Pain at the Pump - a Closer Look at Hawaii's High Fuel Prices

Title Photo


Hawaii. Sunshine. Beautiful beaches. Mai Tais. These are a few of the great motivators for moving to one of America's favorite vacation destinations. However, Hawaii has some of the most expensive consumer products in the nation. According to, when compared to New York City, goods in Honolulu are more expensive:

  • 1 liter of whole fat milk: +41%
  • 1 kg (2 lbs) of apples: +68%
  • Bread for 2 people for 1 day: +67%

In addition to exorbitant food prices, Hawaii currently holds the crown of having the highest fuel prices in the entire United States, according to The Aloha state has long held the reputation of having the most expensive fuel in the land. However, until recently, such trends have been difficult to quantify. In order to better analyze datasets such as Hawaiian fuel prices, the US government in 2009 established a data collection website, Datasets are available online to conduct research, develop web applications, and design data visualizations, on a variety of topics ranging from agriculture, to manufacturing, to health, among many other.

These datasets are published using the Socrata Open Data Format. Socrata is a Seattle based company that develops software for government agencies to publish and manage their data in an open format. According to their website, the Socrata Open Data Format is used by the US Federal government, 25 US states, 300+ US cities, and contains 4,000+ datasets for numerous US counties.

Hawaiian Fuel Prices Dataset

Let us take a look at a dataset from which looks at Hawaiian fuel prices.

From 2006 to 2012, the State of Hawaii compiled AAA fuel prices for each of these fuel types:

Diesel, Gasoline - Regular, Gasoline - Midgrade, Gasoline - Premium

In turn, each of these fuel prices were recorded for these locations:

Hilo, Honolulu, Wailuku, US, State of Hawaii

The dataset from can be found here:

On the website, datasets can be downloaded as a CSV, RDF, JSON, or a XML file. To help interpret this data, the user is given the option of opening the CSV file with either CartoDB or plotly.


Mapping software CartoDB does not support plotting datasets (in this case gas prices of Hawaii) over some time period.


This visualization tool allows the user to display the relationship of gas prices over time; however, without extensively manipulating the raw data set, each location is allowed to be compared with only one fuel type at a time.

We will quickly run through plotting this dataset in plotly.

Once you click on the above dataset, you are given the option of choosing preview, plotly, or CartoDB. Choose plotly.


Once the raw data is opened via plotly, the user must select Filter from Data Tools, as shown below.

Figure 1

Next, choose Filter by, in our case for example, Gasoline - Regular. You must click choose as x for Fuel so that plotly knows which column to filter.

Figure 2

Finally, to output the data, the user must select Group By and choose Month_of_Price as the x axis, County as G (this will separate the prices of fuel for each location), and the Price as the y axis.

Figure 3

The output will look as is shown below. The graph is relatively easy to interpret. The user can see that Gasoline - Regular fuel prices in Hawaii have for the last 6 years steadily remained more expensive than US average prices. The main drawback of using plotly to process datasets from is the extensive time and effort it would take to create outputs for each of every fuel types. The same time-consuming steps would have to be taken for analyzing Diesel, Gasoline - Midgrade, and Gasoline - Premium between all 5 locations. The same cumbersome process would have to be followed for comparing fuel types for each particular location. Additionally, data in plotly is static, that is every time the data is updated, everything will need to be re-plotted.

Figure 4

Axibase Time Series Database

Processing datasets using ATSD is much less cumbersome. Processing the same data with ATSD is less time consuming because its collection tool has built-in heuristics to handle the format in which datasets are published, namely the Socrata Open Data Format. When loading data for a particular dataset the collector uses Socrata metadata to understand the meaning of columns and automatically extract dates, times, and categories from the data files. Besides, ATSD stores the data in the user's own database so that this public data can be combined with internal data sources as well as mixed and matched across different datasets. Once you install ATSD, you don't have to:

  • Add additional datasets from
  • Manipulate and design table schema
  • Provision an application server
  • Write programs to parse and digest these types of files.

Rather, you can configure a scheduled job to retrieve the file from the specified endpoint and have ATSD parse it according to pre-defined rules. Once you have raw data in ATSD, creating and sharing reports with built-in widgets is fairly trivial. The reports will be continuously updated as new data comes in.

With ATSD, the user is able display the dataset in an easily understandable manner. The below figure shows each fuel type for each of the 5 locations.

Figure 5

The dataset can be sorted by location and/or fuel type, and the user can easily toggle through comparing different scenarios. The next 2 figures show outputs comparing fuel types at Hilo and Diesel prices by location, respectively.

Figure 6

Figure 7

Here, you can explore the complete dataset for Hawaiian fuel prices using our portal:

View in ChartLab

Creating Custom Portals

Custom portals can be created from the default portal. The user has the capability to change or display certain aspects of the dataset to their liking. For example, the user may change graph styling, such as color, graph type, and other display options.

Likewise, by customizing the data the way you want, you can filter out any unnecessary information. If, for example, you are interested only in fuel prices at Hilo, you can customize your portal to only show that information without the effort to toggle through for it.

A blank, customizable portal for your use can be found here: BLANK

The default portal, from which you can customize the dataset results, again can be found here: DEFAULT

We will walk through a brief example on how to customize the default dataset to only display fuel prices at Hilo.

Example 1

  • Open the blank portal and copy configuration section from the default portal. Delete the entity line.

  • In the blank portal change the Source from Random to ATSD.

    Figure 11

  • Copy these settings to the blank portal under the [group] level.

        type = chart
        legend-position = top
          entity =
          metric =
          county =
          fuel =
  • Copy the entity name from the default portal into the blank portal (in this case dqp6-3idi).

  • In the blank portal enter price into metric. This will display the price of fuel as the y column.

  • In the blank portal enter in the county and fuel. In this case, enter Hilo for county and * for fuel (* is the wildcard symbol).

  • Your blank portal should now look as is shown below. Hit run to output your customized graph.

Figure 12

Your customized graph should look something like this:

Figure 13

Here, you can explore the this graph:

View in ChartLab

Now, we will quickly walk through creating a histogram to display the fuel price differences for Diesel fuel between Hilo and the US.

Example 2

  • Follow the first five steps in Example 1

  • In the blank portal enter in the county and fuel. In this case, enter Hilo for county and Diesel for fuel.

  • Since we will be finding the difference between Hilo and US Diesel prices, we will need to make a second series. Copy and paste the existing series and change the name of the county to US. At this point your portal should look something like this:

    Figure 14

Next, we need to make a new series to find the difference between US and Hilo Diesel prices.

  • In the Hilo series, enter in alias = s1. In the US series, enter in alias = s2. For both series enter display = false.
  • Create a new series. Enter label = Hilo over US Diesel Surcharges and value = value(s1) - value(s2).

At this point your portal should look something like this:

Figure 15

Your custom graph should look like this:

Figure 16

Now, you have the options of customizing your output further, by editing features such as color, graph type, and graph extents.

  • Change the minimum price to 0. Enter min-range = 0.
  • Change the graph type to columns. Enter mode = column.
  • To showcase the exorbitant gas prices at Hilo, enter color = red.
  • Under configuration (at the very top) enter height-units = 2 to increase the size of your graph.
  • Press Run.

Your customized graph should look like this:

Figure 17

Here, you can explore this graph:

View in ChartLab

Various additional settings may be applied to create outputs that fit your needs. Below is a link to settings that may be applied to create custom charts:

Adding/Combining a Second Dataset

Exploring the complete dataset for fuel prices, we can see that, generally speaking, Wailuku is more expensive for any fuel type than Hilo and Honolulu. Are products generally more expensive in Wailuku than the other islands, or is this simply an anomaly? One way we can investigate further is to incorporate a second dataset with another consumer product in Hawaii. If the price of this second consumer item is also more expensive in Wailuku than in Hali and Honolulu, then we may not be dealing with an anomaly, but quite possibly a trend.

From the website, let us choose Hawaii electricity prices as our second dataset.

From 2008 to 2012, the State of Hawaii collected electricity prices (in cents/kwh) for each of the Hawaiian islands:

Hawaii, Kauai, Lanai, Maui, Molokai, Oahu

In turn, each island had it's electricity broken into these sectors:

All Sectors, Commercial, Residential, Street Lights

Here, you can explore the portal for this dataset:

View in ChartLab

Next, let's look at which areas we can compare.

The specified locations for the 2 datasets are different: one compared cities, while the other compared islands. Areas for which we have both datasets are marked in red in the figure below.


To briefly demonstrate our capabilities, let us compare Diesel prices at Honolulu, Wailuku, and Hilo with the Residential electricity rates at their respective corresponding islands (Oahu, Maui, Hawaii).

Figure 10

Again, guidelines for setting up the various settings to create outputs can be found here.

This graph is a standard distribution of the datasets plotted side by side. As was stated previously, Wailuku was found to generally have the most expensive fuel, which is shown here graphically. When looking at the electricity rates, we can see that the most expensive location is Hawaii island. So, based off our quick example, we cannot say that there is a trend of consumer products being more expensive in Wailuku (or Maui island) than others. However, this quickly shows the user the possibilities of combining and comparing multiple datasets.

Here you can explore the portal of this comparison:

View in ChartLab

Additional Examples

Here is a table of additional datasets from that you can explore using Axibase's portal:

State dataset Axibase Portal
Illinois Abortion Demographics, 1995-2012 Portal
Maryland Anne Arundel County Crime Rate By Type Portal
New York Automobiles Annual Imports and Exports Through Port Authority of NY NJ Maritime Terminals: Beginning 2000 Portal
Maryland Employment Figures Portal
New York Solar Photovoltaic (PV) Incentive Program Completed Projects by City and Contractor: Beginning 2010 Portal
Hawaii Solid Waste Recycled (in tons) Portal
Iowa Math And Reading Proficiency by School Year, Public School District and Grade Level Portal
Connecticut Sales and Use Tax per Town by NAICS (2013 and 2014) Portal
Maryland Per Capita Electricity Consumption Portal
Maryland MVA Vehicle Sales Counts by Month for CY 2002-2015 Portal
Connecticut DAS HR Almanac - Executive Branch Employment By Race Portal
New York Scholarship Recipients and Dollars by Sector Group: Beginning 2009 Portal
New York Public Assistance and SNAP Fraud Prevention Performance Measures: Beginning 2013 Portal
Maryland Maryland Veterans Unemployment Rate Portal
Maryland Trips Taken on Public Transit by Transit Type - Monthly Total Trips Portal
Iowa Employee Compensation by Industry in Iowa Portal

If you would like to view a dataset without installing the ATSD software, please contact us and we would be happy to add it to this table!

Action Items

Below are the steps to follow to install ATSD:

  1. Install the database on a virtual machine or in a Linux container.
  2. Install Axibase Collector and configure Collector to write data into your ATSD instance.
  3. Import SOCRATA Job into Axibase Collector.
  4. Add your desired dataset to the job to enable data collection. Click Run to collect data for the first time.
  5. Log in to ATSD and open a sample Socrata portal to explore the data.

If you require assistance in installing this software or have any questions, please feel free to contact us and we would be happy to help!