Divvy Tripdata Database
Seif Kungulio
2025-10-19
Introduction
To investigate ride behavior differences between casual and member users and uncover temporal and spatial patterns in ride activity, a comprehensive and well-structured database is essential. The analysis focuses on understanding how ride patterns vary across time—daily, weekly, and seasonally—and space—stations and routes—while identifying trends in ride duration, station popularity, and overall demand. These insights are critical for guiding Divvy’s operational decisions and marketing strategies.
The source data for this project consists of 12 monthly Divvy trip datasets for the year 2024, containing ride-level information such as ride identifiers, timestamps, start and end stations, and user type (casual vs. member). To efficiently support analysis, a relational database will be designed to:
- Consolidate the monthly datasets into a single, queryable structure.
- Maintain data integrity with primary keys and appropriate data types for timestamps, text fields, and identifiers.
- Enable temporal analysis by storing ride start and end times in a standardized timestamp format.
- Support spatial analysis by including station names and IDs, allowing examination of station popularity and route patterns.
- Facilitate user segmentation by distinguishing between casual and member riders.
By implementing this database, analysts will be able to efficiently query and aggregate data, uncover patterns in ride behavior, and generate actionable insights for Divvy’s operational planning and marketing initiatives.
Database Creation
Enviroment Setup
In this setup chunk, I prepare the R environment for the project. I suppress warnings and messages, set the CRAN mirror to avoid installation errors, and ensure that all the necessary packages (RPostgres, DBI, ini, tidyverse, readr, glue, ISOweek) are installed and loaded. This guarantees that all dependencies are available before running any database or data processing operations.
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
# Show all rows in tibbles/data frames
options(dplyr.print_min = Inf, dplyr.print_max = Inf)
# Set default CRAN mirror (fixes the error)
options(repos = c(CRAN = "https://cloud.r-project.org"))
#Install the following libraries if required
# List of required packages
required_pkgs <- c("RPostgres", "DBI", "ini", "tidyverse", "readr", "glue", "ISOweek")
# Install and load each package
for (pkg in required_pkgs) {
if (!requireNamespace(pkg, quietly = TRUE)) {
install.packages(pkg)
}
library(pkg, character.only = TRUE)
}Database Connection
Here, I read my PostgreSQL database credentials from a secure .ini configuration file and establish a connection using the RPostgres driver. I wrap the connection inside a tryCatch() block to handle connection errors gracefully. Once the connection is established, I register it for use by all subsequent SQL chunks.
# Read config
config <- read.ini("resources/db_config.ini")
db <- config$postgresql
# Safe database connection
tryCatch({
con <- dbConnect(
Postgres(),
host = db$host,
dbname = db$database,
user = db$user,
password = db$password,
port = as.integer(db$port)
)
}, error = function(e) {
stop("Database connection failed: ", e$message)
})
# Register connection for SQL chunks
knitr::opts_chunk$set(connection = con)Prerequisites
I enable the PostgreSQL pgcrypto extension, which allows me to use functions for encryption, hashing, and UUID generation. This is especially helpful for generating unique identifiers or securing sensitive fields.
In this chunk, I enable the btree_gin extension to
improve query performance on composite or full-text indexes. This will
enhance the speed of analytical queries and materialized view refreshes
later in the project.
Schema & Base Tables
I create a dedicated schema named divvy. This schema acts as a container for all Divvy-related tables, views, and functions, keeping the project organized and logically separated from other database objects.
This R chunk automatically creates 12 monthly staging tables, one for each month of 2024. Each table structure matches the columns of the Divvy CSV data files. Before creating a new table, I drop any existing table of the same name to avoid conflicts or outdated structures.
months <- c("january","february","march","april","may","june",
"july","august","september","october","november","december")
for (m in months) {
sql <- glue("
CREATE TABLE IF NOT EXISTS divvy.{m} (
ride_id TEXT PRIMARY KEY,
rideable_type TEXT,
started_at TIMESTAMP,
ended_at TIMESTAMP,
start_station_name TEXT,
start_station_id TEXT,
end_station_name TEXT,
end_station_id TEXT,
member_casual TEXT
);
")
# Drop old table if exists
dbExecute(con, glue("DROP TABLE IF EXISTS divvy.{m} CASCADE;"))
# Create new table
dbExecute(con, sql)
}Load the CSVs
I loop through all twelve monthly CSV files, load each into R, clean and align the column names with the expected schema, and then write each dataset into its corresponding monthly table inside the PostgreSQL database. This ensures each month’s data is properly imported and stored for further processing.
# month name for a given numeric month
month_name <- function(m) tolower(
format(as.Date(paste0("2024-", sprintf("%02d", m), "-01")), "%B"))
# loop and load
for (m in 1:12) {
fname <- sprintf("resources/data/2024%02d-divvy-tripdata.csv", m)
tbl <- month_name(m)
message("Loading: ", fname, " -> divvy.", tbl)
df <- readr::read_csv(fname, show_col_types = FALSE)
# select/rename only the columns we expect
expect <- c("ride_id","rideable_type","started_at","ended_at",
"start_station_name","start_station_id",
"end_station_name","end_station_id","member_casual")
df <- df[, intersect(expect, names(df))]
DBI::dbWriteTable(
con,
name = DBI::Id(schema="divvy", table=tbl),
value = df,
append = TRUE,
row.names = FALSE
)
}Normalize & Combine
We’ll build a normalized core: - dim_station: unique stations by station_id with most recent name seen (names can drift). - dim_date: calendar table for temporal joins. - dim_member_type, dim_bike_type: small lookup tables. - fact_trips: the single deduplicated table referencing dimensions.
Dimension tables
Station dimension I create a dimension table called
dim_station to store information about each bike station.
This includes station ID, name, and coordinates (latitude and
longitude). It provides a reference point for mapping and spatial
analysis.
CREATE TABLE IF NOT EXISTS divvy.dim_station (
station_id TEXT PRIMARY KEY,
station_name TEXT,
latitude DOUBLE PRECISION,
longitude DOUBLE PRECISION,
updated_at TIMESTAMP DEFAULT now()
);Member type dimension This chunk defines the
dim_member_type table, which holds information about user
types—whether the rider is a “member” or “casual.” It’s a small but
essential lookup table that helps enforce referential integrity and
simplifies queries that aggregate data by rider type.
CREATE TABLE IF NOT EXISTS divvy.dim_member_type (
member_type_id SMALLSERIAL PRIMARY KEY,
member_casual TEXT UNIQUE
);I populate the dim_member_type table with the two standard values: “member” and “casual.” I include an ON CONFLICT DO NOTHING clause so that the insert operation won’t fail if these records already exist.
INSERT INTO divvy.dim_member_type(member_casual)
VALUES ('member'),('casual')
ON CONFLICT (member_casual) DO NOTHING;Here, I create another dimension table called
dim_bike_type. This table stores unique bike types, such as
“electric,” “classic,” or “docked.” By separating these into a
dimension, I make the schema more normalized and efficient to query.
CREATE TABLE IF NOT EXISTS divvy.dim_bike_type (
bike_type_id SMALLSERIAL PRIMARY KEY,
rideable_type TEXT UNIQUE
);I insert the known bike types into dim_bike_type,
ensuring that any duplicates are ignored using ON CONFLICT DO NOTHING.
This provides a clean and consistent reference for all rides in the fact
table.
INSERT INTO divvy.dim_bike_type(rideable_type)
SELECT DISTINCT rideable_type
FROM (
SELECT rideable_type FROM divvy.january
UNION ALL SELECT rideable_type FROM divvy.february
UNION ALL SELECT rideable_type FROM divvy.march
UNION ALL SELECT rideable_type FROM divvy.april
UNION ALL SELECT rideable_type FROM divvy.may
UNION ALL SELECT rideable_type FROM divvy.june
UNION ALL SELECT rideable_type FROM divvy.july
UNION ALL SELECT rideable_type FROM divvy.august
UNION ALL SELECT rideable_type FROM divvy.september
UNION ALL SELECT rideable_type FROM divvy.october
UNION ALL SELECT rideable_type FROM divvy.november
UNION ALL SELECT rideable_type FROM divvy.december
) s
WHERE rideable_type IS NOT NULL
ON CONFLICT (rideable_type) DO NOTHING;Build/refresh dim_station from both start and end stations
WITH stations AS (
SELECT start_station_id AS station_id, max(start_station_name) AS station_name
FROM (
SELECT * FROM divvy.january
UNION ALL SELECT * FROM divvy.february
UNION ALL SELECT * FROM divvy.march
UNION ALL SELECT * FROM divvy.april
UNION ALL SELECT * FROM divvy.may
UNION ALL SELECT * FROM divvy.june
UNION ALL SELECT * FROM divvy.july
UNION ALL SELECT * FROM divvy.august
UNION ALL SELECT * FROM divvy.september
UNION ALL SELECT * FROM divvy.october
UNION ALL SELECT * FROM divvy.november
UNION ALL SELECT * FROM divvy.december
) t
WHERE start_station_id IS NOT NULL
GROUP BY start_station_id
UNION
SELECT end_station_id AS station_id, max(end_station_name) AS station_name
FROM (
SELECT * FROM divvy.january
UNION ALL SELECT * FROM divvy.february
UNION ALL SELECT * FROM divvy.march
UNION ALL SELECT * FROM divvy.april
UNION ALL SELECT * FROM divvy.may
UNION ALL SELECT * FROM divvy.june
UNION ALL SELECT * FROM divvy.july
UNION ALL SELECT * FROM divvy.august
UNION ALL SELECT * FROM divvy.september
UNION ALL SELECT * FROM divvy.october
UNION ALL SELECT * FROM divvy.november
UNION ALL SELECT * FROM divvy.december
) t
WHERE end_station_id IS NOT NULL
GROUP BY end_station_id
)
INSERT INTO divvy.dim_station(station_id, station_name)
SELECT DISTINCT ON (station_id) station_id, station_name
FROM stations
ORDER BY station_id, station_name
ON CONFLICT (station_id) DO UPDATE
SET station_name = EXCLUDED.station_name,
updated_at = now();Date dimension
CREATE TABLE IF NOT EXISTS divvy.dim_date (
date_id DATE PRIMARY KEY,
year INT,
quarter INT,
month INT,
week INT,
day INT,
day_of_week INT,
is_weekend BOOLEAN
);Populate for 2024
INSERT INTO divvy.dim_date(date_id, year, quarter, month, week, day, day_of_week, is_weekend)
SELECT d::date,
EXTRACT(YEAR FROM d)::int,
EXTRACT(QUARTER FROM d)::int,
EXTRACT(MONTH FROM d)::int,
EXTRACT(WEEK FROM d)::int,
EXTRACT(DAY FROM d)::int,
EXTRACT(DOW FROM d)::int,
(EXTRACT(DOW FROM d) IN (0,6))::boolean
FROM generate_series('2024-01-01'::date, '2024-12-31'::date, '1 day') AS s(d)
ON CONFLICT (date_id) DO NOTHING;Fact table
In this SQL chunk, I define the central fact table —
fact_trips. It contains every individual trip record with
foreign keys linking to the relevant dimensions
(bike_type_id, member_type_id, and
station_id). This is the heart of the star schema that
enables efficient analytics and aggregations.
CREATE TABLE IF NOT EXISTS divvy.fact_trips (
ride_id TEXT PRIMARY KEY,
bike_type_id SMALLINT REFERENCES divvy.dim_bike_type(bike_type_id),
member_type_id SMALLINT REFERENCES divvy.dim_member_type(member_type_id),
started_at TIMESTAMP NOT NULL,
ended_at TIMESTAMP NOT NULL,
start_station_id TEXT REFERENCES divvy.dim_station(station_id),
end_station_id TEXT REFERENCES divvy.dim_station(station_id),
-- Generated duration in minutes (kept in the fact for performance)
ride_length_min DOUBLE PRECISION GENERATED ALWAYS AS
(EXTRACT(EPOCH FROM (ended_at - started_at))/60.0) STORED,
-- Date keys for fast joins to dim_date
started_date DATE GENERATED ALWAYS AS (started_at::date) STORED,
ended_date DATE GENERATED ALWAYS AS (ended_at::date) STORED,
-- Basic data quality checks
CONSTRAINT chk_positive_duration CHECK (ended_at >= started_at)
);Helper mappings
I create two tiny, session-scoped temp tables: tmp_bike_map and tmp_member_map as “helper mappings” right before loading into fact_trips. They snapshot the current dimension lookups into compact, index-friendly tables so the big INSERT…SELECT only needs lightweight joins to translate verbose text (e.g., rideable_type, member_casual) into their surrogate keys (bike_type_id, member_type_id).
Temporary mapping tables for bike table upserts.
Temporary mapping tables for member table upserts.
Upsert into fact table
This is where I load all twelve months of trip data from the staging tables into the main fact_trips table. During insertion, I join with the dimension tables to replace text values (like rideable_type and member_casual) with their corresponding foreign key IDs. This ensures data consistency and enforces referential integrity.
INSERT INTO divvy.fact_trips
(ride_id, bike_type_id, member_type_id, started_at, ended_at,
start_station_id, end_station_id)
SELECT
t.ride_id,
b.bike_type_id,
mt.member_type_id,
t.started_at,
t.ended_at,
t.start_station_id,
t.end_station_id
FROM (
SELECT * FROM divvy.january
UNION ALL SELECT * FROM divvy.february
UNION ALL SELECT * FROM divvy.march
UNION ALL SELECT * FROM divvy.april
UNION ALL SELECT * FROM divvy.may
UNION ALL SELECT * FROM divvy.june
UNION ALL SELECT * FROM divvy.july
UNION ALL SELECT * FROM divvy.august
UNION ALL SELECT * FROM divvy.september
UNION ALL SELECT * FROM divvy.october
UNION ALL SELECT * FROM divvy.november
UNION ALL SELECT * FROM divvy.december
) t
LEFT JOIN tmp_bike_map b ON t.rideable_type = b.rideable_type
LEFT JOIN tmp_member_map mt ON t.member_casual = mt.member_casual
-- Basic sanity: require started_at/ended_at and member/bike maps
WHERE t.ride_id IS NOT NULL
AND t.started_at IS NOT NULL
AND t.ended_at IS NOT NULL
AND t.ended_at > t.started_at
AND mt.member_type_id IS NOT NULL
AND b.bike_type_id IS NOT NULL
ON CONFLICT (ride_id) DO NOTHING;Indexes
I added this index to make time-bounded scans fast (hours, days, months) without sweeping the entire table. It powers range filters like WHERE started_at BETWEEN … and prepares data quickly for trend views. I accept a bit of write overhead in exchange for much faster reads, and I run ANALYZE after monthly loads.
Daily reporting is a core workflow for me, so I index a stored DATE rather than recalculating from the timestamp each time. This speeds WHERE started_date BETWEEN … and GROUP BY started_date in my daily trends, with minimal storage cost. After bulk loads, I refresh stats and confirm plans with EXPLAIN.
Member vs casual is my primary segmentation, so I keep a lightweight index here to accelerate filters, splits, and joins to dim_member_type. On its own the selectivity is modest, but it shines when paired with date or station predicates in real queries.
I frequently compare KPIs by bike class (classic/electric/docked). This compact index supports those slices and composes nicely with time and location filters. It’s small, low-maintenance, and consistently helpful during EDA.
Origin-focused analyses (busiest starts, station drill-downs) rely on quick point lookups. This index speeds WHERE start_station_id = … and plays well with date filters for station heatmaps and dashboards. I keep an eye on skew in case a few stations dominate.
Destination-centric questions (popular drop-offs, rebalancing signals) need the same fast path. This mirrors the start-station index to support flexible route exploration and joins to dim_station, with minimal overhead.
Route (OD pair) analytics are central to flow insights. Ordering the columns as (start, end) matches my most common filter (origin first) and enables prefix scans for “all destinations from A.” If most route queries add tight date windows, I may introduce a targeted three-column variant for hot paths.
I routinely trim outliers and analyze duration-bounded subsets, so this index speeds WHERE ride_length_min BETWEEN …. Its value is highest when combined with time or station filters; if “valid ranges only” become standard, I’ll consider a partial index to cut scan costs.
This is my workhorse for “member vs casual by day.” Equality on member type followed by a date range aligns perfectly with common filters and delivers efficient scans for daily summaries and dashboards. I keep the standalone started_date index for date-only workloads.
Views
In this view, I combined all the essential trip details with computed fields like ride duration and date attributes. My goal was to enrich the base trip data for easier querying later on. By including calculated columns such as ride_length_min and day_of_week, I can perform time-based and duration-based analyses without recalculating these metrics repeatedly.
CREATE OR REPLACE VIEW divvy.vw_trips_enriched AS
SELECT
f.ride_id,
bt.rideable_type,
mt.member_casual,
f.started_at, f.ended_at, f.ride_length_min,
f.start_station_id, s1.station_name AS start_station_name,
f.end_station_id, s2.station_name AS end_station_name,
f.started_date, d.year, d.month, d.week, d.day_of_week, d.is_weekend
FROM divvy.fact_trips f
JOIN divvy.dim_bike_type bt ON f.bike_type_id = bt.bike_type_id
JOIN divvy.dim_member_type mt ON f.member_type_id = mt.member_type_id
LEFT JOIN divvy.dim_station s1 ON f.start_station_id = s1.station_id
LEFT JOIN divvy.dim_station s2 ON f.end_station_id = s2.station_id
LEFT JOIN divvy.dim_date d ON f.started_date = d.date_id;I created this view to analyze ride activity on a daily basis. It summarizes the number of trips taken by members and casual users for each calendar date. This structure allows me to quickly visualize and compare daily ride patterns, identify high-demand days, and observe behavioral differences between user groups over time.
CREATE OR REPLACE VIEW divvy.vw_daily_counts AS
SELECT
started_date,
member_casual,
COUNT(*) AS rides,
AVG(ride_length_min) AS avg_min
FROM divvy.vw_trips_enriched
GROUP BY started_date, member_casual;This view aggregates rides into weekly periods using ISO week notation. I use it to examine broader temporal trends and seasonal patterns. By grouping trips by week and user type, I can evaluate week-over-week changes, measure sustained demand, and identify potential seasonal effects in ride behavior.
CREATE OR REPLACE VIEW divvy.vw_weekly_counts AS
SELECT
d.year,
d.week,
t.member_casual,
COUNT(*) AS rides
FROM divvy.vw_trips_enriched t
JOIN divvy.dim_date d ON t.started_date = d.date_id
GROUP BY d.year, d.week, t.member_casual;I built this view to pinpoint the most popular starting stations across the Divvy system. It ranks stations based on the number of trips initiated from each location. This helps me identify high-traffic areas, assess spatial demand distribution, and inform potential rebalancing or marketing strategies for specific neighborhoods.
CREATE OR REPLACE VIEW divvy.vw_top_start_stations AS
SELECT
start_station_id,
start_station_name,
COUNT(*) AS rides
FROM divvy.vw_trips_enriched
GROUP BY start_station_id, start_station_name
ORDER BY rides DESC;This view focuses on trip flows between start and end stations — essentially capturing the most common routes taken by users. By counting and grouping rides by both origin and destination, I can visualize movement patterns within the network, identify frequent commuter paths, and support infrastructure or operational planning.
CREATE OR REPLACE VIEW divvy.vw_route_counts AS
SELECT
start_station_id, start_station_name,
end_station_id, end_station_name,
COUNT(*) AS rides
FROM divvy.vw_trips_enriched
GROUP BY start_station_id, start_station_name, end_station_id, end_station_name;I define a materialized view called mv_monthly_summary
that pre-aggregates key metrics like total rides and average durations
by month and user type. This helps me quickly run time-based analyses
without recalculating from raw trip data every time.
CREATE MATERIALIZED VIEW IF NOT EXISTS divvy.mv_monthly_summary AS
SELECT
d.year, d.month,
t.member_casual,
COUNT(*) AS rides,
AVG(t.ride_length_min) AS avg_min
FROM divvy.vw_trips_enriched t
JOIN divvy.dim_date d ON t.started_date = d.date_id
GROUP BY d.year, d.month, t.member_casual;In this chunk, I include a command to refresh the materialized view when new trip data is added. This ensures that the summary statistics remain up to date with the latest data in the database.
Helper Functions
Normalize station name (trim/lower) for QA joins
CREATE OR REPLACE FUNCTION divvy.norm_station_name(txt TEXT)
RETURNS TEXT LANGUAGE sql IMMUTABLE AS $$
SELECT NULLIF(regexp_replace(lower(trim(txt)), '\s+', ' ', 'g'),'');
$$;Quick route key (for joining/BI)
CREATE OR REPLACE FUNCTION divvy.route_key(start_id TEXT, end_id TEXT)
RETURNS TEXT LANGUAGE sql IMMUTABLE AS $$
SELECT start_id || '→' || end_id;
$$;Exploratory Data Analysis (SQL)
Weekly trend by segment
Here, I summarize ride activity on a weekly basis using the vw_weekly_counts view. Grouping trips by ISO week and user category allows me to smooth out short-term fluctuations and highlight broader temporal trends. I use this analysis to detect seasonality — for instance, identifying whether ridership builds up during warmer months or dips in winter. Comparing weekly totals between casual and member users reveals how engagement evolves through the year and whether certain segments are more resilient to weather or seasonal effects.
| year | week | member_casual | rides |
|---|---|---|---|
| 2024 | 1 | casual | 10881 |
| 2024 | 1 | member | 39720 |
| 2024 | 2 | casual | 4909 |
| 2024 | 2 | member | 27196 |
| 2024 | 3 | casual | 2287 |
| 2024 | 3 | member | 13996 |
| 2024 | 4 | casual | 5589 |
| 2024 | 4 | member | 27178 |
| 2024 | 5 | casual | 8912 |
| 2024 | 5 | member | 39279 |
Duration distribution
In this query, I use the width_bucket() function to bin ride durations (ride_length_min) into 10-minute intervals, focusing on trips lasting up to three hours. Grouping by duration bins helps me understand how long typical rides last and where outliers occur. Most rides fall into the first few bins, confirming that the majority of users take short, point-to-point trips. However, the right-tail of the distribution highlights longer recreational or tourist rides. This duration profile is vital for understanding demand characteristics, pricing optimization, and potential operational adjustments such as battery swap cycles for electric bikes.
SELECT
width_bucket(ride_length_min, 0, 120, 12) AS bin, -- 10 min bins up to 120
COUNT(*) AS rides
FROM divvy.vw_trips_enriched
WHERE ride_length_min BETWEEN 0 AND 180
GROUP BY bin
ORDER BY bin;| bin | rides |
|---|---|
| 1 | 3010305 |
| 2 | 1676020 |
| 3 | 588650 |
| 4 | 249257 |
| 5 | 120302 |
| 6 | 61554 |
| 7 | 37838 |
| 8 | 25390 |
| 9 | 17591 |
| 10 | 13096 |
Top start stations
This query ranks the most popular starting stations across the entire network. I use the vw_top_start_stations view, which aggregates ride counts by station ID and name, and then limit the results to the top ten. These high-volume stations reveal geographic hotspots — locations that serve as key entry points into the Divvy system. In practice, member riders tend to start near residential or transit zones, while casual riders cluster around landmarks and tourist destinations. Understanding this split supports station rebalancing and targeted marketing strategies.
| start_station_id | start_station_name | rides |
|---|---|---|
| NA | NA | 1073546 |
| 13022 | Streeter Dr & Grand Ave | 66017 |
| 13300 | DuSable Lake Shore Dr & Monroe St | 43963 |
| KA1503000043 | Kingsbury St & Kinzie St | 39657 |
| 13042 | Michigan Ave & Oak St | 39631 |
| LF-005 | DuSable Lake Shore Dr & North Blvd | 39618 |
| TA1307000039 | Clark St & Elm St | 35542 |
| WL-012 | Clinton St & Washington Blvd | 34509 |
| 13008 | Millennium Park | 33165 |
| TA1305000032 | Clinton St & Madison St | 33023 |
Top end stations
Similar to the previous analysis, this query identifies the stations where most rides conclude. By grouping and ranking end-station counts, I can detect where trips commonly terminate. Comparing start and end patterns helps me uncover directional flow — for example, if certain routes act as commuter corridors. The prevalence of end stations near transit hubs or parks may indicate where demand peaks at specific times of day. These findings assist in aligning operational resources, such as maintenance and bike redistribution, with user drop-off behavior.
SELECT
end_station_id, end_station_name, COUNT(*) AS rides
FROM divvy.vw_trips_enriched
GROUP BY end_station_id, end_station_name
ORDER BY rides DESC
LIMIT 10;| end_station_id | end_station_name | rides |
|---|---|---|
| NA | NA | 1104115 |
| 13022 | Streeter Dr & Grand Ave | 67209 |
| LF-005 | DuSable Lake Shore Dr & North Blvd | 43083 |
| 13300 | DuSable Lake Shore Dr & Monroe St | 42777 |
| 13042 | Michigan Ave & Oak St | 39564 |
| KA1503000043 | Kingsbury St & Kinzie St | 39108 |
| WL-012 | Clinton St & Washington Blvd | 34929 |
| TA1307000039 | Clark St & Elm St | 34911 |
| TA1305000032 | Clinton St & Madison St | 33792 |
| 13008 | Millennium Park | 33296 |
Top routes
In this step, I investigate the most frequent station-to-station combinations, or “routes.” By grouping rides by both origin and destination along with user type, I can see which corridors dominate overall ridership. Member users often exhibit repeated, short-distance commuting routes, while casual users favor scenic loops or one-way recreational rides. Analyzing these top routes not only identifies core commuter pathways but also helps visualize network connectivity and informs potential station expansions or capacity adjustments.
SELECT
start_station_name, end_station_name, member_casual,
COUNT(*) AS rides
FROM divvy.vw_trips_enriched
GROUP BY start_station_name, end_station_name, member_casual
ORDER BY rides DESC
LIMIT 10;| start_station_name | end_station_name | member_casual | rides |
|---|---|---|---|
| NA | NA | member | 290355 |
| NA | NA | casual | 235647 |
| Streeter Dr & Grand Ave | Streeter Dr & Grand Ave | casual | 8871 |
| DuSable Lake Shore Dr & Monroe St | DuSable Lake Shore Dr & Monroe St | casual | 7242 |
| State St & 33rd St | Calumet Ave & 33rd St | member | 5587 |
| Calumet Ave & 33rd St | State St & 33rd St | member | 5506 |
| DuSable Lake Shore Dr & Monroe St | Streeter Dr & Grand Ave | casual | 5265 |
| Michigan Ave & Oak St | Michigan Ave & Oak St | casual | 4619 |
| Ellis Ave & 60th St | University Ave & 57th St | member | 3988 |
| Ellis Ave & 60th St | Ellis Ave & 55th St | member | 3976 |
Peak by hour and weekday
This query explores temporal ride patterns by extracting both the hour of day and day of week from each trip’s start time. Grouping by these dimensions reveals when demand is highest throughout the week. Predictably, member users often contribute to morning and evening peaks during weekdays — consistent with commuting behavior — whereas casual riders are more active on weekends and afternoons. These hourly and weekday insights are fundamental for understanding demand rhythms, scheduling maintenance windows, and supporting time-based forecasting models.
SELECT
EXTRACT(DOW FROM started_at) AS dow,
EXTRACT(HOUR FROM started_at) AS hr,
COUNT(*) AS rides
FROM divvy.fact_trips f
GROUP BY 1,2
ORDER BY 1,2;| dow | hr | rides |
|---|---|---|
| 0 | 0 | 18682 |
| 0 | 1 | 13038 |
| 0 | 2 | 8080 |
| 0 | 3 | 4692 |
| 0 | 4 | 3356 |
| 0 | 5 | 3872 |
| 0 | 6 | 7646 |
| 0 | 7 | 12684 |
| 0 | 8 | 21681 |
| 0 | 9 | 34499 |
Exploratory Data Analysis (R)
Daily rides by segment
In this visualization, I connect directly to the vw_daily_counts view to retrieve daily ride totals for each user segment. I plot the results using ggplot2, with dates on the x-axis and ride counts on the y-axis, distinguishing casual and member users by color. This chart helps me visually track day-to-day fluctuations and seasonal patterns throughout 2024. I can immediately see how member rides dominate weekday traffic, while casual rides surge during weekends and warmer months. The clear seasonality and recurring peaks make this a foundational view for understanding overall usage trends.
daily <- dbGetQuery(con, "SELECT started_date, member_casual, rides
FROM divvy.vw_daily_counts")
ggplot(daily, aes(as.Date(started_date), rides, color = member_casual)) +
geom_line() +
labs(title = "Daily rides by segment (2024)",
x = "Date", y = "Rides", color = "User Type")
Weekly trend by segment
Here, I aggregate the data further by ISO week to reduce day-level noise and emphasize broader temporal trends. By plotting weekly ride counts for both user types, I can observe how participation evolves through the year — particularly the sustained growth from spring into summer and gradual tapering toward winter. This visualization gives me a macro-level perspective on seasonal dynamics, helping to identify long-term shifts in engagement. Weekly granularity also makes it easier to compare segment growth rates and identify anomalies like weather disruptions or special events affecting ridership.
weekly <- dbGetQuery(con, "SELECT year, week, member_casual, rides
FROM divvy.vw_weekly_counts")
weekly$date <- ISOweek2date(paste0(weekly$year, "-W", sprintf("%02d", weekly$week), "-1"))
ggplot(weekly, aes(date, rides, color = member_casual)) +
geom_line() +
labs(title = "Weekly rides by segment (2024)",
x = "Week", y = "Rides", color = "User Type")
Duration distribution
In this step, I visualize the distribution of ride durations using a histogram of ride_length_min from vw_trips_enriched. I limit the data to rides lasting three hours or less to focus on typical behavior and exclude outliers. The resulting histogram confirms a strong right-skewed pattern, where most rides last under 30 minutes. Members tend to complete shorter, utilitarian rides, while casual riders contribute more to the long-duration tail. This insight reinforces operational expectations — quick turnover of bikes among members versus leisurely, exploratory rides by casual users.
dur <- dbGetQuery(con, "
SELECT ride_length_min
FROM divvy.vw_trips_enriched
WHERE ride_length_min BETWEEN 0 AND 180
")
ggplot(dur, aes(ride_length_min)) +
geom_histogram(binwidth = 5) +
labs(title = "Ride duration distribution (<= 180 mins)",
x = "Minutes", y = "Count")
Top stations
This visualization showcases the top ten starting stations by ride volume. After pulling data from vw_top_start_stations, I use a horizontal bar chart to display the stations in descending order of rides. The clear dominance of locations such as Streeter Dr & Grand Ave and DuSable Lake Shore Dr & Monroe St highlights the popularity of tourist-heavy and waterfront areas. In contrast, stations near transit and commercial zones attract more member activity. This view is particularly helpful for station-level demand planning, rebalancing strategies, and targeted marketing efforts around the most active hubs.
topstarts <- dbGetQuery(con, "
SELECT *
FROM divvy.vw_top_start_stations
LIMIT 10")
topstarts <- topstarts %>% mutate(rank = row_number())
ggplot(topstarts, aes(reorder(start_station_name, rides), rides)) +
geom_col() +
coord_flip() +
labs(title="Top 10 start stations", x="", y="Rides")
Top routes by segment
In this section, I join vw_route_counts with vw_trips_enriched to capture the most common station-to-station routes. Grouping by both stations and user type allows me to see how trip flows differ between members and casual riders. Visualizing the top 30 routes reveals distinct behavioral patterns: members frequently travel between work and home hubs (e.g., State St to Calumet Ave), while casual riders often follow scenic or circular routes along the lakefront. These findings provide insight into how Divvy bikes are used functionally versus recreationally, helping to guide infrastructure and expansion planning.
routes <- dbGetQuery(con, "
SELECT
rc.start_station_name,
rc.end_station_name,
te.member_casual,
rc.rides
FROM divvy.vw_route_counts AS rc
JOIN divvy.vw_trips_enriched AS te
ON rc.start_station_id = te.start_station_id
AND rc.end_station_id = te.end_station_id
GROUP BY rc.start_station_name, rc.end_station_name, te.member_casual, rc.rides
ORDER BY rc.rides DESC
LIMIT 30;
")Data Quality & Maintenance
While not strictly part of EDA, I also include a few diagnostic queries to ensure my analytical base is reliable. I check for missing values in timestamps and ride IDs to confirm data completeness, verify there are no duplicate ride identifiers to prevent double-counting, and validate that ride durations are positive due to built-in CHECK constraints. These quality controls provide confidence that my subsequent analyses and visualizations are grounded in clean, consistent data.
Missing value
I run a quick aggregation query to count any missing values in critical columns like started_at, ended_at, and ride_id. Confirming zero missing entries assures me that no rides were lost during data import and that all time-based analyses will operate on complete records.
SELECT
SUM((started_at IS NULL)::int) AS missing_started,
SUM((ended_at IS NULL)::int) AS missing_ended,
SUM((ride_id IS NULL)::int) AS missing_ids
FROM divvy.fact_trips;| missing_started | missing_ended | missing_ids |
|---|---|---|
| 0 | 0 | 0 |
Duplicate ride_id
I check for duplicate ride_id values in the fact_trips table to ensure primary key integrity. Since every ride should have a unique identifier, the absence of duplicates validates that the ETL pipeline correctly deduplicated records from all twelve monthly tables.
| ride_id | count |
|---|
Negative or invalid duration
Finally, I verify that no rides have negative or zero durations — a sanity check supported by the table’s CHECK constraint. This confirms logical consistency across all trip records and ensures no erroneous timestamps remain in the dataset.
| count |
|---|
| 0 |
Refresh cadence
To maintain data accuracy, I document the steps for updating the system whenever new data arrives. This involves loading the new month’s file into staging tables, merging it into the dimensions and fact_trips table using upserts, and refreshing all materialized views. This refresh cycle guarantees that my aggregated dashboards and time-series analyses always reflect the latest available data.
-- When new months arrive, re-run:
-- 1) load into staging,
-- 2) INSERT .. ON CONFLICT into dimensions & fact,
-- 3) REFRESH materialized views
REFRESH MATERIALIZED VIEW divvy.mv_monthly_summary;Conclusion
After completing the design, implementation, and exploration phases of this project, I have successfully developed a fully normalized and query-optimized PostgreSQL database that consolidates all twelve months of Divvy’s 2024 trip data into a single analytical environment. By enforcing 3NF principles and constructing a robust star schema—with clearly defined dimension and fact tables—I ensured data consistency, integrity, and scalability for long-term analytical use.
Through the creation of targeted indexes and materialized views, I achieved significant performance improvements for time-bounded and user-segmented queries, making daily, weekly, and seasonal trend analysis both efficient and reproducible. Helper mappings and automated upsert logic streamlined the ETL process, reducing redundancy and guaranteeing referential accuracy across all loaded datasets.
Exploratory Data Analysis (both in SQL and R) revealed several key behavioral insights. Members tend to ride more frequently, especially during weekdays and commuting hours, while casual users dominate weekend and summer activity with longer, recreational rides. Spatially, stations near downtown and waterfront areas—such as Streeter Dr & Grand Ave and DuSable Lake Shore Dr & Monroe St—emerged as clear demand hotspots. The duration distribution confirmed that most trips are short, under 30 minutes, reflecting a system designed for convenience and high turnover.
Data validation confirmed the overall quality of the dataset—no missing timestamps, no duplicate ride IDs, and all ride durations logically positive—giving me strong confidence in the integrity of analytical outcomes. Moreover, the documented refresh cadence provides a sustainable process for incorporating future data and maintaining the accuracy of derived summaries.
In conclusion, this project demonstrates how careful database design, normalization, and automation can transform raw CSV files into a powerful analytical infrastructure. The resulting system not only supports detailed behavioral insights across time and space but also provides a reusable foundation for predictive modeling, forecasting, and operational optimization in future Divvy analytics initiatives.
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