Clean Ports Survey: What do youth in the Bay Area think about the Port of Oakland?
Getting Started
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About the Data
Survey responses were collected from August 27, 2024 through December 29, 2025. The survey was distributed via Google Forms and shared broadly across our networks of Bay Area youth, including program alumni and participants, through social media channels, and via in-person and digital outreach efforts.
To ensure data quality, we filtered out responses identified as spam or bot-generated. We also removed x responses from non-youth. After data cleaning and several rounds of verification checks, we retained 338 valid English responses and 11 valid Spanish responses for a total of 349. While we have taken extensive measures to verify response authenticity, we acknowledge that absolute certainty is not possible.
#Renaming the response "Another Identity (Please specify in next field)" to just "Another identity"responses <- responses %>%mutate(gender =case_when( gender =="Another identity (Please specify in next field)"~"Another identity",TRUE~ gender ))
Demographic Analysis: Who filled out our survey?
A Breakdown of Survey Respondents by Age and Gender
Across respondents under 30 years old- our age group breakdown shows we mainly reached participants in the high school aged group (~49% of respondents were aged 0-17) and around 40% of participants were ages 18-24 and only about 10% were between 25-30.
We tracked a higher percent of female participants (~52%), followed by male participants (~43%) as well as non-binary/trans/different responses.
Code
#Standardizing agesresponses <- responses |>mutate(Age =case_when( Age %in%c("0 - 13", "13 - 17", "14 - 17") ~"0-17", Age %in%c("18-24", "18 - 24") ~"18-24",TRUE~ Age ))#library(dplyr)# Calculate percentagesage_summary <- responses |>count(Age) |>mutate(percent = n /sum(n) *100,label =paste0(Age, "\n(", round(percent, 1), "%)"))gender_summary <- responses |>count(gender) |>mutate(percent = n /sum(n) *100,label =paste0(gender, "\n(", round(percent, 1), "%)"))ggplot(data=responses, mapping =aes(x=Age, fill=gender)) +geom_bar() +scale_x_discrete(labels =setNames(age_summary$label, age_summary$Age)) +scale_fill_brewer(palette ="Set2", labels =setNames(gender_summary$label, gender_summary$gender)) +theme_minimal() +theme(text =element_text(family ='serif', size =14),plot.title =element_text(family ="serif", size =16),plot.background =element_rect(fill ="#F5F5F0", color =NA),panel.background =element_rect(fill ="#F5F5F0", color =NA),panel.border =element_blank(), # Remove frameaxis.text.x =element_text(angle =0, hjust =0.5, margin =margin(t =10)), # Add space above labelsaxis.title.x =element_text(margin =margin(b =20, t =20)), # Space above x-axis titleaxis.title.y =element_text(margin =margin(l =20, r =10)), # Space to right of y-axis titlepanel.spacing =unit(2, "lines") # General spacing ) +labs(x ='Age Group',y ='Count',fill ='Gender' )
A Breakdown of Survey Respondents by City
The graph below shows cities that appeared at least twice in our data. The survey received responses from multiple cities across the region. Oakland had the highest participation with 198 respondents, followed by Berkeley with 64. The remaining top cities showed considerably lower representation, ranging from 2 to 12 respondents each, including Richmond (12), San Leandro (10), and San Francisco (8). This distribution reflects strong concentration of responses from the Oakland-Berkeley area.
Code
#City Column Clean#cities with common dupesmain_cities <-c("Oakland", "Richmond", "Berkeley", "San Francisco", "Pittsburg", "San Leandro", "San Pablo", "Pinole", "Hercules", "Emeryville", "Alameda")find_city_match <-function(city_text, city_list){ cleaned <- city_text |>str_to_lower() |>str_trim() |>str_remove("(,.*|\\s+ca.*)$") |>str_squish() city_list_lower <-str_to_lower(city_list) exact_match <- city_list[city_list_lower == cleaned]if (length(exact_match) >0) return(exact_match[1])#distances of characters from cleaned to our list of main cities distances <-adist(cleaned, city_list_lower) # gives you distances between cleaned and cities best_match_idx <-which.min(distances) # gives you the index of the city with min val min_distance <-min(distances) # gives you vectors with min valsif(min_distance <=2) {return(city_list[best_match_idx]) }return(str_to_title(cleaned))}responses <- responses |>mutate(City =sapply(City, find_city_match, city_list = main_cities))city_counts <- responses |>count(City) |>filter(n>=2)responses |>filter(City %in% city_counts$City) |>ggplot(aes(y=fct_rev(fct_infreq(City)))) +geom_bar(fill ="#FC8D62FF") +geom_text(stat='count', aes(label=after_stat(count)), hjust=-0.2, family ='serif', size =4) +scale_x_continuous(expand =expansion(mult =c(0, 0.1)))+theme_minimal() +theme(text =element_text(family ='serif', size =14),plot.title =element_text(family ="serif", size =16),plot.background =element_rect(fill ="#F5F5F0", color =NA),panel.background =element_rect(fill ="#F5F5F0", color =NA),panel.border =element_blank(),axis.text.y =element_text(margin =margin(r =10)),axis.title.x =element_text(margin =margin(b =5, t =10)),axis.title.y =element_text(margin =margin(l =5, r =5)),panel.spacing =unit(2, "lines") ) +labs(y ='City',x ='Count' )
Assessing Knowledge: What do people know about the Port of Oakland?
Do people know where the Port of Oakland is located? (Answer: kind of)
Close to 40% of respondents indicated they were unsure of the Port’s location. About half (46.4%) mentioned Oakland generally. More specifically, ~14% mentioned West Oakland, ~9% referenced Jack London Square, and 0.9% specifically mentioned Middle Harbor. 9.6% provided other responses (e.g., Alameda, San Francisco, California, etc.).
Code
#Where is the Port of Oakland located?responses <- responses %>%mutate(response_lower =str_to_lower(str_trim(`Where.is.the.Port.of.Oakland.located..Please.put..not.sure..if.you.are.unsure.`)),# Remove extra spacesresponse_lower =str_squish(response_lower),# Check for each mention (can have multiple)mentions_not_sure =str_detect(response_lower, "not\\s*sure|notsure|n/a|idk|don'?t\\s*know|dont\\s*know|not\\s*totally\\s*sure|assuming|presumably|guessing|unsure") |str_detect(response_lower, "^n$"),mentions_oakland =str_detect(response_lower, "oak\\s*land|oakland"),mentions_west_oakland =str_detect(response_lower, "west\\s*oak\\s*land|westoakland|west\\s*oakland"),mentions_jack_london =str_detect(response_lower, "jack\\s*london|jacklondon"),mentions_middle_harbor =str_detect(response_lower, "middle\\s*harbor|middleharbor"),# Check if none of the above were mentionedmentions_other =!mentions_not_sure &!mentions_oakland &!mentions_west_oakland &!mentions_jack_london &!mentions_middle_harbor )# Summary statistics - count and percentage for each mentionsummary_stats <- responses %>%summarise(total_responses =n(),not_sure =sum(mentions_not_sure),not_sure_pct =round(100* not_sure / total_responses, 1),oakland =sum(mentions_oakland),oakland_pct =round(100* oakland / total_responses, 1),west_oakland =sum(mentions_west_oakland),west_oakland_pct =round(100* west_oakland / total_responses, 1),jack_london =sum(mentions_jack_london),jack_london_pct =round(100* jack_london / total_responses, 1),middle_harbor =sum(mentions_middle_harbor),middle_harbor_pct =round(100* middle_harbor / total_responses, 1),other =sum(mentions_other),other_pct =round(100* other / total_responses, 1) )# Create a tidy summary for easy readingsummary_tidy <-tibble(mention =c("Not Sure", "Oakland", "West Oakland", "Jack London", "Middle Harbor", "Other"),count =c(summary_stats$not_sure, summary_stats$oakland, summary_stats$west_oakland, summary_stats$jack_london, summary_stats$middle_harbor, summary_stats$other),percentage =c(summary_stats$not_sure_pct, summary_stats$oakland_pct, summary_stats$west_oakland_pct, summary_stats$jack_london_pct, summary_stats$middle_harbor_pct, summary_stats$other_pct)) %>%arrange(desc(count))# Print resultscat("MENTIONS BREAKDOWN:\n")
Oakland: 159 (46.4%)
Not Sure: 139 (40.5%)
West Oakland: 48 (14.0%)
Other: 32 (9.3%)
Jack London: 29 (8.5%)
Middle Harbor: 3 (0.9%)
Have youth heard of the Port of Oakland? (Answer: kind of)
Among 349 survey respondents, awareness and engagement with the Port of Oakland varied considerably. The majority (57.7%) had heard of the Port, while 26.2% had physically visited it. Direct engagement was lower, with 10.2% having toured the Port facilities and 6.4% having participated in a harbor tour.
This tells us: 42.3% of respondents learned about the Port of Oakland through this survey.
Code
library(dplyr)# Calculate percentages at each stagefunnel <-data.frame(Stage =c("Heard of Port", "Been to Port", "Toured Port", "Harbor Tour"),Count =c(sum(responses$heard =="Yes", na.rm =TRUE),sum(responses$been =="Yes", na.rm =TRUE),sum(responses$toured =="Yes", na.rm =TRUE),sum(responses$harbor =="Yes", na.rm =TRUE) )) %>%mutate(Percent = Count /nrow(responses) *100,Stage =factor(Stage, levels = Stage) )ggplot(funnel, aes(x = Stage, y = Count)) +geom_bar(stat ="identity", fill ="#66C2A5", width =0.7) +geom_text(aes(label =paste0(Count, "\n(", round(Percent, 1), "%)")), vjust =-0.5, family ='serif', size =4) +scale_y_continuous(expand =expansion(mult =c(0, 0.1))) +theme_minimal() +theme(text =element_text(family ='serif', size =14),plot.title =element_text(family ="serif", size =16, margin =margin(b =5, t =10)),plot.background =element_rect(fill ="#F5F5F0", color =NA),panel.background =element_rect(fill ="#F5F5F0", color =NA),panel.border =element_blank(),axis.text.x =element_text(angle =0, hjust =0.5, margin =margin(b =10)), axis.text.y =element_text(margin =margin(r =10, l =10)), axis.title.x =element_text(margin =margin(b =5, t =5)), axis.title.y =element_text(margin =margin(l =5, r =5)) ) +labs(title ="Port of Oakland Awareness and Engagement", subtitle ="Out of 349 survey respondents", x =NULL,y ="Number of Respondents" )
What associations do youth have with the Port of Oakland?
We asked: How do you think the Port of Oakland impacts the wider community?
Respondents identified diverse ways the Port of Oakland affects the wider community. The most frequently mentioned impact was jobs and economic contributions, followed by trade and connectivity benefits. Environmental and health concerns were also prominent themes, alongside responses indicating uncertain or limited knowledge about the Port’s role. Additional themes included community infrastructure, traffic and noise impacts, and various other considerations. This distribution reveals that while many recognize the Port’s economic importance, concerns about environmental impacts and gaps in public awareness remain significant.
Here is a visual and breakdown of the major themes:
Code
#Select questionwider_com <- responses %>%transmute(impact = wider_impact)#Remove blank/NA and read in stopwordswider_com <- wider_com |>filter(!is.na(impact), str_trim(impact) !="")data("stop_words")### Simple Theme Taggingthemes <- wider_com |>mutate(theme =case_when(# Jobs & Economy (keep as is)str_detect(str_to_lower(impact), "job|employment|economic|business|commerce|income|economy|creates jobs|brings jobs|opportunities|worker|career|wage|work|investment|money|fund|revenue") ~"Jobs/Economy",# Environmental/Health Issues (expand)str_detect(str_to_lower(impact), "air quality|air pollution|pollution|polluter|polluters|environment|health|emission|diesel|toxic|environmental|fuel|polution|burning|sick|water|environment") ~"Environment/Health",# Traffic & Noisestr_detect(str_to_lower(impact), "traffic|noise|truck|congest|highway|commute") ~"Traffic/Noise",# Trade & Global Connectionsstr_detect(str_to_lower(impact), "shipping|import|export|global|international|trade|market|supply|cargo|connects|ship|goods|products|deliver|resources|overseas|connect|transportation|travel|travels|goods|packages") ~"Trade/Connectivity",# Community & Infrastructure (expand)str_detect(str_to_lower(impact), "community|neighborhood|resident|infrastructure|identity|services|local|housing|school|living|real estate|skyline|families|people|cultural") ~"Community/Infrastructure",# Awareness/Knowledgestr_detect(str_to_lower(impact), "not sure|don't know|unsure|unclear|no idea|learning|hearing|no clue|n/a|uncertain|i dont know|i don't know|idk|IDK|wouldn't know") ~"Uncertain/Limited Knowledge",TRUE~"Other" ))library(crosstalk)library(plotly)library(DT)# Create shared datasd <- SharedData$new(themes)# Plotp <-plot_ly(sd, y =~theme) |>add_histogram() |>layout(title ="Click bars to filter responses",yaxis =list(title ="Number of Responses",categoryorder ="total ascending"# orders bars by frequency ))# Table showing individual responsesdt <-datatable(sd, options =list(pageLength =10),selection ='none', )# Display bothbscols(widths =12, # full width for each p, dt)
3 words that come up when you think about the Port of Oakland?
Here are the top words in a bar graph and wordcloud:
When asked to provide three words that come to mind about the Port of Oakland, respondents’ associations centered primarily on maritime infrastructure and operations. “Ship” was the most frequently mentioned term (134 times), followed by “cargo” (63) and “water” (57). Physical elements like “boat,” “container,” and “crane” also featured prominently. Economic terms such as “trade,” “economy,” “import,” and “export” appeared with moderate frequency, while “pollution” emerged as a notable concern (26 mentions). The word cloud reflects a strong association with the Port’s functional and physical characteristics, alongside some awareness of its economic role and environmental impact.
Code
# Load stop wordsdata("stop_words")# Process directly from responsesunigrams <- responses |>filter(!is.na(three_words), str_trim(three_words) !="") |>mutate(three_words =str_to_lower(three_words)) |>unnest_tokens(word, three_words) |>mutate(lemma =lemmatize_words(word)) |>filter(str_detect(lemma, "[a-z]"), nchar(lemma) >2) |>anti_join(stop_words, by =c("lemma"="word")) |>count(lemma, sort =TRUE)#I need a table like unigrams but filtered where n > 20top_uni <- unigrams |>slice_max(n, n =20)top_uni
Do you think the Port of Oakland is important to the Oakland Community?
Out of 349 responses, the vast majority viewed the Port positively, primarily citing:
Jobs/Economy: Respondents emphasized employment opportunities and economic benefits. Representative response: “Yes, it provides jobs, supports local businesses, and boosts the economy through trade.”
Trade/Connectivity: Many recognized the Port’s role in importing goods and connecting Oakland globally.
Environment/Health concerns: A notable minority raised concerns about pollution and air quality, often alongside economic benefits (“Mixed” sentiment).
Some respondents acknowledged unfamiliarity with the Port.
responses %>%select(importance_to_community, sentiment, themes) %>%datatable(filter ="top",options =list(pageLength =5),colnames =c("Response", "Sentiment", "Themes"),caption ="Community Responses: Port of Oakland Importance" )
Maritime and Trade Knowledge
Participants were asked about their knowledge of the maritime industry.
Just over half of respondents (55.4%) reported accurate knowledge about the maritime industry, while 23.0% indicated no knowledge and 21.6% expressed unclear or uncertain understanding. These results suggest a significant knowledge gap among nearly half of respondents.
Code
library(dplyr)library(stringr)# Classify responsesresponses <- responses %>%mutate(response_lower =str_to_lower(maritime_knowledge),# Classify knowledge levelknowledge_type =case_when(# Clear "no" or "don't know" responsesstr_detect(response_lower, "^no[,.]?$|^n$|^nope|^no i don|i don't know|i do not know|not sure|never heard|no idea|idk|im not sure|i'm not sure|No I do not") ~"No/Don't Know",# Accurate understanding (mentions key concepts)str_detect(response_lower, "ship|cargo|port|trade|transport|import|export|sea|ocean|logistics|goods|freight") ~"Knows (Accurate)",# Everything elseTRUE~"Unclear/Other" ) )# Summary tablesummary_table <- responses %>%count(knowledge_type) %>%mutate(percent =round(n /sum(n) *100, 1))print(summary_table)
Participants were asked: “How does Oakland connect to overall trade?”
Most respondents (approximately 80%) demonstrated a basic understanding of Oakland’s connection to global trade, recognizing the Port of Oakland as the primary link, although many responses were vague (‘it’s connected through the port’) without elaborating on how or why this matters to the community. The most common themes included:
Import/export hub - Respondents frequently described Oakland as a gateway for goods moving in and out of the U.S.
Asia-Pacific connection - Many specifically mentioned trade with Asia, particularly China, Japan, and South Korea
West Coast significance - Several noted Oakland’s role as one of the largest/busiest ports on the West Coast or in Northern California
Supply chain role - Some described how goods arrive by ship and are distributed inland via truck and rail
About 15-20% were unsure or unfamiliar with Oakland’s trade connections. A small number offered more nuanced responses, mentioning specific goods (agriculture, electronics, weapons), competition from other ports (LA, Seattle), or historical context.
Participants were asked about their understanding of how the Port of Oakland connects the Bay Area to global trade and maritime industry:
Most respondents understood the port’s basic function as a trade hub, though around 23% were unsure. A smaller subset demonstrated deeper knowledge of specific trade routes (Asia) or industries (agriculture, tech).
knows n pct
1 Don't Know 79 23
2 Has Understanding 264 77
Climate Associations
We asked youth: Do you think the Port of Oakland is impacting the local climate environment?
The overwhelming majority of respondents (90%) believe the Port of Oakland is impacting the local climate and environment, with only 4% saying no and 6% unsure.
Among those who said yes, the most commonly cited concerns were:
Air pollution - Emissions from ships, trucks, and cargo equipment were the dominant concern
Fossil fuels/greenhouse gases - Many specifically mentioned CO2, diesel, and climate change contributions
Water pollution - Oil spills, runoff, and impacts on marine ecosystems
Health impacts - Several connected pollution to asthma and respiratory issues in nearby communities (West Oakland, Chinatown)
Noise and traffic
There is strong consensus among youth that the port has environmental impacts.
believes_impact n pct
1 No 13 3.8
2 Unsure 21 6.1
3 Yes 309 90.1
When asked how they think the Port can reduce climate impact:
When asked how the Port of Oakland can reduce its environmental impact, the majority of respondents (~50%) suggested electrification and renewable energy solutions, including electric trucks, ships, and equipment, as well as transitioning to solar, wind, or cleaner fuels.
About 30% offered other suggestions, including stricter regulations, pollution monitoring, community engagement, habitat protection, and reducing overall shipping volume. Some respondents specifically mentioned stopping weapons shipments or halting airport expansion as environmental priorities.
Approximately 18% were unsure how to reduce impacts, and a small percentage (~2%) said no, either believing the port wasn’t impacting the environment or that reduction wasn’t possible.
how_to_reduce_impact n pct
1 Electrify/Renewable Energy 167 48.7
2 No 5 1.5
3 Other 110 32.1
4 Unsure 61 17.8
How can the Port Reduce its environmental impact?
The interactive chart below shows how respondents suggested the Port of Oakland could reduce its environmental impact, grouped by whether they believe the port is currently impacting the environment. Click on bars to filter the table and view individual responses.
Code
# Create shared datasd <- SharedData$new(responses |>select(believes_impact, how_to_reduce_impact, how_to_reduce_climate_impact))# Plot by solution type, colored by beliefp <-plot_ly(sd, y =~how_to_reduce_impact, color =~believes_impact) |>add_histogram() |>layout(title ="Click bars to filter responses",xaxis =list(title ="Count"),yaxis =list(title ="",categoryorder ="total ascending" ),barmode ="stack" )# Tabledt <-datatable(sd, options =list(pageLength =10),selection ='none',colnames =c("Believes Impact?", "Solution Category", "Full Response"))# Display both stackedbscols(widths =12, p, dt)
Thoughts on Electrification
We provided a definition for electrification on our survey and asked:
“Do you think electrification will decrease climate environmental impacts of the Port of Oakland?”
Most youth (66%) believe electrification will reduce the Port’s environmental impact, with another 20% saying it might help. Around 4% were skeptical, suggesting broad community support for the Port’s clean energy transition.
Code
#this is a drop down and easy to visualizeggplot(data = responses, mapping =aes(x =fct_infreq(electrification_impact))) +geom_bar(aes(y =after_stat(count/sum(count)*100)), fill ="#66C2A5") +geom_text(aes(y =after_stat(count/sum(count)*100), label =paste0(round(after_stat(count/sum(count)*100), 1), "%")),stat ="count", hjust =-0.2, size =3.5) +coord_flip() +labs(title ="Do you think electrification will decrease climate/environmental impacts of the Port?",x ="",y ="Percent" ) +theme_minimal() +theme(plot.title =element_text(family ='serif', size =10) ) +ylim(0, 100)
We asked about if they were aware of Clean Ports electrification efforts:
Most (~80%) respondents reported that they did not know about the $322 million in funding for electrification but a notable 14 percent did know about it and about 5% were unsure.
This also tells us: 86% of respondents learned about the $322M funding through this survey.
Code
#also a drop down - ggplot(data = responses, mapping =aes(x =fct_infreq(electrification_funding_knowledge), fill = electrification_funding_knowledge)) +geom_bar(aes(y =after_stat(count/sum(count)*100))) +geom_text(aes(y =after_stat(count/sum(count)*100), label =paste0(round(after_stat(count/sum(count)*100), 1), "%")),stat ="count", hjust =-0.2, size =3.5) +coord_flip() +scale_fill_brewer(palette ="Set2") +labs(title ="Did you know the Port received $322M for electrification?",subtitle ="Awareness of federal/state funding for Port sustainability initiatives",x ="",y ="Percent" ) +theme_minimal() +theme(legend.position ="none",plot.title =element_text(size =12, face ="bold"),plot.subtitle =element_text(size =10, color ="gray40") ) +ylim(0, 100)
Future Engagement and Opportunities
We asked youth what they wanted to learn more about the Port of Oakland?
There was high engagement with learning interest! When asked what they want to learn about the Port of Oakland, respondents showed strongest interest in environment and sustainability efforts, especially around electrification and pollution reduction. Jobs and careers was the second most common theme, with many asking about employment opportunities, internships, and training programs. Respondents also expressed curiosity about how the port operates, its community impact, and trade/cargo specifics.
Code
library(tidyverse)library(crosstalk)library(plotly)library(DT)responses <- responses |>mutate(learn_theme =case_when(str_detect(str_to_lower(what_do_you_want_to_learn_more), "^no$|^no,|nothing|not sure|idk|don't know|^na$|^n$|n/a|no thank") ~"Not Interested/Unsure",str_detect(str_to_lower(what_do_you_want_to_learn_more), "environment|sustain|green|climate|pollution|emission|clean energy|electri|renewable|eco|carbon") ~"Environment/Sustainability",str_detect(str_to_lower(what_do_you_want_to_learn_more), "job|career|work|employ|intern|opportunities|training|pay|paid") ~"Jobs/Careers",str_detect(str_to_lower(what_do_you_want_to_learn_more), "how it work|how they work|operate|function|logistics|run|day to day|daily") ~"Operations/How It Works",str_detect(str_to_lower(what_do_you_want_to_learn_more), "community|impact|affect|local|resident|neighborhood|health") ~"Community Impact",str_detect(str_to_lower(what_do_you_want_to_learn_more), "history|started|founded|when was") ~"History",str_detect(str_to_lower(what_do_you_want_to_learn_more), "cargo|goods|ship|import|export|trade|what comes|what goes") ~"Trade/Cargo",str_detect(str_to_lower(what_do_you_want_to_learn_more), "tech|automat|electric truck|crane|equipment|moderniz") ~"Technology",str_detect(str_to_lower(what_do_you_want_to_learn_more), "^yes$|^sure$|everything|anything|more about it") ~"General Interest",TRUE~"Other" ) )# Create shared datasd <- SharedData$new(responses |>select(learn_theme, what_do_you_want_to_learn_more))# Plotp <-plot_ly(sd, y =~learn_theme, color =I("#66C2A5")) |>add_histogram() |>layout(title =list(text ="What Do You Want to Learn About the Port?<br><sup>Click bars to filter responses</sup>",font =list(size =14) ),xaxis =list(title ="Number of Responses"),yaxis =list(title ="",categoryorder ="total ascending" ),plot_bgcolor ="white",paper_bgcolor ="white",margin =list(l =150) )# Tabledt <-datatable(sd, options =list(pageLength =10,dom ='ftp' ),selection ='none',class ='compact stripe hover',colnames =c("Theme", "Full Response"))# Display bothbscols(widths =12, p, dt)
Would you ever consider employment with the Port of Oakland?
Nearly 6 in 10 youth (58%) would consider working at the Port, with the largest group (37%) saying ‘Maybe’ indicating strong potential interest that could be converted through better career information and outreach programs.
Code
ggplot(data = responses, mapping =aes(x =fct_infreq(employment_consideration), fill = employment_consideration)) +geom_bar(aes(y =after_stat(count/sum(count)*100))) +geom_text(aes(y =after_stat(count/sum(count)*100), label =paste0(round(after_stat(count/sum(count)*100), 1), "%")),stat ="count", hjust =-0.2, size =3.5) +coord_flip() +scale_fill_brewer(palette ="Set2") +labs(title ="Would you ever consider employment with the Port of Oakland?",x ="",y ="Percent" ) +theme_minimal() +theme(legend.position ="none",plot.title =element_text(size =12, face ="bold"),plot.subtitle =element_text(size =10, color ="gray40") ) +ylim(0, 100)
What types of training or skills would be helpful for you or wider community to explore employment with the Port of Oakland?
Youth want hands-on technical training (many respondents noted technical/equipment operations and logistics/maritime operations training), but many don’t know what skills are needed - suggesting an opportunity for the Port to develop clearer career pathway information and expand outreach programs like internships and tours.
# 1. Code Yes/No for each engagement typeresponses <- responses |>mutate(follows_socials =case_when(str_detect(str_to_lower(following_socials), "^no|i do not|i don't|don't follow|didn't know") ~"No",str_detect(str_to_lower(following_socials), "^yes|instagram|twitter|facebook|tiktok|linkedin|X|x|IG|ig") ~"Yes",TRUE~"No" ),visited_website =case_when(str_detect(str_to_lower(website_visit), "^no|never|i have not|haven't") ~"No",str_detect(str_to_lower(website_visit), "^yes|visited|have visited|recently") ~"Yes",TRUE~"No" ),subscribed_newsletter =case_when(str_detect(str_to_lower(newsletter), "^no|not signed|didn't know|i am not|never heard") ~"No",str_detect(str_to_lower(newsletter), "^yes|signed up|subscribed|i'll sign|i will") ~"Yes",TRUE~"No" ) )# 2. Create summary for visualizationengagement_summary <- responses |>summarise(`Following Socials`=sum(follows_socials =="Yes"),`Visited Website`=sum(visited_website =="Yes"),`Newsletter Subscriber`=sum(subscribed_newsletter =="Yes") ) |>pivot_longer(everything(), names_to ="Engagement Type", values_to ="Yes") |>mutate(Total =nrow(responses),No = Total - Yes,Yes_pct =round(Yes/Total*100, 1) )# Stacked bar engagement_long <- engagement_summary |>select(`Engagement Type`, Yes, No) |>pivot_longer(cols =c(Yes, No), names_to ="Response", values_to ="Count")ggplot(engagement_long, aes(x =`Engagement Type`, y = Count, fill = Response)) +geom_col(position ="fill") +scale_fill_manual(values =c("Yes"="#FC8D62FF", "No"="#cccccc")) +scale_y_continuous(labels = scales::percent) +coord_flip() +labs(title ="Community Engagement with Port of Oakland",x ="",y ="" ) +theme_minimal()
I think it would be interesting to see why people say yes
Code
yes_responses <- responses |>filter(follows_socials =="Yes"| visited_website =="Yes"| subscribed_newsletter =="Yes") |>select(following_socials, website_visit, newsletter) datatable(yes_responses,options =list(pageLength =10, dom ='ftp'),colnames =c("Following Socials", "Website Visit", "Newsletter"),caption ="Responses from those who engaged with the Port through Socials or Website or Newsletter")
Increase in Knowledge
The survey itself served as an educational tool. Prior to the survey, only 14% of respondents were aware that the Port received $322 million in federal/state funding for electrification - meaning 86% learned about this investment through the survey. Similarly, while 58% had previously heard of the Port of Oakland, 42% of respondents were introduced to the Port through this survey. This highlights both a significant awareness gap and the potential for community outreach to increase knowledge about the Port’s sustainability initiatives and economic importance.
