Tutorials and FAQs
- What is a Palladio project?
- Which data formats work with Palladio?
- What does it mean if I see a red icon and ‘Please review’ in one of my dimensions?
- What is a Multiple Values Delimiter?
- How should Dates be entered?
- How should Coordinates be entered?
- What do the labels and numbers listed directly below my dimension represent?
- How do I get the most use out of the TimeLine feature?
- How do I get the most use out of the TimeSpan feature?
- What are some applications of the Gallery View?
- How do I get the most out of Map view?
- What are Size Points?
- I would like to export a Map visualization but can only export it as an .svg with dots and lines. What should I do?
- How does the List view work?
A Palladio project begins first with the tabular data you have on your computer. Once you’ve uploaded this data into the Palladio interface, you can then refine it, visualize, and save it back to your computer as a Palladio project. The Palladio project will be saved with the extension .json and includes the schema and structure required to visualize your data in Palladio the next time you visit. (In other words, you cannot simply upload any .json file created elsewhere and expect it to work within the Palladio platform.) Now, as you continue to use Palladio, you can shorten the upload and refine stages by uploading your existing palladio.json file (the Palladio project). But please remember that if you make further changes to your data in the refine stage, you will have to save this new work as a new .json file in order to have a version of the project that reflects your more recent changes.
Any collection of information that can be represented in a table format (in other words, any data you might enter into a spreadsheet program) will work with Palladio. Make sure that all data is represented by delimited-separated values. Palladio supports the following delimiters: commas, semicolons and tabs.
Palladio works best with data that is uniform, consistent, and simple. Avoid notes and comments within your data, or any unnecessary diacritics.
Tip for those using .csv files: Breve, another tool from the Stanford Humanities + Design Lab, will help reveal the scale, types, and gaps in your data.
You can also build your own tables and bring them into Palladio in three ways:
By typing text into any word processing program (ie. textedit or MS Word) and then copying the contents into Palladio interface.
By dragging any file format into the Palladio interface, for instance by dragging a .txt or .csv file from your work station and dropping into Palladio.
By typing directly into the Palladio interface.
If you plan to build your own table in Palladio:
The first row in your data will be parsed as a header. This means that the first row in your data will indicate the name of the dimensions in the column below.
So, a table column of people’s names would have ‘Names’ as its first row and then dimensions, such as ‘John Doe’ and ‘Jane Smith’ in the rows below. Palladio cannot properly access and reference the different dimensions in your data unless you follow this format.
Each column header must be unique from the other. Within a header name you may use letters, spaces, and numbers. Do not use any special alphanumeric characters, such as underscores ‘_’ or dashes ‘-‘, as these will confuse Palladio.
Examples of data:
First name, Last name, Age John, Doe, 28 Jane, Smith, 35
Incorrect (missing header):
John, Doe, 28 Jane, Smith, 35
Incorrect (two headers with the same name):
Name, Name, Age John, Doe, 28 Jane, Smith, 35
Incorrect (special characters in the header):
First_Name, Last-Name, Age John, Doe, 28 Jane, Smith, 35
Other Data Issues:
Note that empty rows in your data will be ignored by Palladio.
Palladio does not work with unstructured data, such as one long string of text, but only with data that has been placed into some kind of table. The richest uses of Palladio comes from having several different related tables of information, however many users will start with just one single table.
This is Palladio’s way of telling you that there could be errors in your data. Palladio notices if there are non-standard alpha-numerical characters in your data. These special characters may either lead to errors, or (in the case of commas or semicolons used to separate values in a given dimension) these special characters may be of help for you to use advanced tools such as the Multiple-Values Delimiter. Palladio could also be telling you that not all of your data seems to be of the same type.
Your tabular data is made up of various dimensions (ie. individual cells in a spreadsheet). In some cases you may want to assign more than one value for a certain dimension. For instance, you may have a column describing someone’s ‘Position.’ The column could be filled Princes, Popes, and Poets. But what if your Prince was also a Pope? In this case you could enter ‘Prince, Pope’ in one single cell of your table. Now, after you’ve uploaded the data into Palladio, you can parse that cell into two unique values. Here’s how: In the Multiple Values Delimiter input box, enter the symbol you’ve used to separate your values, in this case, a comma that separated Pope and Prince. Now Palladio understands that this entry as two separate values.
Dates should be entered in the following format Year-Month-Day (2014-01-01). Years must always be rendered as four digit integers. Thus years between 0 CE and 1000 CE should still be rendered as 0001 or 0999. Negative dates should follow the same format, but preceded by a minus sign (ie. -200-01-01). If using Excel, be sure to specify that the column reads as “Text” rather than dates, so that Excel doesn’t change the dates into its own (non-Palladio friendly) format.
Coordinates information must be rendered as latitude and longitude, separated by a comma. For example: 41.95, 12.5. This Geocoding tool may be of help for those who have a list of place names but no associated coordinates: http://sandbox.idre.ucla.edu/geocoder/
See the Scenario: Creating Data for a Simple Map for more information about creating and entering coordinates information. Download PDF
In the refine (non-visualization) view your data appears as table, with each column header represented as a unique dimension.
For instance, Gender as the Dimension Name, with the label Text and number 2 listed below.
In this case Gender is the header from one column of your data, Text is the data type recognized by Palladio (most data will be recognized as Text), and the number 2 represents the number of unique values in your data - in this case the data contains 2 values: Male and Female.
Palladio will guess the type of data entered based on its attributes. Data will be recognized as either: Text, Number, Year or Date, Coordinates, URL.
If Palladio ‘guesses’ your data incorrectly you can click on the dimension and in the dropdown menu labeled Data Type, change the type of data specified. The more accurately Palladio defines your data, the better the platform will work.
As you change the temporal period in your TimeLine filter view, the map will update to show data only from that period. You can zoom in to get as granular a view as you would like. One of the most exciting features of Palladio’s timeline is that it allows users to select more than one point on the timeline to show more than one period (ie. 1900-150) and 1970-200) view these simultaneously within a given visualization.
The Group By option allows you to see the data in stacked bars, showing you how a certain group of data appears across the timeline as you hover.
Use the TimeSpan feature to visualize any data for which you have both a starting date and an end date, such as a lifetime, or a season. Palladio will recognize any dimensions entered in the correct format (ie. 2014-06-27) and will allow you to select any such dimension as either the start date or end date within the timeline view. Choose from a parallel or bar view.
In the Gallery View you can display different kinds of data that cannot be visualized as maps or graphs.
For instance, if in your data set you had a column in which you linked a list of people to URLs containing images of each person, you could then display these images in grid view.
In this case, you would go to Grid View and click the ‘Image URL’ option. Palladio would then locate the dimension in which you’ve listed image URLs in your data.
The default map view is a blank map. Note the settings panel to the right. You can hide the panel by clicking on the gear symbol. Zoom in and out using the + and - signs in the top left, and click and drag or use the arrow keys to move the map around.
The first step is to specify the type of map you’d like to see. To do so, choose from the menus within the settings panel. You can choose from two types of map views: ‘Points’ or ‘Point to Point’. (Note that access to these functions will depend on the kinds of information you’ve uploaded. You wouldn’t, for example, be able to use the ‘Point to Point’ function unless Palladio recognizes a dataset with more than one type of coordinates column, ie. ‘Source City’ and ‘Destination City.’)
####‘Points’ map view
In the ‘Points’ map view, Palladio can map out a collection of unique locations, based on any coordinates data (longitude, lattitude) you’ve entered. For instance, if you uploaded a table listing all of the Popes in the last five hundred years, their individual cities of birth, and coordinates data for each of these cities, you might want to see a worldwide view of where Popes have been born in the last five hundred years.
In Points view you can only select one dimension. If you toggle Size points, the sizes of each dot will relates to their frequency within your data. You can specify how you would like to count your data by using the Count By function. Note you can only count by unique tables uploaded in your data. Hovering over specific dots will display corresponding data. You can select which type of data to display on hovering, by using the Tooltip label.
Below the ‘Type of Map’ menu is a second menu, ‘Places.’ This menu will be populated with any data for which there are coordinates, and the menu choices will be named for the headers of columns containing coordinates data. For instance, in our Popes dataset set, if the coordinates data in your original table is labeled ‘Birth City Coordinates,’ then you will be prompted to choose ‘Birth City Coordinates’ within the ‘Places’ menu. To see all of your Popes birthplace data on the map, you would select ‘Birth Place Coordinates’ in the ‘Places’ menu. Now the map will be populated with dots representing each individual value in your coordinates data.
See the Scenario: Creating Data for a Simple Map for more information about creating and entering coordinates information.
####‘Point-to-Point’ map view
The point to point view give us a node-link map. Here you will select two different dimensions (ie. source city and destination city) and see the links between various points.
The arc of the link between two points represents the direction of the flow. Think of direction in a clockwise function.
Now you can explore this Map data in various ways by using the Timeline and Facet Filter functions. You can also use the search bar to filter the data being visualized to accord with whatever parameters you wish, so long as your coordinates data corresponds in some way to whatever you’ve typed into the search bar. You can also isolate specific points on your map by clicking on them.
See the Scenario: Creating Data for a Point to Point Map for more information about creating and entering coordinates information.
Toggling the Size Points function in the map view will change the sizes of your various dots on the map, depending on how frequently they appear in your dataset. So, for instance, if you had a table listing 30 people born in Rome and 1 in Florence, and you have produced a map visualization that counts by people, the dot representing Rome in your map view will be significantly larger than the dot representing Florence. You can adjust the scale of the Size Points (in other words, the size of the dots as they appear in relation to the map) by toggling the Adjust Scale button.
I would like to export a Map visualization but can only export it as an .svg with dots and lines. What should I do?
As Palladio Map Visualizations involve placing static coordinates data onto a separate map grid layer the full visualization cannot currently be exported as an .svg, rather you will see only the dots and lines against a blank background. If this solution is unsatisfactory, we recommended using ‘screen grabs’ as the best method, currently, for exporting map visualizations.
Use this view to filter and arrange data to create customized lists. First select the Row Dimension by which your list will be arranged. Then select the specific Dimensions you would like to see. Palladio will then return a list including those Dimensions, arranged according to the Row Dimension you’ve selected. Data can then be filtered using the usual suite of filters provided in other visualizations. Lists can be exported as csv files.
Understanding how lists are created: Let’s say you have a list of unique names, and the places where each person was born. If you created a list with the dimensions “Names” and “Places of Birth,” and arranged this list by the Row Dimension “Names,” then you would see a list where each row contains a single name and the place of birth of the person listed. But if you created that same list, but now arranged it by the Row Dimension “Places of Birth” you would see a list where each row contains a single Place of Birth and every person born in that place. So, if more than one person in your data was born in Paris, you would not have a simple 1 to 1 ratio across the dimensions; instead your Paris row might contain several names, while your New York row might contain only one name. The point is not to be misled into thinking that the List view will automatically return something akin to a simple table, where every row lines up across the various columns.
Please email us at firstname.lastname@example.org to share your experience.