This application visualizes mean income of households in United States, for each zip code (ZCTA, zip code tabulation area): Click here to open the application
Background: Urban Decay
The visualization shows clearly, how large cities in US are surrounded by wealthy zip code areas (red rings). Inner cities, however are areas with less income (green or yellow), with few exceptions, such as New York, Washinton DC and San Francisco.
“In the United States during the 1940s, for the first time a powerful interaction between segregation laws and race differences in terms of socioeconomic status enabled white families to abandon inner cities in favor of suburban living. The result was severe urban decay that, by the 1960s, resulted in crumbling “ghettos“. Prior to national data available in the 1950 US census, a migration pattern of disproportionate numbers of whites moving from cities to suburban communities was easily dismissed as merely anecdotal. Because American urban populations were still substantially growing, a relative decrease in one racial or ethnic component eluded scientific proof to the satisfaction of policy makers. In essence, data on urban population change had not been separated into what are now familiarly identified its ‘components.’ The first data set potentially capable of proving ‘white flight’ was the 1950 census.” Source: Wikipedia
The visualization is based on American Community Survey (ACS) from census.gov
Red = high income
Yellow = medium income
Green = low income
I’m looking for bloggers, who want to co-author articles about interesting/surprising facts about US (or European) population demographics, economy etc. along with visualizations. Also, if you have ideas for further visualizations, please contact me via firstname.lastname@example.org.
If you want to embed this app to your web page, read the instructions here.
This is an app, through which you can visualize demographic data from Finland. Try also clicking for example “Relative” and “Higher level university degree”, or “Employed”.
Click here to open the application
The data used in this visualization: Paavo – Open data by postal code area, Statistics Finland. The material was downloaded from Statistics Finland’s interface service with the licence CC BY 4.0.
Check this article how to embed a WhereOS app to your own website. If you have ideas what kind of data you’d like to see in the apps, write me an email email@example.com.
Embedding a WhereOS apps to any web page or a blog is easy. Just include the an HTML iframe-tag with width and height properties, and point the link to the application you have created.
[domain] is the name of the service domain you have used when starting the WhereOS service for yourself
[application_name] is the name of the application
[ui_view_name] is the name of the UI view, if more than one UI view exists for the given application
Remeber to replace spaces in the application name with underscore in the URL. Application with name “Reachability Analysis” becomes Reachability_Analysis and so forth.
<iframe frameborder="0" style="width: 100%; height: 400px;" src="https://apps.whereos.com/a/Reachability_Analysis"></iframe>
WhereOS is a new cloud based operating system for distributed, data driven applications, that’s built on top of Apache Spark. WhereOS makes it easier to build, host, distribute and share applications built using (big) data.
WhereOS can be programmed through SQL programming language including built-in functions for ETL (extract, transform, load) operations, statistical analysis, geographical and spatial analysis, data visualization etc. Programs written in WhereOS applications are always executed as distributed processes, without developers explicitly design their applications for parallel processing and scalability.
WhereOS uses Spark and Hive as the execution engine for the applications. WhereOS can be extended through two types of drivers: function and data asset drivers. Function drivers can introduce any new Hive/SparkSQL functions, to implement new features for the SQL language. Data asset drivers provide new ways of integrating data formats and/or data transmission protocols into the system.
If you want to discuss more, contact us through firstname.lastname@example.org