Inspired by a recent support desk question, this week’s SimplyMap report will demonstrate how a user would run a business search that consists of multiple names. In short, the user was attempting to view on a map and report where Walmart and Target stores were located within a city. Here’s how we did it.
First, select a location and map a variable – in this example, we’ll use the city of Philadelphia and map the variable Median Household Income:
In this week’s SimplyMap report we’ll take a look at one of our newer data packages offered, P$YCLE® by Nielsen.
P$YCLE® is a segmentation system that evaluates consumers using key demographic factors that have the greatest effect on their financial behaviors, such as income, age, presence of children, home ownership and Nielsen’ proprietary measure of Income Producing Assets (IPA). The result is a total of 58 P$YCLE® segments, within 12 P$YCLE Lifestage Groups, each with distinct usage patterns for financial and investment products and services.
Let’s take a look at the top segments from a few cities around the US. First, create a new Standard Report (under New Tabular Report > Standard Report). Next, open the variables panel and navigate to the P$YCLE® segments folder – from there, select Add All Variables from the action dropdown that appears. Close out the panel to generate your report for the US.
This week’s SimplyMap report will provide an overview of one of the more advanced functionalities in SimplyMap – applying multiple filter conditions. Filters can be applied to any map or report tabs. For example, a map showing Household income levels by ZIP Code could be filtered to only show ZIP Codes with income over a specific threshold. Another example would be to filter the same income map to only show ZIP Codes with a population over 8,000. Using multiple filter conditions is a great way to hone your focus on a narrower set of geographic locations that meet your multiple criteria. Let’s get started with an example.
Scenario: You are looking for the best location to open up a high end salon in California and want to develop an initial list to further analyze. Let’s assume there are three criteria that must be met before considering a location.
1. The location has to have more than 500,000 residents.
2. The location must have a median household income of at least 75,000.
3. At least 30% of households must report spending money at a salon.
The Setup: First, create a new Location Analysis Report and set your location to California. Next, navigate through some pertinent variables and add the following variables that will serve as our criteria.
In previous blog entries we highlighted the usefulness of viewing multiple years of data for the same variable. In short, analyzing the same variable across multiple years allows users to identify trends. For example, in one of the previous blogs we showcased the explosion of tablet ownership over recent years (quadrupling!).For this week’s blog, we’ll see if the data matches our own observations on the rapid decline of landline phones in households. Let’s get started!
First, open a Standard Report and navigate through this path: MRI Consumer Survey » Telephone to find the variable, % Households w/ a Telephone: Have a landline telephone, 2015.
Hover over the variable and select the Select Variable Year option from the Action Dropdown menu. You will see the years 2008-2015 on your screen. Select each year as shown below:
Inspired by some recent support desk questions, this week’s SimplyMap report will provide some useful tips and tricks to help make the most out of your maps. Let’s get started!
First, let’s take a look at the map that we will apply changes to. This is a map showing Median Household Income for the city of Chicago:
At this level of zoom and geography, your map will display by ZIP Codes. If you’d like to see a more granular map, select a smaller geography from the View Data By dropdown in your map legend. The map below displays the data by census tract:
In this week’s SimplyMap report we take a look into the SimmonsLOCAL dataset to determine which cities in America have the highest percentage of residents who consume organic food products. Let’s jump right in.
Before we rank the variable, % FOOD – GENERAL | ORGANIC FOODS | ORGANIC FOODS – HH USES? | YES, 2014 let’s do a quick search for “organic” to see how many related variables are in SimplyMap. Open the Variables tab, click on Search, and type in “organic”.
You will then get a list of all variables in SimplyMap that contain the keyword organic.
Drop by booth #857at PLA 2016 to meet the SimplyMap team and learn more about the features and functions behind SimplyMap. Have a Ghirardelli Chocolate while we present SimplyMap and offer tips & tricks for current users. See you in Denver!
In a previous blog entry, we highlighted the benefits of having multiple years of data available for the same variable. This allows users to identify trends over time. Keeping with the same theme, this week we’ll take a look at how the median rent has changed between 2000, 2010 and 2015.
Let’s take a quick look at the US and move on to some cities we suspect may show a marked increase.
First, create a Standard Report and navigate through the median rent variable at this path: Census Data » Housing » Rent.
Next, hover over the variable and use the Select Variable Year option to select the years 2000, 2010 and 2015. Picture below for reference.
Visit the SimplyMap team at the ALA Midwinter Meeting 2016 in Boston. We will be at booth #1913 offering product demonstrations, tips & tricks, and of course Ghirardelli Chocolate!
With the final week of the college football season in the books, and bowl game selections having just taken place, we wanted to search within SimplyMap to see if any data pertaining to college football bowl games is available. Here’s what we found.
To start, open up the search function within the Variables panel.
In a previous post, we covered how to use the variable search function to search for multiple terms. First, we searched for “football”.
Not surprising, the results yielded a large number of football related variables at 648.
