,No,Bus,No,No,V5P,Diesel,No,Vancouve…organization,Lower Mainland,Commercial,Business,1", "Vancouver,2019,No,Cabover,No,No,V5P,Diesel,No,Vanc…organization,Lower Mainland,Commercial,Business,1", "Vancouver,2019,No,Crewcab,No,No,V5P,Diesel,No,Vanc…organization,Lower Mainland,Commercial,Business,2", "Vancouver,2019,No,Dump,No,No,V5P,Diesel,No,Vancouv…organization,Lower Mainland,Commercial,Business,6", "Vancouver,2019,No,Flatdeck,No,No,V5P,Diesel,No,Van…organization,Lower Mainland,Commercial,Business,2", "Vancouver,2019,No,Loader,No,No,V5P,Diesel,No,Vanco…organization,Lower Mainland,Commercial,Business,2", …]

Below are two functions that read in a data file into the data model and organizes it properly according to its unique key, it also verifies the data for missing and bad (out of range, out of type) values.

cleanAndOrganizeData = ƒ(lines, raw_text)

verifyRecord = ƒ(record)

Call the function

vp_formatted_csv = Array(56136) [Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, …]

Programming

#1 Find the maximum and minimum

vc_values = Array(56136) [4, 5, 3, 1, 2, 4, 1, 1, 1, 1, 1, 8, 4, 1, 1, 2, 6, 2, 2, 1, …]

vc_minValue = 1

vc_maxValue = 20341

#2 Sum values by a specified dimension (total of n in dimension d, where n is quantitative)

vc_sumValue = 1887038

#3 Average a set of values

Note: choose the right centrality measure: skewed data = median

vc_avgValue = 2

#4 Count how many records match a particular dimension criterion, where Fuel Type is "Electric"

ev_matchingRecords = Array(4720) [Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, …]

electric_count = 4720

#5 Information about the data source(s) (where they are from)

#6 Provide a histogram for each dimension

Filter dataset first to only include Vehicle Count with reasonable amount

vcy_cleaned = Array(53459) [Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, …]

vehicle_count = Array(53459) [4, 5, 3, 1, 2, 4, 1, 1, 1, 1, 1, 8, 4, 1, 1, 2, 6, 2, 2, 1, …]

12.195506122597868

02,0004,0006,0008,00010,00012,00014,00016,00018,00020,00022,000↑ Frequency05101520253035404550556065707580

Comment on bin size selection

what does the distribution tell you in each case?

I chose a bin size of 2 because it balances granularity and readability the best. I have experimented with various bin sizes, but smaller bins resulted in excessive noise, while larger bins oversimplified the data and hid important patterns in the 0-10 range.

The distribution of the data is highly right-skewed, most of the data points are concentrated near the lower values, with a long tail extending to the right. Therefore, a bin size of 2 allows us to capture variations in the lower values while still displaying trends in the higher values.

2nd Important Question

What has been the growth of electric vehicles over the years?

Vehicle Population Dataset

Using the same dataset as Question #1: https://catalogue.data.gov.bc.ca/dataset/icbc-vehicle-population-vehicle-policies-in-force

Programming

#1 Find the maximum and minimum

vcy_values = Array(56136) [2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, …]

vcy_minValue = 2019

vcy_maxValue = 2023

#2 Sum values by a specified dimension (total of n in dimension d, where n is quantitative)

vcy_sumValue = 113452799

#3 Average a set of values

Note: choose the right centrality measure: ordinal/skewed data = median

vcy_avgValue = 2021

#4 Count how many records match a particular dimension criterion, where Electric Vehicle Indicator is "Yes"

evi_matchingRecords = Array(4380) [Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, …]

evi_count = 4380

#5 Information about the data source(s) (where they are from)

#6 Provide a histogram for each dimension

vehicle_count_year = Array(56136) [2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, …]

1.416703898539932

01,0002,0003,0004,0005,0006,0007,0008,0009,00010,00011,000↑ Frequency2,0192,0202,0212,0222,0232,024

Comment on bin size selection

what does the distribution tell you in each case?

I chose a bin size of 1 because the data is based on years, which makes it easier to compare trends across different years.

The histogram shows a fairly consistent distribution from 2019 to 2023, with a gradual increase each year. This suggests that there are a relatively stable car population per year, but with slight growth.

3rd Important Question

How has the adoption of electric vehicles influenced the overall trends in greenhouse gas emissions in the transport sector over the years?

GHG emission Dataset

Dataset Link: https://catalogue.data.gov.bc.ca/dataset/british-columbia-greenhouse-gas-emissions

ghg_file = FileAttachment {name: "bc_ghg_emissions_1990-2021.csv", mimeType: "text/csv"}

ghg_text = `gas,sector,subsector_level1,subsector_level2,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021 CARBON DIOXIDE (CO2),ENERGY,STATIONARY COMBUSTION,Public Electricity and Heat Production,0.7934420695,0.465220682,0.9257044185,1.9340284382,1.77947149936,2.2894969295000003,0.605219362310303,1.0475168891206,1.76838114593091,0.99647347785669,2.0486960353530197,2.58301008962183,1.12951089149891,0.996877611585569,1.2179909783031702,1.29480527846724,1.47536494305522,1.32032060453395,1.69733754291601,1.5967400659683302,1.49009151589844,1.0469528237304702,0.6921356594116209,0.844159389525635,0.793406725863403,0.7444785576130369,0.969238663549805,0.848337746897338,0.978794049319214,0.998319407028808,0.689631745777466,0.907008674453613 CARBON DIOXIDE (CO2),ENERGY,STATIONARY COMBUSTION,Petroleum Refining Industries,1.23203700743511,1.22904675629093,1.03972162104062,0.715975284092954,0.709045618036116,0.5733418515644201,0.734569598184814,0.440330344070068,0.41327170503935,0.468888850883782,0.41526049698969003,0.437994388404297,0.5115888802783201,0.488328509152466,0.8522076836293939,0.49166755370166,0.625432467430786,0.6330703653837889,0.48229951886108396,0.579360967075439,0.653981616905151,0.5666146453356931,0.555032607069824,0.47307599482861296,0.510233585313965,0.529092801565674,0.626424808444454,0.49885924551183003,0.374948949447022,0.471067577344238,0.38143589465332,0.43564227416748 CARBON DIOXIDE (CO2),ENERGY,STATIONARY COMBUSTION,Oil and Gas Extraction,1.98413471577324,1.3106083258094199,0.654698529872068,0.728528224190388,1.49483840322736,2.51896342572017,3.18025239212112,1.95291897061453,2.78773613916064,4.00652702326576,2.96491368864794,3.9906488374034303,4.2149469942093205,4.40538790187478,4.82841261707983,4.7225388962299,5.33178357755979,5.765196766943189,5.92309971701634,6.0444180292104,6.34287487021444,6.50426967186153,6.45183551286055,6.4747889564428,6.52482181411733,6.348886823195911,6.77745511282289,6.937767913308959,7.00426886410894,6.39660559907724,6.58670346669318,6.3509574382182 CARBON DIOXIDE (CO2),ENERGY,STATIONARY COMBUSTION,Mining,0.6128010669496841,0.625672870548389,0.433588155631369,0.517398070292786,0.44749070098933497,0.546212493073039,0.667666917869117,0.6422263053708189,0.5394524866293979,0.587410628925616,0.61186996551139206,0.8604252864809261,0.647059962352181,0.7244217744987961,0.710927224728824,0.381970891160882,0.592768615329489,0.597571380207109,0.623376993634046,0.5254099555079561,0.6000559482806479,0.5605694318986261,0.6149517434056609,0.593137879156716,0.566730238902683,0.465442290143738,0.495973790147618,0.483556485204124,0.533974376801609,0.5344319900591891,0.5378871672975469,0.583750210008559 CARBON DIOXIDE (CO2),ENERGY,STATIONARY COMBUSTION,Manufacturing Industries,6.38146236066448,6.05120842643508,5.42168728668013,5.87140961732829,6.0524560946263,6.86912814601636,7.45081302176098,6.98018383627833,6.48450391016589,7.14674678317907,7.6343011671984495,7.61656310406928,6.55128168300389,6.63839469962519,6.28331583771709,5.95125392256949,4.48366937787345,4.33022701227802,3.70932663669874,3.71078738290575,3.7083999546303197,3.84093257322311,3.91415999556351,3.92497708319855,4.20645123945656,4.2653190225586,4.545620206022271,4.75065036969414,4.83364598260893,4.39311062037776,3.88943853182792,3.9150278785198203 CARBON DIOXIDE (CO2),ENERGY,STATIONARY COMBUSTION,Construction,0.30515790308081103,0.268605946085693,0.318715649249258,0.341517859964619,0.284540600086182,0.199700232688232,0.207949309985352,0.12628037998046898,0.10082483999023399,0.085979618737793,0.0758838942382813,0.0715778478271484,0.07502082067871091,0.0828505404418945,0.102696612329102,0.111491959228516,0.117195611010742,0.124417383569336,0.104747995654297,0.0634028627441406,0.0821526647705078,0.10105122690429699,0.0978899258789063,0.0670336012329102,0.06531493837890631,0.0708642588623047,0.095429442175293,0.0957486565185547,0.105102607409668,0.100552180615234,0.0996125612060547,0.0898617567260742 CARBON DIOXIDE (CO2),ENERGY,STATIONARY COMBUSTION,Commercial and Institutional,2.92987598598658,3.1908164434628703,3.2984087118463097,3.68716991829302,3.41471804749972,3.49235411794359,3.53166648193205,3.4146821212743497,3.02285361528092,3.11312216054006,3.52106040759251,3.50927284791495,4.10769204901352,3.35625309225063,3.23833781337878,3.1148015054745803,3.04365939499049,3.0073949681573,3.20405707391764,2.8452619877441396,2.6054250016845697,2.90967107186035,2.9136240933593798,2.69641507716064,2.61973387502441,2.41402049523926,2.69959503348389,2.83429481992188,2.7567923317751504,2.90536910871579,2.98422498972168,3.10414884720459 CARBON DIOXIDE (CO2),ENERGY,STATIONARY COMBUSTION,Residential,4.08904304776682,4.00024737923747,3.9066945718624,4.4017362068097,4.1794221582056394,4.224190266618409,4.76464612922363,4.35612017441406,4.2770602044970705,4.563721481831051,4.43312981887207,4.3273509848999,4.13761463071289,3.90095626949463,3.7819419748291003,4.2621174164917,4.36903294128418,4.32575970056152,4.2404911473877,4.22414973466797,4.24622522668457,4.25776242697754,3.94217134802246,3.9170406158935496,3.7430988315673797,3.66612928452148,3.7797680882690403,4.15803689772949,3.9164177368652298,4.07369008037109,4.14545638039551,4.23513366417236 CARBON DIOXIDE (CO2),ENERGY,STATIONARY COMBUSTION,Agriculture and Forestry,0.321392687650615,0.373089444826436,0.370540645983447,0.372661449094727,0.20178924141479498,0.152770409960205,0.18782941933593802,0.26826476931152304,0.250808159619141,0.261157528674317,0.315933051123047,0.360506099035644,0.13041204291992198,0.084158832397461,0.07225904233398439,0.0741127570800781,0.0734267760742188,0.0730450567138672,0.06291600620117191,0.0486734573730469,0.304819358349609,0.275591644067383,0.380130203515625,0.379885180432129,0.377217023168945,0.41272330120849604,0.563857141223145,0.5648005632202151,0.610190107116699,0.583234307531738,0.5745788701904301,0.517360023339844 CARBON DIOXIDE (CO2),ENERGY,TRANSPORT,Domestic Aviation,1.32425309507863,1.14899052607749,1.13892826144197,1.07277819162644,1.12892169572812,1.24696340862816,1.4017924602938399,1.45808306045877,1.5279725614244302,1.5891297568334801,1.53333598048914,1.3997746104206201,1.35213879146457,1.35300494408285,1.48497091533691,1.5321841907457798,1.50754739729163,1.44724869055086,1.36416625338963,1.22842357589415,1.18741889600009,1.1358832004686201,1.30235136380304,1.33373767326046,1.29474636321421,1.30627087538653,1.33284376784362,1.44971445881138,1.5887285111465201,1.58720991760236,0.896999627393325,1.10857915371015 CARBON DIOXIDE (CO2),ENERGY,TRANSPORT,Road Transport,9.95657248003399,10.118721567803599,10.4603695065435,10.856543304478,11.520139249234301,12.0449893929792,12.415969077686599,12.887697319525799,13.0214008599926,12.9614969583146,12.7030438852673,12.6262271015298,12.6140750898314,13.4053307565355,14.1183641546413,13.4261133907725,13.4152888174555,13.9195208332326,14.336131540606301,14.232317127800199,13.8691818002238,12.851912373126899,13.32111054311,14.149564701245499,14.287970171267899,14.2648126813959,15.086751294588101,14.974663415527901,15.379229584131,15.179228757449101,13.688324799410001,14.441124241040699 CARBON DIOXIDE (CO2),ENERGY,TRANSPORT,Railways,1.7049485885295101,1.6275233197642598,1.5927254056128899,1.58441439125781,1.48261515328474,1.43370955824004,1.49784752953931,1.4804770338456401,1.32546099576113,1.44203983931061,1.3452767892378599,1.27708235592943,1.08841133023121,1.1248372822792,1.2295304198782702,1.33262220513516,1.44066332736197,1.5460814843764399,1.59261885310456,1.4856977645612202,1.44155499887674,1.63249959221584,1.69806053929479,1.65199452738749,1.6050424872936402,1.54566362701555,1.4611467635402,1.6537244904924702,1.79254569158165,1.91533093710823,1.8701633412332102,1.8463310876943402 CARBON DIOXIDE (CO2),ENERGY,TRANSPORT,Domestic Marine,0.6091075339273919,0.6355778176791029,0.662590777722971,0.687182939240065,0.711270301841413,0.739320191074261,0.748530176257844,0.75975824728237,0.770349984587543,0.785142036431921,0.797831975978522,0.809943898715963,0.8201394560505151,0.833767628143846,0.842378990654371,0.850748182189638,0.839220115324538,0.825885968806909,0.809703819950035,0.7969391861777321,0.769330435790939,0.8205292900210781,0.875745795572038,0.9300666313547841,0.9896860974503,1.04143443254323,1.10598863916998,1.05355689275227,1.11129274547291,1.21421146526496,1.20976928859296,1.41960340927168 CARBON DIOXIDE (CO2),ENERGY,TRANSPORT,Off-Road Transport,3.5506534295471015,3.500434597176798,3.2315616067662885,3.288411593463254,3.499909469024465,3.7198369032251835,3.790967044729149,4.1877388443830235,4.39127772219545,4.320557310421192,4.595898100407616,4.549828469892341,4.738948087479666,4.807231925576421,5.1670204067916,5.0768068641383755,4.880708801704882,4.706824767389974,4.681455443521902,3.150447666038446,3.629101268182421,3.6314043944658607,3.845656869771944,4.101273757512345,3.952660264440497,4.707715180950533,4.903608101150166,5.520223156430186,6.3174629262849145,6.069641306005525,5.569386429090717,5.66187056845864 CARBON DIOXIDE (CO2),ENERGY,TRANSPORT,Pipeline Transport,0.83582067876,1.080742203,1.02512922876,1.1011463295000001,1.22261546076,1.353357933,1.4747481,1.4125641,1.54133805,1.3715743568237302,1.60975282016602,1.810480193396,1.31732000991211,1.02699712386475,1.09172696425781,0.952713319934082,0.744914328515625,0.899996991259766,0.865016410986328,0.836563606225586,0.8069436789062501,0.776887307958984,0.769983829370117,0.977934966479492,0.997119948999023,1.26474559975586,1.3451516049316399,1.38877412224121,1.30297272059326,1.3338025543335,1.27137538537598,1.2644082612304701 CARBON DIOXIDE (CO2),ENERGY,FUGITIVE SOURCES,Coal Mining,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA CARBON DIOXIDE (CO2),ENERGY,FUGITIVE SOURCES,Oil and Natural Gas,1.72004522359465,1.43258503771541,1.5807582775783602,1.26267571462405,2.20226345798504,2.3921326441818502,2.86493463575,2.92286444627329,3.04882502647843,2.81894990977809,2.903525376144,3.0274625014098002,2.68697010407569,2.62567822930704,2.4513564336725997,2.56967478172356,2.5177175634585303,2.66385752291067,3.20198989229213,2.73259514483853,2.46239110919604,2.7621413721074,2.49541226470749,2.39384474142746,2.3307479642017603,2.1064122747480103,1.54950517283515,1.4397523171000401,1.54408495400902,1.60825260078783,1.8783972066494,2.00423660025464 CARBON DIOXIDE (CO2),ENERGY,FUGITIVE SOURCES,CO2 Transport and Storage,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA CARBON DIOXIDE (CO2),"IPPU, AGRICULTURE, AND WASTE",INDUSTRIAL PROCESSES AND PRODUCT USE (IPPU),Mineral Products,0.879056458921204,0.776521168735002,0.8460833413554499,0.881125142497174,0.988899079294114,1.05999519384148,1.02320896257897,1.18716389071268,1.17425407243877,1.39213634507449,1.41200675675426,1.33362772243373,1.38293604583249,1.36469039637837,1.49128312457017,1.49935239488205,1.4427864667796801,1.46543555214825,1.3291246499810299,1.05324880661427,1.14697069741326,1.1645280001911,1.24964665433137,1.14620357052585,1.15130563949221,1.1985644017955501,1.0928784487975198,0.966359866423903,1.0585400325349201,0.995768427522734,0.8984925649031931,1.0034227440899701Show 283 truncated lines

ghg = Array(301) [Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, …]

Data cleaning

ghg_lines = Array(302) ["gas,sector,subsector_level1,subsector_level2,1990,…2012,2013,2014,2015,2016,2017,2018,2019,2020,2021", "CARBON DIOXIDE (CO2),ENERGY,STATIONARY COMBUSTION,…8319407028808,0.689631745777466,0.907008674453613", "CARBON DIOXIDE (CO2),ENERGY,STATIONARY COMBUSTION,…471067577344238,0.38143589465332,0.43564227416748", "CARBON DIOXIDE (CO2),ENERGY,STATIONARY COMBUSTION,…6.39660559907724,6.58670346669318,6.3509574382182", "CARBON DIOXIDE (CO2),ENERGY,STATIONARY COMBUSTION,…319900591891,0.5378871672975469,0.583750210008559", "CARBON DIOXIDE (CO2),ENERGY,STATIONARY COMBUSTION,…9311062037776,3.88943853182792,3.9150278785198203", "CARBON DIOXIDE (CO2),ENERGY,STATIONARY COMBUSTION,…52180615234,0.0996125612060547,0.0898617567260742", "CARBON DIOXIDE (CO2),ENERGY,STATIONARY COMBUSTION,….90536910871579,2.98422498972168,3.10414884720459", "CARBON DIOXIDE (CO2),ENERGY,STATIONARY COMBUSTION,….07369008037109,4.14545638039551,4.23513366417236", "CARBON DIOXIDE (CO2),ENERGY,STATIONARY COMBUSTION,…234307531738,0.5745788701904301,0.517360023339844", "CARBON DIOXIDE (CO2),ENERGY,TRANSPORT,Domestic Avi…58720991760236,0.896999627393325,1.10857915371015", "CARBON DIOXIDE (CO2),ENERGY,TRANSPORT,Road Transpo…28757449101,13.688324799410001,14.441124241040699", "CARBON DIOXIDE (CO2),ENERGY,TRANSPORT,Railways,1.7…33093710823,1.8701633412332102,1.8463310876943402", "CARBON DIOXIDE (CO2),ENERGY,TRANSPORT,Domestic Mar….21421146526496,1.20976928859296,1.41960340927168", "CARBON DIOXIDE (CO2),ENERGY,TRANSPORT,Off-Road Tra…69641306005525,5.569386429090717,5.66187056845864", "CARBON DIOXIDE (CO2),ENERGY,TRANSPORT,Pipeline Tra…3338025543335,1.27137538537598,1.2644082612304701", "CARBON DIOXIDE (CO2),ENERGY,FUGITIVE SOURCES,Coal …A,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA", "CARBON DIOXIDE (CO2),ENERGY,FUGITIVE SOURCES,Oil a…1.60825260078783,1.8783972066494,2.00423660025464", "CARBON DIOXIDE (CO2),ENERGY,FUGITIVE SOURCES,CO2 T…A,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA", "CARBON DIOXIDE (CO2),\"IPPU, AGRICULTURE, AND WASTE…68427522734,0.8984925649031931,1.0034227440899701", …]

Call the cleanAndOrganizeData() function defined above to do data cleaning on ghg dataset

ghg1_formatted_csv = Array(301) [Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, …]

Filter dataset to only include TRANSPORT sector

ghg_cleaned = Array(18) [Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object]

Reformatting

Since the original data was not in the desired format, I decided to reformat it with new headers for improved clarity.

P.S. Make sure to run each line before the code can execute properly.

rows = Array(18) [Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object, Object]

output = Array(0) []

undefined

1

ghg_formattedOutput = ""

ghg_formatted_csv = Array(0) [columns: Array(0)]

Programming

#1 Find the maximum and minimum

ghg_values = Array(0) []

ghg_minValue = undefined

ghg_maxValue = undefined

#2 Sum values by a specified dimension (total of n in dimension d, where n is quantitative)

ghg_sumValue = 0

#3 Average a set of values

Note: choose the right centrality measure: skewed data = median

ghg_avgValue = undefined

#4 Count how many records match a particular dimension criterion, where subsector_level2 is "Domestic Aviation"

ghg_matchingRecords = Array(0) []

domestic_aviation_count = 0

#5 Information about the data source(s) (where they are from)

#6 Provide a histogram for each dimension

Filter dataset first to look closely into the patterns within the +/-0.5 range of median values.

megatonnes_cleaned = Array(0) []

megatonnes = Array(0) []

undefined

0Frequency

Comment on bin size selection

what does the distribution tell you in each case?

I chose a bin size of 0.005 because it can capture a smaller variations in the dataset, which allows us to observe more detailed patterns where values are most concentrated, rather than excessively smoothing the distribution.

The histogram is right-skewed, with most values concentrated at the lower end (closer to 0). This indicates that most emission values are small, with fewer instances of significantly high emissions. Additionally, there are likely some extreme values and outliers which were filtered out before generating the histogram.

Challenges and Issues

1. Initially, I was unable to access files in Excel format, possibly due to permission issues. I solved this by converting the file to CSV format and reattached it in Observable. However, the problem seems to arise from the data being encoded with null characters (e.g., \x00), which typically indicates that the data is in UTF-16 or another encoding that includes padding. To fix this, I converted the text to a more appropriate format by using the method replace(/\x00/g, ''), which removes all null characters from the string and resolves the issue.

2. Some dimensions in the dataset cannot be visualized in a histogram or may not be meaningful for computation, even though they are quantitative data.

3. The GHG dataset has to be separated or filtered into different parts to avoid inconsistent data and to create meaningful visualizations.

Bad Data & Missing Values Handling

To handle bad data and missing values, I have two functions for data cleaning. The cleanAndOrganizeData function mainly trims whitespace, while the verifyRecord function checks if all fields contain valid values, ensuring there are no empty or null entries. If a record passes verification, it is added to the final dataset; otherwise, a warning is logged to indicate an invalid entry. This ensures that only clean and complete data is retained for further analysis.

Data sources

Vehicle Population Dataset: https://catalogue.data.gov.bc.ca/dataset/icbc-vehicle-population-vehicle-policies-in-force

GHG Dataset: https://catalogue.data.gov.bc.ca/dataset/british-columbia-greenhouse-gas-emissions

References

https://observablehq.com/@d3/d3-extent

https://observablehq.com/@d3/d3-sum

https://observablehq.com/@d3/d3-mean-d3-median-and-friends

https://observablehq.com/@d3/d3-count

https://observablehq.com/@d3/histogram/2

https://observablehq.com/@fer-aguirre/data-cleaning

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d3=require("d3@6")

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vehiclePopulationVehiclePoliciesInForce_ful_d

56,136 rows

Vehicle Population - Vehicle Policies in Force_Ful_data@3.csv

FilterColumnsSortSlice

Save

Type Table, then Shift-Enter. Ctrl-space for more options.

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vehicle_population_file=FileAttachment("Vehicle Population - Vehicle Policies in Force_Ful_data.csv")

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raw_text=vehicle_population_file.text()

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vehicle_population_text=raw_text.replace(/\x00/g,'').replace(/\t/g,',');

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vehicle_population_lines=vehicle_population_text.trim().split('\n');

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functioncleanAndOrganizeData(lines,raw_text){

// Split the text into lines and parse headers

constheaders=lines[0].split(',');// First line as headers

// Initialize an array to hold the organized data

constdata=[];

// Process each line of data

for(leti=1;i<lines.length;i++){

constvalues=lines[i].split(',');

// Create an object for the current line

constrecord={};

headers.forEach((header,index)=>{

record[header]=values[index]?values[index].trim():null;// Trim whitespace

});

// Verify data

if(verifyRecord(record)){

data.push(record);// Push valid records

}else{

console.warn(`Invalid record at line ${i+1}:`,record);

}

}

returndata;

}

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// Function to verify the record

functionverifyRecord(record){

// Get all keys from the record

constkeys=Object.keys(record);

// Loop through each key to verify the value

for(constkeyofkeys){

// Check if the value is missing or not a number

if(!record[key]){

returnfalse;// If any field is invalid, return false

}

}

returntrue;// All fields are valid

}

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vp_formatted_csv=cleanAndOrganizeData(vehicle_population_lines,raw_text);

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vc_values=vp_formatted_csv.map(d=>+d["Vehicle Count"])

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vc_minValue=d3.min(vc_values)

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vc_maxValue=d3.max(vc_values)

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vc_sumValue=d3.sum(vp_formatted_csv,d=>d["Vehicle Count"])

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vc_avgValue=d3.median(vp_formatted_csv,d=>d["Vehicle Count"])

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ev_matchingRecords=vp_formatted_csv.filter(record=>record["Fuel Type"]==="Electric")

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electric_count=ev_matchingRecords.length

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// Filter dataset first to only include Vehicle Count with reasonable amount

vcy_cleaned=vp_formatted_csv.filter(d=>d["Vehicle Count"]<=80)

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vehicle_count=vcy_cleaned.map(d=>+d["Vehicle Count"])

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d3.deviation(vehicle_count)

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Plot.plot({

width:1000,

height:500,

marks:[

Plot.rectY(vehicle_count,Plot.binX({y:"count"},{x:vehicle_count,fill:"steelblue",thresholds:50})),

Plot.ruleY([0])

]

})

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vcy_values=vp_formatted_csv.map(d=>+d["Vehicle Count Year"])

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vcy_minValue=d3.min(vcy_values)

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vcy_maxValue=d3.max(vcy_values)

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vcy_sumValue=d3.sum(vp_formatted_csv,d=>d["Vehicle Count Year"])

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vcy_avgValue=d3.median(vp_formatted_csv,d=>d["Vehicle Count Year"])

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evi_matchingRecords=vp_formatted_csv.filter(record=>record["Electric Vehicle Indicator"]==="Yes")

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evi_count=evi_matchingRecords.length

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vehicle_count_year=vp_formatted_csv.map(d=>+d["Vehicle Count Year"])

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d3.deviation(vehicle_count_year)

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Plot.plot({

width:400,

height:500,

marks:[

Plot.rectY(vehicle_count_year,Plot.binX({y:"count"},{x:vehicle_count_year,fill:"steelblue",thresholds:5})),

Plot.ruleY([0])

]

})

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bc_ghg_emissions_by_economic_sector_by_gas_199020

301 rows

bc_ghg_emissions_1990-2021.csv

FilterColumnsSortSlice

Save

Type Table, then Shift-Enter. Ctrl-space for more options.

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ghg_file=FileAttachment("bc_ghg_emissions_1990-2021.csv")

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ghg_text=ghg_file.text()

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ghg=d3.csvParse(ghg_text,d3.autoType)

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ghg_lines=ghg_text.trim().split('\n');

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ghg1_formatted_csv=cleanAndOrganizeData(ghg_lines,ghg_text);

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// Filter dataset to only include TRANSPORT sector

ghg_cleaned=ghg.filter(d=>d.subsector_level1==="TRANSPORT"&&d["2021"]!=="NA")

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rows=ghg_cleaned

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// !!!!!!!!!! P.S. Run each line before the code can run properly -------------------------------------------------->>>>>>>>>>>>>>>>>>>

// array to store new headers

output=[]

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// !!!!!!!!!! P.S. Run each line before the code can run properly -------------------------------------------------->>>>>>>>>>>>>>>>>>>

rows.forEach(row=>{

constgas=row.gas;

constsector=row.sector;

constsubsectorLevel1=row.subsector_level1;

constsubsectorLevel2=row.subsector_level2;

// Loop through each year and construct new rows

for(letyear=1990;year<=2021;year++){

constvalue=row[year];// Assuming the yearly values are properties on the row object

// output.push(`${gas},${sector},${subsectorLevel1},${subsectorLevel2},${year},${value}`);

output.push([gas,sector,subsectorLevel1,subsectorLevel2,year,value]);

}

});

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// !!!!!!!!!! P.S. Run each line before the code can run properly -------------------------------------------------->>>>>>>>>>>>>>>>>>>

// define new headers

output.push(`gas,sector,subsector_level1,subsector_level2,year,megatonnes (mt CO2e)`)

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ghg_formattedOutput=output.join('\n');

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ghg_formatted_csv=d3.csvParse(ghg_formattedOutput,d3.autoType)

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ghg_values=ghg_formatted_csv.map(d=>+d["megatonnes (mt CO2e)"])

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ghg_minValue=d3.min(ghg_values)

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ghg_maxValue=d3.max(ghg_values)

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ghg_sumValue=d3.sum(ghg_formatted_csv,d=>d["megatonnes (mt CO2e)"])

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ghg_avgValue=d3.median(ghg_formatted_csv,d=>d["megatonnes (mt CO2e)"])

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ghg_matchingRecords=ghg_formatted_csv.filter(record=>record.subsector_level2==="Domestic Aviation")

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domestic_aviation_count=ghg_matchingRecords.length

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// Filter dataset first to look closely into the patterns within the +/-0.5 range of median values.

megatonnes_cleaned=ghg_formatted_csv.filter(d=>d["megatonnes (mt CO2e)"]<=0.1)

// megatonnes_cleaned = ghg_formatted_csv.filter(d => d.gas === "CARBON DIOXIDE (CO2)" && d.subsector_level2 === "Road Transport")

Insert cell

megatonnes=megatonnes_cleaned.map(d=>d["megatonnes (mt CO2e)"])

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d3.deviation(megatonnes)

Insert cell

Plot.plot({

width:960,

height:500,

marks:[

Plot.rectY(megatonnes,Plot.binX({y:"count"},{x:megatonnes,fill:"steelblue",thresholds:20})),

Plot.ruleY([0])

]

})

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File attachments

Vehicle Population - Vehicle Policies in Force_Ful_data.csvCSV Vehicle Population - Vehicle Policies in Force_Ful_data@1.csvCSV bc_ghg_emissions_by_economic_sector_by_gas_1990-2021.csvCSV bc_ghg_emissions_1990-2021.csvCSV Vehicle Population - Vehicle Policies in Force_Ful_data@2.csvCSV Vehicle Population - Vehicle Policies in Force_Ful_data@3.csvCSV

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Column type

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Derive column

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Column type

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Derive column

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Column type

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• raw

• string

• number

• date

• boolean

• bigint

Derive column

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Column type

• autostring

• raw

• string

• number

• date

• boolean

• bigint

Derive column

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Column type

• autostring

• raw

• string

• number

• date

• boolean

• bigint

Derive column

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Column type

• autostring

• raw

• string

• number

• date

• boolean

• bigint

Derive column

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Column type

• autostring

• raw

• string

• number

• date

• boolean

• bigint

Derive column

No sorting

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Column type

• autostring

• raw

• string

• number

• date

• boolean

• bigint

Derive column

No sorting

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Column type

• autostring

• raw

• string

• number

• date

• boolean

• bigint

Derive column

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Column type

• autostring

• raw

• string

• number

• date

• boolean

• bigint

Derive column

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Column type

• autostring

• raw

• string

• number

• date

• boolean

• bigint

Derive column

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Column type

• autostring

• raw

• string

• number

• date

• boolean

• bigint

Derive column

No sorting

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Column type

• autostring

• raw

• string

• number

• date

• boolean

• bigint

Derive column

No sorting

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Column type

• autostring

• raw

• string

• number

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• boolean

• bigint

Derive column

No sorting

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Column type

• autostring

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• boolean

• bigint

Derive column

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Column type

• autostring

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Derive column

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Column type

• autostring

• raw

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Derive column

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Column type

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Derive column

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Column type

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Derive column

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Column type

• autostring

• raw

• string

• number

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• boolean

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Derive column

No sorting

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Column type

• autostring

• raw

• string

• number

• date

• boolean

• bigint

Derive column

No sorting

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Column type

• autostring

• raw

• string

• number

• date

• boolean

• bigint

Derive column

No sorting

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Hide

Column type

• autostring

• raw

• string

• number

• date

• boolean

• bigint

Derive column

No sorting

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Hide

Column type

• autostring

• raw

• string

• number

• date

• boolean

• bigint

Derive column

No sorting

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Hide

Column type

• autostring

• raw

• string

• number

• date

• boolean

• bigint

Derive column

No sorting

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Hide

Column type

• autostring

• raw

• string

• number

• date

• boolean

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