diff --git a/TAA3/tutorial.html b/TAA3/tutorial.html index a03f0e5..e94e718 100644 --- a/TAA3/tutorial.html +++ b/TAA3/tutorial.html @@ -522,8 +522,8 @@
SUBMIT
, you will see a generated api_key and an api_secret. Fill those in below.
<ipython-input-5-cce74c12e76b>:7: FutureWarning: Calling int on a single element Series is deprecated and will raise a TypeError in the future. Use int(ser.iloc[0]) instead
- size = int(area_to_check.area/1e6)
-
Text(0.5, 1.0, 'Ameland, The Netherlands. Total area: 160 km2')
-
6900
-
- | id | -owner | -datetaken | -tags | -latitude | -longitude | -
---|---|---|---|---|---|---|
0 | -54030466890 | -63991153@N00 | -2024-08-29 14:30:05 | -lighthouse fjoertoer vuurtoren ameland amelân ... | -53.449381 | -5.623358 | -
1 | -54026176572 | -10485938@N00 | -2024-09-25 15:23:19 | -- | 53.460082 | -5.777918 | -
2 | -54025875198 | -63991153@N00 | -2024-08-29 13:56:03 | -beach strân strand plage people summer four do... | -53.445075 | -5.613788 | -
3 | -54025748630 | -11288822@N04 | -2024-09-11 10:04:53 | -- | 53.450711 | -5.631969 | -
4 | -54025305871 | -11288822@N04 | -2024-09-11 08:41:10 | -- | 53.449844 | -5.634163 | -
5 | -54025539353 | -11288822@N04 | -2024-09-11 10:04:24 | -- | 53.450688 | -5.631980 | -
6 | -54025636624 | -11288822@N04 | -2024-09-11 10:48:54 | -- | 53.459769 | -5.685588 | -
7 | -54024295987 | -11288822@N04 | -2024-09-11 10:04:24 | -- | 53.450688 | -5.631980 | -
8 | -54025525869 | -11288822@N04 | -2024-09-11 10:04:53 | -- | 53.450711 | -5.631969 | -
9 | -54025193331 | -11288822@N04 | -2024-09-11 08:41:10 | -- | 53.449844 | -5.634163 | -
10 | -54025525859 | -11288822@N04 | -2024-09-11 10:48:54 | -- | 53.459769 | -5.685588 | -
11 | -54023416616 | -63991153@N00 | -2024-08-29 13:51:38 | -backlight tsjinljocht gegenlicht tegenlicht be... | -53.445075 | -5.613788 | -
12 | -54020292802 | -63991153@N00 | -2024-08-29 13:24:45 | -poles peallen palen pfähle pfahl peal pole str... | -53.445196 | -5.614764 | -
13 | -54018514474 | -11288822@N04 | -2024-09-11 08:41:10 | -- | 53.449844 | -5.634163 | -
14 | -54018594770 | -11288822@N04 | -2024-09-11 10:49:26 | -- | 53.459780 | -5.685597 | -
15 | -54018594740 | -11288822@N04 | -2024-09-11 10:50:52 | -- | 53.459786 | -5.685513 | -
16 | -54017257037 | -11288822@N04 | -2024-09-13 10:44:17 | -- | 53.435880 | -5.625911 | -
17 | -54018491864 | -11288822@N04 | -2024-09-13 10:47:02 | -- | 53.435358 | -5.625316 | -
18 | -54018160326 | -11288822@N04 | -2024-09-13 10:45:53 | -- | 53.435611 | -5.625569 | -
19 | -54017256987 | -11288822@N04 | -2024-09-13 10:55:06 | -- | 53.431999 | -5.629647 | -
And let’s see if everything is stored in a format in which we can work with:
- | 0 | -
---|---|
id | -object | -
owner | -object | -
datetaken | -object | -
tags | -object | -
latitude | -object | -
longitude | -object | -
We would expect floating values for the latitude and longitude, and a datetime object for the datetaken. Let’s have a look how these are stored:
'5.623358'
-
Aha! Strings. It will be difficult to convert strings into proper geometries, so let’s convert these columns to floating values, and convert them to points using pygeos.points()
. As you will see, we use a list comprehension to do so. The input for pygeos.points()
is a list or numpy array, in which each element contains a longitude and a latitude. To make sure we have that, we create this list
using the zip function in Python.
Question 1: As all the data is stored in strings, its pretty difficult to do something with the information straight away. Please provide the lines of code that you have written to convert the geo-information (lattitude and longitude) into actual coordinates and also provide the lines of code that you have written to convert the dates to a datetime object. Explain the lines of code.
@@ -1144,123 +651,7 @@apply
functions, and use the fromisoformat()
function from within the datetime package.
-df['datetaken'] = df.datetaken.XXXX(XXXX x : datetime.XXXX(x))
-df['year'] = df.datetaken.dt.year
-df['month'] = df.datetaken.dt.strftime('%b')
-df['month'] = df.datetaken.dt.month # Numeric
-df['month_year'] = df['datetaken'].dt.to_period('M')
-
---------------------------------------------------------------------------
-NameError Traceback (most recent call last)
-<ipython-input-1-44112cf87c20> in <cell line: 1>()
-----> 1 df['datetaken'] = df.datetaken.apply(lambda x : datetime.fromisoformat(x))
- 2 df['year'] = df.datetaken.dt.year
- 3 df['month'] = df.datetaken.dt.strftime('%b')
- 4 df['month'] = df.datetaken.dt.month # Numeric
- 5 df['month_year'] = df['datetaken'].dt.to_period('M')
-
-NameError: name 'df' is not defined
-
df.datetaken.dt.year
-
- | datetaken | -
---|---|
0 | -2024 | -
1 | -2024 | -
2 | -2024 | -
3 | -2024 | -
4 | -2024 | -
... | -... | -
6895 | -2024 | -
6896 | -2024 | -
6897 | -2024 | -
6898 | -2024 | -
6899 | -2024 | -
6900 rows × 1 columns
-Now we have the dates in the correct format, we can plot a figure to identify when most of the photos have been taken/uploaded.
-df.year.value_counts().plot(kind='bar')
-
<Axes: xlabel='year'>
-
As we are dealing with spatial data, it would be nice to plot this information on a map. To do so, we convert our pandas.DataFrame
into a geopandas.GeoDataFrame
. Moreover, we have to specify the coordinate reference system. Given that we have a global dataset, it makes most sense to use epsg:4326, the world geodetic system.
Text(0.5, 1.0, '')
-
Question 2: Describe the plot of the amount of photos over per year (or per month) and the map. Do you already notice specific patterns. Is there something already that may influence our results later on?
Let’s have a look how all these photos are tagged. And whether we can actually do something with this information.
@@ -1752,77 +691,6 @@- | tags | -
---|---|
0 | -lighthouse fjoertoer vuurtoren ameland amelân ... | -
1 | -- |
2 | -beach strân strand plage people summer four do... | -
3 | -- |
4 | -- |
... | -... | -
6895 | -- |
6896 | -- |
6897 | -- |
6898 | -- |
6899 | -- |
6900 rows × 1 columns
-This looks like a mess. It seems we have some work to do to be able to use some of this. Lets get an overview of all the tags and get an idea how often certain tags are used. Write some code to find the most common tags from all of the rows, then print the most popular tags.
Text(0.5, 1.0, '')
-
⚠️ Take your time and play around with the tags. Make your own sets if you like based on different tags.
Question 4: Report the tag sets that you made, along with the number of photos that each tag set belongs to. Why did you make the tag sets you made?
@@ -1885,56 +747,6 @@- | count | -
---|---|
owner | -- |
63991153@N00 | -186 | -
57755771@N05 | -40 | -
20829994@N04 | -32 | -
146166548@N02 | -31 | -
88330064@N07 | -27 | -
And have a look at one of the users with the most pictures
Ok, so it seems that we have several users that dominate the amount of uploads. If we want to say something about the preference of locations to visit, we might have to compensate for this. To do so, we can make use of the groupby function. Which columns would you like to use to make sure you still keep enough unique entries? And which groupby functions will you choose to group on? First, last, mean?
<Axes: >
-
Now let’s make sure we georeference the data and convert it to EPSG:3857 (the same as the Flickr data when plotting).
Question 6: Describe the results of the aggregation through using the grid-based approach. Are you already able to identify some areas that seem to be most preferred? Can you identify them by using, for example, Google Maps? Does it surprise you?
(There’s some issues with deprecation in Pandas in the next section. This code stops some spammy outputs)
@@ -2570,11 +915,6 @@[170048.22796566627, 604297.6778623887, 192423.01071488988, 609454.430915703]
-
Now, we will perform a call to a “web feature service” (WFS), which is a web endpoint that serves geometries. Simply put, it’s a specialized API that serves vector data. Here, we will load a land use layer that contains very detailed descriptions of land use.
-layer names: ['bestandbodemgebruik:BBG2017']
-available variables: 4
-total features: 244
-
Now let’s look at the specific categories that came back. The descriptions are in Dutch unfortunately, but you can run them through a translator to get a gist of what they are.
@@ -2628,34 +961,6 @@{'Bedrijfsterrein',
- 'Begraafplaats',
- 'Bos',
- 'Bouwterrein',
- 'Dagrecreatief terrein',
- 'Delfstofwinplaats',
- 'Detailhandel en horeca',
- 'Hoofdweg',
- 'Noordzee',
- 'Open droog natuurlijk terrein',
- 'Open nat natuurlijk terrein',
- 'Openbare voorziening',
- 'Overig agrarisch terrein',
- 'Overig binnenwater',
- 'Park en plantsoen',
- 'Semi-verhard overig terrein',
- 'Sociaal-culturele voorziening',
- 'Sportterrein',
- 'Stortplaats',
- 'Verblijfsrecreatief terrein',
- 'Vliegveld',
- 'Waddenzee, Eems & Dollard',
- 'Water met recreatieve functie',
- 'Woongebied'}
-
Now we can make some specialized one-hot encoded variables (ones where true, zeroes where false), simplifying the land use to a few simple categories:
@@ -2726,16 +1031,6 @@ sin_time cos_time
-0 -0.609050 -0.793132
-1 -0.775266 -0.631635
-2 -0.485000 -0.874514
-3 0.481435 -0.876482
-4 0.762762 -0.646679
-
Question 8: Which solution did you come up with for point 3? Which kinds of problems could arise if we don’t transform our data into a cyclical range?
Finally, let’s clean the dataframe to contain only the columns we want to retain.
@@ -2747,318 +1042,6 @@- | latitude | -longitude | -rel_yr | -month | -has_nature_tags | -sin_time | -cos_time | -is_built_up | -is_aquatic | -is_natural | -
---|---|---|---|---|---|---|---|---|---|---|
0 | -53.449381 | -5.623358 | -45 | -8 | -0 | --0.609050 | --0.793132 | -0 | -0 | -1 | -
1 | -53.460082 | -5.777918 | -45 | -9 | -0 | --0.775266 | --0.631635 | -0 | -0 | -1 | -
2 | -53.445075 | -5.613788 | -45 | -8 | -1 | --0.485000 | --0.874514 | -0 | -0 | -1 | -
3 | -53.450711 | -5.631969 | -45 | -9 | -0 | -0.481435 | --0.876482 | -0 | -0 | -1 | -
4 | -53.449844 | -5.634163 | -45 | -9 | -0 | -0.762762 | --0.646679 | -1 | -0 | -0 | -
Perfect, now that we have a set of variables we can do some clustering to determine similarity in images.
@@ -3096,9 +1079,6 @@Let’s try to make some more of some of these clusters by looking at their statistics in aggregate:
⚠️ Take your time and play around with the following:
\n", - " | id | \n", - "owner | \n", - "datetaken | \n", - "tags | \n", - "latitude | \n", - "longitude | \n", - "
---|---|---|---|---|---|---|
0 | \n", - "54030466890 | \n", - "63991153@N00 | \n", - "2024-08-29 14:30:05 | \n", - "lighthouse fjoertoer vuurtoren ameland amelân ... | \n", - "53.449381 | \n", - "5.623358 | \n", - "
1 | \n", - "54026176572 | \n", - "10485938@N00 | \n", - "2024-09-25 15:23:19 | \n", - "\n", - " | 53.460082 | \n", - "5.777918 | \n", - "
2 | \n", - "54025875198 | \n", - "63991153@N00 | \n", - "2024-08-29 13:56:03 | \n", - "beach strân strand plage people summer four do... | \n", - "53.445075 | \n", - "5.613788 | \n", - "
3 | \n", - "54025748630 | \n", - "11288822@N04 | \n", - "2024-09-11 10:04:53 | \n", - "\n", - " | 53.450711 | \n", - "5.631969 | \n", - "
4 | \n", - "54025305871 | \n", - "11288822@N04 | \n", - "2024-09-11 08:41:10 | \n", - "\n", - " | 53.449844 | \n", - "5.634163 | \n", - "
5 | \n", - "54025539353 | \n", - "11288822@N04 | \n", - "2024-09-11 10:04:24 | \n", - "\n", - " | 53.450688 | \n", - "5.631980 | \n", - "
6 | \n", - "54025636624 | \n", - "11288822@N04 | \n", - "2024-09-11 10:48:54 | \n", - "\n", - " | 53.459769 | \n", - "5.685588 | \n", - "
7 | \n", - "54024295987 | \n", - "11288822@N04 | \n", - "2024-09-11 10:04:24 | \n", - "\n", - " | 53.450688 | \n", - "5.631980 | \n", - "
8 | \n", - "54025525869 | \n", - "11288822@N04 | \n", - "2024-09-11 10:04:53 | \n", - "\n", - " | 53.450711 | \n", - "5.631969 | \n", - "
9 | \n", - "54025193331 | \n", - "11288822@N04 | \n", - "2024-09-11 08:41:10 | \n", - "\n", - " | 53.449844 | \n", - "5.634163 | \n", - "
10 | \n", - "54025525859 | \n", - "11288822@N04 | \n", - "2024-09-11 10:48:54 | \n", - "\n", - " | 53.459769 | \n", - "5.685588 | \n", - "
11 | \n", - "54023416616 | \n", - "63991153@N00 | \n", - "2024-08-29 13:51:38 | \n", - "backlight tsjinljocht gegenlicht tegenlicht be... | \n", - "53.445075 | \n", - "5.613788 | \n", - "
12 | \n", - "54020292802 | \n", - "63991153@N00 | \n", - "2024-08-29 13:24:45 | \n", - "poles peallen palen pfähle pfahl peal pole str... | \n", - "53.445196 | \n", - "5.614764 | \n", - "
13 | \n", - "54018514474 | \n", - "11288822@N04 | \n", - "2024-09-11 08:41:10 | \n", - "\n", - " | 53.449844 | \n", - "5.634163 | \n", - "
14 | \n", - "54018594770 | \n", - "11288822@N04 | \n", - "2024-09-11 10:49:26 | \n", - "\n", - " | 53.459780 | \n", - "5.685597 | \n", - "
15 | \n", - "54018594740 | \n", - "11288822@N04 | \n", - "2024-09-11 10:50:52 | \n", - "\n", - " | 53.459786 | \n", - "5.685513 | \n", - "
16 | \n", - "54017257037 | \n", - "11288822@N04 | \n", - "2024-09-13 10:44:17 | \n", - "\n", - " | 53.435880 | \n", - "5.625911 | \n", - "
17 | \n", - "54018491864 | \n", - "11288822@N04 | \n", - "2024-09-13 10:47:02 | \n", - "\n", - " | 53.435358 | \n", - "5.625316 | \n", - "
18 | \n", - "54018160326 | \n", - "11288822@N04 | \n", - "2024-09-13 10:45:53 | \n", - "\n", - " | 53.435611 | \n", - "5.625569 | \n", - "
19 | \n", - "54017256987 | \n", - "11288822@N04 | \n", - "2024-09-13 10:55:06 | \n", - "\n", - " | 53.431999 | \n", - "5.629647 | \n", - "
\n", - " | 0 | \n", - "
---|---|
id | \n", - "object | \n", - "
owner | \n", - "object | \n", - "
datetaken | \n", - "object | \n", - "
tags | \n", - "object | \n", - "
latitude | \n", - "object | \n", - "
longitude | \n", - "object | \n", - "
\n", - " | datetaken | \n", - "
---|---|
0 | \n", - "2024 | \n", - "
1 | \n", - "2024 | \n", - "
2 | \n", - "2024 | \n", - "
3 | \n", - "2024 | \n", - "
4 | \n", - "2024 | \n", - "
... | \n", - "... | \n", - "
6895 | \n", - "2024 | \n", - "
6896 | \n", - "2024 | \n", - "
6897 | \n", - "2024 | \n", - "
6898 | \n", - "2024 | \n", - "
6899 | \n", - "2024 | \n", - "
6900 rows × 1 columns
\n", - "\n", - " | id | \n", - "owner | \n", - "datetaken | \n", - "tags | \n", - "latitude | \n", - "longitude | \n", - "geometry | \n", - "year | \n", - "month | \n", - "month_year | \n", - "
---|---|---|---|---|---|---|---|---|---|---|
0 | \n", - "54030466890 | \n", - "63991153@N00 | \n", - "2024-08-29 14:30:05 | \n", - "lighthouse fjoertoer vuurtoren ameland amelân ... | \n", - "53.449381 | \n", - "5.623358 | \n", - "POINT (5.62336 53.44938) | \n", - "2024 | \n", - "8 | \n", - "2024-08 | \n", - "
1 | \n", - "54026176572 | \n", - "10485938@N00 | \n", - "2024-09-25 15:23:19 | \n", - "\n", - " | 53.460082 | \n", - "5.777918 | \n", - "POINT (5.77792 53.46008) | \n", - "2024 | \n", - "9 | \n", - "2024-09 | \n", - "
2 | \n", - "54025875198 | \n", - "63991153@N00 | \n", - "2024-08-29 13:56:03 | \n", - "beach strân strand plage people summer four do... | \n", - "53.445075 | \n", - "5.613788 | \n", - "POINT (5.61379 53.44508) | \n", - "2024 | \n", - "8 | \n", - "2024-08 | \n", - "
3 | \n", - "54025748630 | \n", - "11288822@N04 | \n", - "2024-09-11 10:04:53 | \n", - "\n", - " | 53.450711 | \n", - "5.631969 | \n", - "POINT (5.63197 53.45071) | \n", - "2024 | \n", - "9 | \n", - "2024-09 | \n", - "
4 | \n", - "54025305871 | \n", - "11288822@N04 | \n", - "2024-09-11 08:41:10 | \n", - "\n", - " | 53.449844 | \n", - "5.634163 | \n", - "POINT (5.63416 53.44984) | \n", - "2024 | \n", - "9 | \n", - "2024-09 | \n", - "
... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "
6895 | \n", - "53994026908 | \n", - "64644540@N03 | \n", - "2024-09-11 16:30:06 | \n", - "\n", - " | 53.448263 | \n", - "5.618277 | \n", - "POINT (5.61828 53.44826) | \n", - "2024 | \n", - "9 | \n", - "2024-09 | \n", - "
6896 | \n", - "53993792596 | \n", - "64644540@N03 | \n", - "2024-09-12 10:29:01 | \n", - "\n", - " | 53.427294 | \n", - "5.644697 | \n", - "POINT (5.64470 53.42729) | \n", - "2024 | \n", - "9 | \n", - "2024-09 | \n", - "
6897 | \n", - "53994026418 | \n", - "64644540@N03 | \n", - "2024-09-12 10:41:41 | \n", - "\n", - " | 53.424955 | \n", - "5.674044 | \n", - "POINT (5.67404 53.42495) | \n", - "2024 | \n", - "9 | \n", - "2024-09 | \n", - "
6898 | \n", - "53994215850 | \n", - "64644540@N03 | \n", - "2024-09-12 10:41:34 | \n", - "\n", - " | 53.424955 | \n", - "5.674044 | \n", - "POINT (5.67404 53.42495) | \n", - "2024 | \n", - "9 | \n", - "2024-09 | \n", - "
6899 | \n", - "53994215600 | \n", - "64644540@N03 | \n", - "2024-09-12 10:41:19 | \n", - "\n", - " | 53.424955 | \n", - "5.674044 | \n", - "POINT (5.67404 53.42495) | \n", - "2024 | \n", - "9 | \n", - "2024-09 | \n", - "
6900 rows × 10 columns
\n", - "\n", - " | tags | \n", - "
---|---|
0 | \n", - "lighthouse fjoertoer vuurtoren ameland amelân ... | \n", - "
1 | \n", - "\n", - " |
2 | \n", - "beach strân strand plage people summer four do... | \n", - "
3 | \n", - "\n", - " |
4 | \n", - "\n", - " |
... | \n", - "... | \n", - "
6895 | \n", - "\n", - " |
6896 | \n", - "\n", - " |
6897 | \n", - "\n", - " |
6898 | \n", - "\n", - " |
6899 | \n", - "\n", - " |
6900 rows × 1 columns
\n", - "\n", - " | count | \n", - "
---|---|
owner | \n", - "\n", - " |
63991153@N00 | \n", - "186 | \n", - "
57755771@N05 | \n", - "40 | \n", - "
20829994@N04 | \n", - "32 | \n", - "
146166548@N02 | \n", - "31 | \n", - "
88330064@N07 | \n", - "27 | \n", - "
\n", - " | id | \n", - "owner | \n", - "datetaken | \n", - "tags | \n", - "latitude | \n", - "longitude | \n", - "geometry | \n", - "year | \n", - "month | \n", - "month_year | \n", - "nature | \n", - "
---|---|---|---|---|---|---|---|---|---|---|---|
0 | \n", - "54025875198 | \n", - "63991153@N00 | \n", - "2024-08-29 13:56:03 | \n", - "beach strân strand plage people summer four do... | \n", - "53.445075 | \n", - "5.613788 | \n", - "POINT (624924.022 7065752.861) | \n", - "2024 | \n", - "8 | \n", - "2024-08 | \n", - "yes | \n", - "
1 | \n", - "54023416616 | \n", - "63991153@N00 | \n", - "2024-08-29 13:51:38 | \n", - "backlight tsjinljocht gegenlicht tegenlicht be... | \n", - "53.445075 | \n", - "5.613788 | \n", - "POINT (624924.022 7065752.861) | \n", - "2024 | \n", - "8 | \n", - "2024-08 | \n", - "yes | \n", - "
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467 rows × 11 columns
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