The app helps the user find out where and when were traffic tickets issued in Slovenia, thus facilitating safer driving.
Ticket database is limited to territory of Republic of Slovenia.
Choose between these issued citations to show in app:
– driving while using a cellphone
– ignoring safety belt laws
– unpaid toll
and traffic accidents.
The app will locate you, fetch data about traffic citations issued in your vicinity, and show them on map. To see citations, that were issued somewhere else, click on map. Additionally available is summary of threat level, derived from statistical data, collected by government agencies. These are:
– overall threat assessment to be issued a citation, should you violate traffic laws
– average interval between citations issued at that location,
– date of most recent citation issued relative to data source update
– number of citations issued in vicinity
– distance to closest issued citation
And several statistical distributions of issued citations, such as:
– by days in week (how many on Monday, Tuesday, …
– by hours in day (how many in intervals between 9-12h, …)
– by months in year (how many on January, February, …)
– by weather conditions (how many in rain, snow, clear weather)
– by temperature (how many in temperature interval between 5-10 Celsius, …)
Same information si also available on address list, ordered by number of citations issued.
Many thanks to traffic wardens, police, and other officials, who supplied the raw data, used to build this app:
– traffic wardens of Ljubljana,
– state police,
– Parkings of Ljubljana,
– traffic wardens of Maribor,
– traffic wardens of Kranj,
– traffic wardens of Celje,
– traffic wardens of Novo Mesto,
– traffic wardens of Nova Gorica
Data was acquired for time interval between 2012 and end of 2014.
The database contains is a little less than a million traffic citations.
The Global Gender Gap Index examines the gap between men and women in four fundamental categories (subindexes): Economic Participation and Opportunity, Educational Attainment, Health and Survival and Political Empowerment. Table 1 displays all four of these subindexes and the 14 different indicators that compose them, along with the sources of data used for each.
I thought it would be nice to try to visualize the data and make it as interactive as I could, and learn d3.js in process. I actually tried to mobilize all the data in the report, which one can see in graphical form by clicking on countries on world map, or selecting the categories in the dropdown.
There are several categories:
In addition to that, I calculated the differences between 2013 and previous years. These maps are also accessible through dropdown menu, or simply by scrolling up and down.
This subindex is captured through three concepts: the participation gap, the remuneration gap and the advancement gap. The participation gap is captured using the difference in labour force participation rates. The remuneration gap is captured through a hard data indicator (ratio of estimated female-to-male earned income) and a qualitative variable calculated through the World Economic Forum’s Executive Opinion Survey (wage equality for similar work). Finally, the gap between the advancement of women and men is captured through two hard data statistics (the ratio of women to men among legislators, senior officials and managers, and the ratio of women to men among technical and professional workers).
In this subindex, the gap between women’s and men’s current access to education is captured through ratios of women to men in primary-, secondary- and tertiary-level education. A longer-term view of the country’s ability to educate women and men in equal numbers is captured through the ratio of the female literacy rate to the male literacy rate.
Health and Survival
This subindex provides an overview of the differences between women’s and men’s health. To do this, we use two indicators. The first is the sex ratio at birth, which aims specifically to capture the phenomenon of “missing women” prevalent in many countries with a strong son preference. Second, we use the gap between women’s and men’s healthy life expectancy, calculated by the World Health Organization. This measure provides an estimate of the number of years that women and men can expect to live in good health by taking into account the years lost to violence, disease, malnutrition or other relevant factors.
This subindex measures the gap between men and women at the highest level of political decision-making, through the ratio of women to men in minister-level positions and the ratio of women to men in parliamentary positions. In addition, we include the ratio of women to men in terms of years in executive office (prime minister or president) for the last 50 years. A clear drawback in this category is the absence of any indicators capturing differences between the participation of women and men at local levels of government. Should such data become available at a global level in future years, they will be considered for inclusion in the Global Gender Gap Index.
Out of the 110 countries that have been involved every year since 2006, 95 (86%) have improved their performance over the last four years, while 15 (14%) have shown widening gaps. Ten countries have closed the gap on both the Health and Survival and Educational Attainment subindexes. No country has closed the economic participation gap or the political empowerment gap. On the Economic Participation and Opportunity subindex, the highest-ranking country (Norway) has closed over 84% of its gender gap, while the lowest ranking country (Syria) has closed only 25% of its economic gender gap. There is similar variation in the Political Empowerment subindex. The highest-ranking country (Iceland) has closed almost 75% of its gender gap whereas the two lowest-ranking countries (Brunei Darussalam and Qatar) have closed none of the political empowerment gap according to this measure.
This topic is apparently interesting enough that it warrants its own discussion on Quora. People there are relying on keen observational powers of human mind, but for this article, I tried to group the flags algorithmically.
I plotted the results on the map below. Countries with same colors have similar flags. The brighter the color, the bigger the group of countries with similar flags.
Here are some flag groups. To see them all, click the image above.
How I grouped the flags
I used a machine learning algorithm called k-means clustering. It’s really a rudimentary exercise, but the results are good enough to publish on this wee blog.
The algorithm accepts units to be grouped as vectors, so I had to vectorize the images first, that is to say, convert them in a long string of numbers. Each image was partitioned into a grid, then the average color value for each cell was computed. The grid was 24 x 24 cells big. I found that enough for simple flags. These color values were converted into HSB color space and experimentally weighted, then copied into a vector. These vectors were fed into the k-means algorithm with requested number of individual clusters set to 120 (there are 240 different flags). You can see results in the viewer.
Number of clusters was set experimentally, and the clustering is not perfect. For example, Canadian is grouped with some very unlikely lookalikes.
A few months ago, while researching business times of various categories of establishments in Slovenia, I thought it would be nice to somehow visualize a map with a graphical representation of density of open establishments. I decided on heatmap style, although I later discover that my chosen implementation had some drawbacks.
Getting the data
Data with business hours of commercial establishments is traditionally not open for many reasons, two of them being that (1) this information can be commercially exploited, and (2) the opening hours can be subject to frequent changes, which can tax the database owner with considerable effort should the database stay current and reliable.
First I toyed with the idea of crawling entire directory of odpiralnicasi.com, then I actually thought about making a version for London, Amsterdam or San Francisco with Yelp data, for which I would have to crawl an entire Yelp city directory, a task I’m not sure it would succeed. Yelp would probably block my IP before I could harvest a significant portion of what interested me.
So I decided I would use the Najdi.si maps business directory. Disclosure: I work there, so I have access to the database with various business data, which is being kept current.
For every company, I took out only the name, geo coordinates, business hours and business category, then I constructed the animated maps. Before I delve into that, a short video of economic activity in Slovenia in course of a typical Monday.
The animated chart you see on the bottom shows the number of active establishments in various economic categories, such as Restaurants and catering, Industry, Shopping, etc. The full list is:
blue: Computers and IT,
red: Restaurants and catering,
green: Home and garden,
yellow: Beauty and health,
pink: General business,
orange: Free time,
magenta: Culture and schooling
Rendering the maps and constructing the visualization
Rendering one frame in one city at a specific time is just a matter of setting appropriate latitude, longitude and zoom level on the map, selecting the desired time and plotting on the map all establishments that are open at that time. I used Processing to do that, and for the heat map part I used this excellent example by Philipp Seifried. As a finishing touch, I made maps to switch between day and night styles at appropriate times.
To do entire video, I had to write a parallel rendering queue lest the rendering of a single video took an eternity – Eclipse project available by email request.
To complicate things a bit I decided to include up to four different places on the same map, so the viewer could compare opening hours in Ljubljana in different economic categories, or see how different cities woke up and went to sleep at different times.
A typical frame looks like this:
Here’s an example for different economic activities in Ljubljana:
I mostly did this to be able to visually compare levels of business activity in Ljubljana. First of all, the heatmap technique I employed here turned out to be somewhat unreliable for video purposes, because it colors the dots relative to the highest concentration. But concentration and absolute numbers of active businesses change from frame to frame, so it seems that at night there’s more activity that during the day.
Even so it’s still clear that restaurants, bars and clubs are still pretty much open when other activity starts to die down.
This is Ljubljana at noon, again:
top left: General business
top right: Restaurants and catering
bottom left: Industry,
bottom right:Beauty and health
The big spot in the northeast is the mall region, where untold number of business operate in ten or more big malls. Business concentration there dwarfs everything else in the city, except maybe in industrial category.
Below is Ljubljana at eight o’clock in the evening. Pretty much everything has closed down except for eating and drinking, and maybe the cinema theater in the mall.
Below: Ljubljana at ten o’clock in the evening. Some businesses don’t close down at all. I double checked the primary data source and it’s true. There are cleaning services that stay open during the night, etc.
I’m relatively satisfied with results except for the heatmap issue. I may correct that if I get the data for a bigger city.
There has recently been a flurry of activity by self-made mappers on the net that major media have noticed. It seems that proliferation of tools such as the excellent TileMill does help to make custom maps a relatively painless, yet still laborious process.
In my experience, a major hurdle in this process is getting good data. Governments and corporations around the globe have made acquiring the goods easier, but the quality frequently leaves one wanting. More about this particular dataset later.
This map is my attempt to visualize real estate prices in Slovenia. Buildings are colored according to the most expensive unit they contain, except in some cases where data is bad. More below.
This dataset is provided by GURS, a government institution. I used it before, to make the map of structure ages in Ljubljana. It comes in a variety of formats, such as SHP (geometry) and text (building properties) files, which were clearly dumped from database tables.
It has some severe problems. For example, some bigger and more expensive buildings contain many units, but these units all hold the same value regardless of their useful area. To make matters more complicated, other multiunit buildings don’t hold the same value for the units they contain. They are, in other words, evidenced correctly. Then, there are building compounds, like the nuclear power plant in Krško, in which every building clearly holds the exorbitant value of entire compound. Some other buildings have price value as zero, and so on.
All of this doesn’t even start to address the quality of valuation the government inspectors performed. In the opinion of many property owners, the values are too low. There’s a new round of valuation coming, in which the values are reportedly bound to drop by further five to twenty percent, if I remember correctly. It will be interesting to make another map with the valuation differences some day.
Massaging the data
This means that the above map is my interpretation of the dataset beyond the visualization itself. In calculating values for visualization, there were several decisions I made:
For multiunit buildings, I calculated the cost of square meter for every unit, then colored the building with color value of the most expensive unit. This was necessary, because some buildings contain many communal areas, garages and parking lots, which are all independently valued. I first tried with a simple average value, but the apartment buildings with many parking boxes and garages were then valued deceivingly low. I tried to make the map more apartment-oriented, so this was a necessary decision to make it more accurately reflect the market.
For incorrectly evidenced buildings with same value (high) unit value, I took the price of one unit, divided by sum of unit areas. I could do this on one unit only, but which one? There’s no easy answer. The average seemed the way to go.
I also made a list of the most expensive buildings by their total Euro value. Individual unit values were summed, except in cases described in the second bullet point above. there I simply took the price of one unit. It’s accessible as a separate vector layer under “Most expensive buildings” menu item.
Turns out the most expensive buildings are mostly power plants, which is not surprising. In Ljubljana, two of the most expensive buildings were completed recently. Well, the Stožice stadium was not really completed. I don’t know whether it was paid for or not – this is a discourse best suited for political tabloids. See the gallery:
It’s also hardly surprising that the capital and the coast are areas with the most expensive real estate available. The state of city of Maribor is sad to see, though, at least in comparison to Ljubljana.
I suggest taking the tour in the map itself, where I go into a little more depth for some towns and cities. Also, be sure to click the “Most expensive buildings”, then hovering the mouse pointer over highlighted buildings to get an idea of their total cost and price per square meter, which in many cases diverges dramatically.
Here are two charts showing price/m2 distribution at different intervals in time.
This one is an all-time chart. Most buildings are valued low, since all ages were taken into account.
This one shows the period between year 2008 and now, in other words, since the crisis struck. Nevertheless, more expensive buildings seem to prevail. No wonder, since they are new. But that probably also means that there’s more apartment building construction relative to countryside development. I’m not really a real estate expert, so if anyone has a suggestion, comment away.
I also have to thank the kind people at GURS for providing me with data. They know it’s flawed somewhat, but all in all it’s not so bad.
As I’ve noted before, this map is a result of my interpretation of government data. I’m in no way I responsible for any misunderstandings arising from this map. If you want to see the actual valuation of your building or building unit, please consult GURS or use their web application to find out.