Challenge

Spatial Statistics

TWIST 2018 / University of Zurich

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TWISTSpatialStatisticsZurich

A personalized similarity map with local residents in canton of Zurich. https://craigwang.shinyapps.io/twist/

Data

For this project we combined two spacial data sets from the Canton of Zurich with raster tiles of resolution 100m x 100m.

The first data set contains population data

Available attributes are:

total population
population aged 0-6
population aged 7-15
population aged 16-19
population aged 20-24
population aged 25-44
population aged 45-64
population aged 65-79
population aged 80 or over
male population
female population
population with Swiss Citizenships
population with Foreign Citizenships

The second data set contains employment data

Available attributes are:

Attribute name	Description
anz_vzae = Vollzeitäquivalente	Total (fte)
anzvzaew = Vollzeitäquivalte Frauen	Total female (fte)
anz_besch = Anz. Beschäftigte	Nr. of people employed
anz_ast = Anz. Betriebe	Nr. of businesses
ht = High Tech	High Tech Industry (fte)
widl = wissensintensive Dienstl.	Knowledge intensive services (fte)
handel	Trade and Commerce (fte)
finanz	Finance (fte)
freiedl = Freiberufl. Dienstl.	Self-employed services (fte)
gewerbe	Industry (fte)
gesundheit	Socaial and Healthcare (fte)
bau	Construction (fte)
sonstdl	Other Services (fte)
inform	Information technology (fte)
unterricht	Education (fte)
verkehr	Traffic and Logistics (fte)
uebrige	Others (fte)

fte = full time equivalent

Idea

We want to help people who are moving to Zurich to find locations where people most similar to him/her live.

Second, the user might want to know what amenities there are in her/his future neighborhood. This is why, certain types of POI from Open Street Map can be displayed on the map.

Method

The R shiny UI

We created an R shiny interface where the user can input his profile. Currently the following attributes are available:

Age (enter number)
Gender (choose m or f)
Employment sector (chose from dropdown)

Further attributes that could be added in the future:

Percentage working
Number of children aged 0-6
Number of children aged 7-15
Number of children aged 16-19
self-employed

Model

We first calculated the average numbers for the population (for age bands, gender and citizenship) and the average number of employments (per sector) in each cell. Then, we compared each cell to this average. For example, you then get to know that in a particular cell there are 151% more people aged 25-44 than in the average cell. Then, we took the percentile for this to standardize values from 0 to 100 for all the features.

To calculate the similarity of a profile to a cell's population and employment data, we multiply the percentile with the applicable profile features. With this we can calculate a score, how similar a person is to a certain cell's population and employees.