The errata list is a list of errors and their corrections that were found after the product was released. If the error was corrected in a later version or reprint the date of the correction will be displayed in the column titled "Date Corrected".
The following errata were submitted by our customers and approved as valid errors by the author or editor.
Color key: Serious technical mistake Minor technical mistake Language or formatting error Typo Question Note Update
| Version |
Location |
Description |
Submitted By |
Date submitted |
Date corrected |
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Page Ch. 2, "Visualizing Environmental Complaints in New York"
Paragraph 1 |
The paragraph has a hyperlink attached to the sentence "This particular dataset can be downloaded from the GitHub repository accompanying this book."
The URL redirects to the following no-longer-functioning repository and repository entry.
github.com/datamongerbonny/Python-for-geospatial-analysis/blob/main/DOHMH_Indoor_Environmental_Complaints.csv
The same broken URL is present in the Chapter 4 Section "Leafmap: An Alternative to Google Earth Engine" in Paragraph 3 of the section, and vitally, the initial appearance of the broken URL is pointed to in the book's index entry "Github Repository for this book", returning the reader back to the first appearance in the Ch. 2, "Visualizing Environmental Complaints in New York" section.
Note from the Author or Editor:
This is available here:
github.com/datamongerbonny/SQL-for-Geospatial/blob/main/DOHMH_Indoor_Environmental_Complaints.csv
We will update the links in the book. Thanks! |
Anonymous |
Dec 02, 2022 |
Dec 06, 2022 |
| Printed |
Page p.65
Code in figure--pointed out by Japanese translation |
Q3. [P.65] For database with 110m resolution, I couldn't find any city near amazon river. When I used 10m resolution map instead of 110m, I get good result as shown in fig3-22.png.
rivers = '{}|layername=ne_110m_rivers_lake_centerlines'.format(my_gpkg)
places ='{}|layername=ne_110m_populated_places'.format(my_gpkg)
are replaced with,
rivers = '{}|layername=ne_10m_rivers_lake_centerlines'.format(my_gpkg)
places ='{}|layername=ne_10m_populated_places'.format(my_gpkg)
|
Bonny McClain
|
Dec 15, 2022 |
|
| PDF |
Page page 117
Last paragraph |
Working with QuickOSM in QGIS
You got acquainted with QGIS in Chapter 3, and I want to point out briefly that you can also use QGIS to interact with OSM data. QuickOSM in QGIS works similarly within a GUI.
[[Asking the reader to download the plug-in appears to be missing:]]
Navigate to the plug-in menu and download the Quick OSM plug-in.
Open QGIS and select Vector in the toolbar, and you will see a “Quick query” option for Quick OSM (Figure 5-18). QuickOSM allows you to run these quick queries in the console and display results directly on a canvas.
Getting access to the level of data in GIS format within QGIS allows you to select a variety of amenities to include in a single map layer. This is an opportunity to explore key:value relationships outside of a Notebook as your data questions grow in scope.
|
Bonny McClain
|
Dec 15, 2022 |
|
| Printed |
Page page 181
second paragraph |
This is stated:
GeoPandas is a Cartesian coordinate reference system, which means that each point is defined by a pair of numerical coordinates, such as latitude and longitude in our example.
It should be corrected to this:
GeoPandas is a Cartesian coordinate reference system, which means that each point is defined by a horizontal x-axis and a vertical y-axis on a planar surface.
|
Bonny McClain
|
Dec 18, 2022 |
|
| PDF |
Page 173
figure 8-5 |
This current code should run above the figure 8-5:
size, scale = 1000, 10
Complaints = pd.Series(np.random.gamma(scale, size=size) ** 1.5)
Complaints.plot.hist(grid=True, bins=20, rwidth=0.9, color='#607c8e')
plt.title('Distribution of Complaint Types')
plt.xlabel('CMPLNT_NUM')
plt.ylabel('OFNS_DESC')
plt.grid(axis='y', alpha=0.75)size, scale = 1000, 10
Complaints = pd.Series(np.random.gamma(scale, size=size) ** 1.5)
[[new text]]
If instead of running a random sample (np.random.gamma()), we run a sample from the dataset, you will generate the outcome in figure 8-5a.
size, scale = 1000, 10
Complaints = pd.Series(df['CMPLNT_NUM'], df['PD_CD'])
Complaints.plot.hist(grid=True, bins=10, rwidth=0.9,
color='#607c8e')
plt.title('Distribution of Complaint Types')
plt.xlabel('CMPLNT_NUM')
plt.ylabel('PD_CD')
plt.grid(axis='y', alpha=0.75)
The visualization now does not yield a pattern in the distribution of complaint types (Figure 8-5a).
The updated figure will be sent directly...
|
Bonny McClain
|
Jan 11, 2023 |
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