User-uploaded geographic data must include a geographic component that allows the system to plot it on a map. Geographic components include the following:
- Latitude and longitude coordinates
- ZIP Code
- Street address
The way the data is interpreted can restrict how the dataset can be used in target series. When you upload a dataset that contains ZIP Codes™, Rhiza uses other cues in that dataset to decide how to interpret the ZIP Code information.
Briefly, if more than one geographic element is included, every geographic component is interpreted as part of an address and can be mapped as a piont. This is because a common use for uploaded data is to provide a customized list of addresses. It can also be mapped as a shape. For more information on points and shapes, see "Points on a Map" and "Regions (Shapes)" later in this topic.
|ZIP Code only in .csv file||ZIP Code plus other address information in .csv file|
|How will it show up on my map?|| |
As a set of ZIP-Code shapes (group to ZIP Code on the Map-Based tab)
Configure your map's Grouping Data Value to show whatever data you'd like to see for those ZIP Codes.
It can be shown as:
If you show the ZIP Codes as shapes, configure your map's Grouping Data Value to show whatever data you'd like to see for those ZIP Codes.
|What happens if my ZIP Code or address isn't found?|| |
If your ZIP Code is malformed or outside your market, the upload doesn't work as expected or you may get unexpected results on the map.
Check your .csv file to make sure your ZIP Codes are valid and contain only 5 digits.
Rhiza tries to plot the address in the center of the ZIP Code. If that fails, it's plotted at latitude 0, longitude 0 (off the coast of Africa).
This can happen because you've entered an address incorrectly, or because the address does not exist in the geocoder yet. Verify that the address is correct; if it is, open a support request with Nielsen.
To help clarify the difference between addresses and shapes, let's look at two example datasets and compare how Rhiza treats them during an import operation.
ZIP Code, Sales, Number of Stores, Number of Employees 15218, 12000, 1, 8 15219, 24000, 4, 10
City, State, ZIP Code, Sales, Number of Stores, Number of Employees Swissvale, PA, 15218, 12000, 1, 8 Pittsburgh, PA, 15219, 24000, 4, 10
Dataset A is considered a list of ZIP Code shapes. You can create a target series that groups to ZIP Codes on the Map-Based tab; when you put it on a map visualization, you can see each ZIP Code and choose which data you want to show for the ZIP Code.
Dataset B, on the other hand, is more flexible:
- It is considered a list of addresses because it includes city and state information in addition to the ZIP Codes. If you map these ZIP Codes, they are represented as points on a map. To map them, your target series must group to Records on the Dataset-based tab.
- It is also considered a list of ZIP Code shapes. You can also create a target series that groups to ZIP Codes on the Map-Based tab; when you put it on a map visualization, you can see each ZIP Code and choose which data you want to show for the ZIP Code.
By default, the system interprets geographic elements as a specific point on a map (an address); however, there are cases when it can be interpreted as a region, or shape (a ZIP Code or DMA, for example).
It is important to understand the difference between these types when uploading data, because only shapes can be used as geographies (that is, they can be selected in the Dataset-based tab when setting your target filters or grouping).
Points on a Map
Point data describe individual locations. They can be represented by single points on the map. Street addresses and longitude-latitude pairs are examples of point data.
Point locations can be used to aggregate data only if you use the Radius Around Points attribute in your target filter to create a trade radius; this creates a geographic shape in addition to creating the point on the map. This option allows you to specify the distance and create a circular area surrounding the addresses.
Region data are defined shapes that can contain other data points. Examples include shapes like ZIP Codes, DMAs, and Nielsen Radio Metro Areas.
Shape-based geographies can be used to aggregate data across the region. For example, you could use a dataset with ZIP-Code-based data to learn the number of women over age 55 in each ZIP Code.
Rhiza can filter and aggregate data by temporal attributes like day, week, and month. The units of time available depend on how the data is represented in the dataset. For example, if the dataset represents values by week, you cannot filter by day because the dataset does not support that level of detail. You also cannot specify dates or times not included in your dataset (for example, if your earliest date is March 20, you cannot set a search filter starting with March 1).
To get the most out of your temporal data attributes, use these three tips.
Tip 1: Set the correct data type
has two options for time and date data. Make sure you choose the correct type:
- Date: Use this for any temporal values of a day or larger; its value is a whole number. Time values included with this type are ignored.
- Date Time: Use this for any temporal value that includes a value smaller than a day, including data that has both a day and a time in the same field.
If your data contains mixed types (for example, some values include times, while others don't), choosing Date Time will preserve the time values that do exist and add a default time of midnight for the rest.
Tip 2: Format your data correctly
Make sure that your dates are formatted as YYYY-MM-DD in the upload file.
Some spreadsheets, including Microsoft Excel, automatically reformat date values from YYYY-MM-DD to MM/DD/YY. Make sure that your source file does not have an altered value. In Microsoft Excel, you can prevent this behavior by selecting the column containing your dates and setting the cell format to Text.
Tip 3: Understand how Rhiza interprets the formatting
| Input value | Date as interpreted by Rhiza | Notes | | 2014-05-04 13:24 | May 4, 2014 at 1:24 PM | None | | 2014-05-04 13:24:55 | May 4, 2014 at 1:24:55 PM | Rhiza doesn't currently support filtering by less than one minute | | 2015-08-31 8:00 PM | August 31, 2015 at 8:00 PM | None | | 2015-08-31 8:00 | August 31, 2015 at 8:00 AM | If no AM or PM value is included, the value is interpreted as 24-hour time. | | 2009-12 | December 1, 2009 | Omitted values are assumed to be 0, or in the case of a month, the first day. | | 2017 | January 1, 2017 | In this case, the omitted value for the month is assumed to be the first month, and the omitted value for the month is assumed to be the first day. |