See Twitter Account
Trending places aka OSM place trends, inspired by Google Trends, is an analysis of the views on the OSM tiles which could help us determine the trends in views of these places i.e. which places are gaining popularity due a hike in views and try to link them with the latest news around that region.
Places that have unusually higher views than their normal views, implying that places like tourist hot spots are not trending unless there is an increase in the number of views from the average.
Once the 'trending' places or the places with a significant relative hike in views are determined, they need to be ranked relative to each other as well.
Say for two places TouristyPlace_A, UnseenVillage_B the relative hike in views from average is 20%. However, 20% of the TouristyPlace_A is about 500 views, while that of an UnseenVillage_B is 20 people. Thus, the trend is TouristyPlace_A will be considered more significant and ranked higher than UnseenVillage_B.
The tile log views of the slippy map of OpenStreetMap (OSM) are logged and published daily with a lag of 2 days. The log never contains less than 10 hits for any given tile and it is accessed at least 10 times by 3 different IP Addresses The pseudo-code for this anonymization as provided by Matt Amos (OSM) is as follows:
The open source nature of OSM, as well as development with OSM leads to several crawling activities on OSM which are highlighted in the image below. The lack of availability of I.P Address to track these artifacts makes it difficult to filter them out and they often hamper the results. Certain artifacts like the curve trail over Russia on the upper right hand side is a periodic crawling phenomenon.
Fig. January, 2016 All places with views. Major Discrepancies highlighted
While filtering out top n trending places, often the n places that appear are quite close to each other. An example can be seen in the image below. This phenomenon is expected because users tend to view an several tiles close by while viewing a particular place. A small algorithm to detect these clusters and reserve only the highest viewed place amongst the top trending was added in the code and this issue was resolved.
Fig. January 31st 2016, Graph of total views vs. date of top 10 trending places. The similar structure of the graphs suggest that the places are a cluster and have been viewed together.
A T-score is calculated for the total views over n days (By default and the minimum required is 7 days). Hikes in places are detected based on the T-score which in broad terms is the number of standard deviations the views on a particular day are away from the mean. After analysis and testing of the OSM data, a cut off T-score of 3.5 was decided. However, on fetched top 'x' places with the highest T-score implying highest deviation from mean often resulted in displaying insignificant peaks as can be compared in Fig. A and Fig B. below.
Fig A. January 31st 2016, Graph of total views vs. date of lat, lon: -84.79, -140.58 in Antarctica. with the T-score pointed out. It can be seen that the place has almost 0 views usually, but a small spike of nearly 75 views gives it a very high Tscore of nearly 6. The question is, how important is the trend for this place versus another place with regular views?
Fig B. January 31st 2016, Graph of total views vs. date of lat, lon: -84.79, 1.3623.58 in Antarctica. with the T-score pointed out. The place has regular views and a T-score of 5.77 when the deviation from the average views is nearly 1000. Compared to the place in Fig A. Should this have a higher trend?
A scatter plot for absolute deviation in views and T-score suggested the threshold level for abs increase. Further, the ranking was decided in the following manner. An absolute increase in views of all places was calculated. The worldwide median of these absolute views was calculated and the trending places were ranked based on the relative increase in absolute views of the places over the world wide median. A median was chosen instead of mean because median get less effected by outliers or peaks. The logic behind this ranking is that the importance 'trending places' worldwide is a parameter of the world human population looking at OSM maps. Over time, as more and more people use OSM, the absolute increase in views of all places in OSM will increase. The places that have a greater increase than the usual (median in our case) will have a more significant trend.
Fort McMurray, Canada was a victim of wildfire in the beginning of May 2016. It appeared on the Trending places on 4th May, 2016.
Fig. shows the Twitte bot tweet on 4th May, 2016 with the attached graph for the Top 10 trending places and the status text. Due to a limit on the length of tweet the names of all Trending places are not tweeted and Ellipsis is appended after the word limit is reached. However, all names can be seen on the graph. Places with names in non english characters are replace with the lat and lon values and country code.
This can be done by (you can iterate over your date range by using a for loop, this example if for a particular date)