Google Fusion Tables: Online Data Management and Publishing

Google Fusion Tables is a powerful tool for managing and publishing data online. It allows you to import, store, and analyze data from various sources, including Google Sheets and other spreadsheets.

With Google Fusion Tables, you can easily create and customize tables to suit your needs. You can also add and edit data, as well as format tables with colors, fonts, and other visual elements.

One of the key benefits of Google Fusion Tables is its ability to easily share and publish data with others. You can create interactive tables that can be embedded on websites, blogs, or other online platforms.

By using Google Fusion Tables, you can take your data analysis to the next level and make it more accessible to others.

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Data Management is a crucial aspect of working with Google Fusion Tables. You can store up to 500,000 rows of data in a table, making it suitable for large datasets.

Fusion Tables automatically indexes your data, allowing for fast querying and filtering. This means you can quickly find specific information within your data.

Data validation is also a key feature, enabling you to restrict the types of data that can be entered into a column. For example, you can specify that a column should only contain dates or numbers.

Fusion Tables allowed users to reuse publicly published data sets or data sets shared with them by creating visualizations and filtered views in new tabs.

These views wouldn't affect the original file for the owner or others, but would appear whenever the user who created them opened the file.

The new views were indicated with a dotted line outline, making it easy to distinguish them from the original data.

Users could not edit the read-only data set, but they could still create new views and visualizations without affecting the original data.

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Uploading data to Google Fusion Tables is a straightforward process. You can log in to Google Docs, create a new Table, and upload your file from there.

Excel and CSV files are the most commonly used formats for uploading data to Fusion Tables. You can also upload KML files to bring in spatial information like locations or polygon definitions.

Only numeric fields can be used to categorize data, so it's a good idea to create these fields before uploading your data.

Here are the steps to upload data to Fusion Tables:

Merging tables is a powerful tool in data management. It allows you to combine data from multiple sources into a single, cohesive dataset.

To merge tables, you need to select the tables you want to merge. In the example, the Population table is merged with a table containing zip code boundaries. This is done by selecting Merge in the Population table.

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The table containing zip code boundaries has two columns of interest: "name" and "geometry". The "name" column contains the zip code, while the "geometry" column contains KML for the corresponding boundary.

You can use these boundaries to represent the locations on a map, otherwise they will only be points. To merge the tables, you need to select the columns you want to merge on. In this case, the zip code columns in both tables need to be matched.

The matching process works by looking for exact string matches in the selected columns and then linking matching rows together. This is done by selecting the radio button next to "Zip" in the left column and the radio button next to "name" in the right column.

Once you have selected the columns to merge on, you need to enter a table name for your merged table. A suggested name is "Bay Area Population merged with KML".

Here's a step-by-step guide to merging tables:

After merging the tables, you can visualize the data on a map. You may initially see "data loading" messages, but wait a minute and zoom in or refresh to see the correct images.

Reviews are essential for evaluating the effectiveness of data management tools. Google Fusion Tables is one such tool that has been reviewed by the Digital Humanities Blog at the University of Alabama.

Google Fusion Tables is a powerful tool for managing and visualizing data.

Fusion Tables automatically detects various data types during import and generates a few appropriate visualizations, including row views, card views, and map visualizations.

These visualizations can include standard strings, numbers, images, and KML, making it easy to get a quick overview of your data.

The map visualization feature is particularly useful, especially when working with location data, and can be automatically created for tables with multiple types of location data.

Fusion Tables also integrates seamlessly with Google Maps, allowing you to visualize your data on a map and even style it with different colors and visual presentations.

Editing is a crucial step in data management, and it's great to know that some tools make it easy.

You can add rows to a table, which is a fundamental aspect of data editing. The UI supports this feature, making it accessible to users of all levels.

Editing data is also possible programmatically through the Fusion Tables API, giving developers more control over the editing process.

Fusion Tables automatically detected various data types in the data and generated a few appropriate visualizations during import.

The visualizations included a row view and a card view for all tables, and a map visualization was automatically created for those with many types of location data.

Data types supported within the table view included standard strings, numbers, images, and KML.

Fusion Tables also supported KML point, line, and polygon objects as a native datatype in the tables, visualized on top of Google Maps' basemap.

The integration of Fusion Tables with Google Maps through the FusionTablesLayer allowed for fast, server-side rendering of large and complex user data onto the Google Maps base map.

This made it possible to show thousands of user data points on a map, rather than the previous limit of around 200.

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Maps visualization is a powerful tool for understanding and communicating complex data. Fusion Tables automatically detected various data types in the data, and generated a few appropriate visualizations during import.

All tables saw a row view and a card view, and those with many types of location data saw a map visualization automatically created as well. This is a great way to get a quick overview of the data and identify patterns.

Data types supported within the table view included standard strings, numbers, images, and KML. This means you can store and visualize a wide range of data types in Fusion Tables.

Fusion Tables was tightly integrated with the Google Maps geocoding service, as well as the Google Maps API. This allowed for the creation of interactive maps with a Fusion Table Layer.

Types of location data automatically detected included latitude/longitude information in one or two columns, KML place descriptions, and some types of placenames and addresses. These were sent to the Google Maps Geocoding API to put them on the map.

Fusion Tables supported KML descriptions of geographic points, lines, and polygons as a datatype within the tables. This allowed for the creation of complex maps with multiple layers.

To create a map in Fusion Tables, you can select the column that contains the location data, such as an address, and Fusion Tables will geocode that address and place a marker on the map.

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Filtering is a crucial step in data editing and visualization. Simple filtering tools can automatically summarize values in data columns.

These summaries help you quickly understand the data and make informed decisions. By doing so, you can focus on the most important information.

With checkboxes, you can easily filter the visualized data to show only the relevant information. This saves time and reduces clutter.

Maps are a great way to visualize data, and Google Fusion Tables makes it easy to create interactive maps with just a few clicks.

Fusion Tables can automatically detect location data, including latitude/longitude information, KML place descriptions, and some types of placenames and addresses.

To geocode addresses, select "Map" under the "Visualize" tab, and the program will automatically begin geocoding based on the left-most field containing spatial information.

Geocoding addresses can be tricky, but it's easier if you separate items with spaces only, no commas, like "134 Chapel Drive Durham NC 27708".

Once you've geocoded your addresses, you can style the map to highlight different types of data, like crime categories in the Durham gun crimes dataset.

Fusion Tables also supports KML descriptions of geographic points, lines, and polygons as a datatype within the tables, making it easy to work with polygon data.

To merge tables and create a new table with boundary definitions, you'll need to find a suitable boundaries table and merge it with your data using a common field, like FIPS codes.

Sharing maps is easy with Fusion Tables, and you can share links, embeddable scripts, or even email maps to others.

Here are some key features to keep in mind when working with maps in Fusion Tables:

By using these features, you can create interactive maps that help you visualize and understand your data in a whole new way.

Fusion Tables supports simple queries, embeddable HTML snippets for visualizations, and a simple HTML templating language for customizing layouts.

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You can use these features to develop highly custom, expressive websites and tools with your data.

Fusion Tables can be used by many data owners with limited software development time or expertise.

Maps created in Fusion Tables can be exported to KML and viewed in Google Earth.

This makes Fusion Tables an important authoring tool for non-profits and NGOs working closely with Google Earth Outreach.

If you've created a Github Pages repository, you can easily embed your Fusion Tables work into a webpage of your choice.

Here's a step-by-step guide to publishing your Fusion Tables work to the web:

Google Fusion Tables had a significant impact on the way people worked with data, but its time was limited. It was announced that Fusion Tables would be retired on 3 December 2019.

Google provided an open-source archive tool to help users export their existing Fusion Tables maps to an open-sourced visualizer. This was a welcome move, but it was still a blow to the many users who had grown attached to the service.

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Fusion Tables was inspired by the challenges of managing scientific data collections in multi-organization collaborations.

The website launched as part of Google Labs in June 2009, announced by Alon Halevy and Rebecca Shapley.

A scientific paper in 2010 further described the service, providing more insight into its functionality and capabilities.

Deprecation can be a tough pill to swallow, especially for users who have grown attached to a particular tool or service. Google's decision to retire Fusion Tables in December 2018 was a prime example of this.

Fusion Tables was retired on 3 December 2019. An open-source archive tool was created to help users export their existing maps to an open-sourced visualizer. This was a kind gesture from Google, but it didn't change the fact that the service was no longer available.

The deprecation of Fusion Tables was met with disappointment from its loyal user base.

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