Introduction

Ever puzzled how the topography of your nation influences financial and political growth? Topographic maps – maps of the earth’s floor that use contour strains for visualization – will help reply these questions! We are going to use Python to create a topographic map for Nepal, a rustic with an fascinating topographic setting. You’ll discover ways to learn geospatial information that describes the topography of a rustic, the way to interpret this information, and the way to visualize it. The ensuing map will be mixed with different information of curiosity at very disaggregated subnational ranges to grasp how the topography of a rustic influences its financial and/or political growth. This weblog submit will educate you the way to generate a very fascinating instrument that may inform insurance policies and personal sector growth!

Studying Goals

• Achieve proficiency in information evaluation methods for digital elevation information.
• Learn to use geospatial information and associated evaluation instruments in Python.
• Purchase information of mapping methods.
• Develop abilities in efficient information visualization for communication.
• Perceive the significance of elevation for inequality and poverty.

This text was printed as part of the Information Science Blogathon.

What are Topographic Maps?

Topographic maps are maps of the earth’s floor that use contour strains for visualization. Topographic maps are a priceless instrument for navigating unfamiliar terrain and function city planning and catastrophe administration inputs. They’re typically used to grasp the spatial context of insurance policies or personal sector tasks round infrastructure growth, to determine areas weak to pure disasters or with restricted entry to important companies, reminiscent of schooling, healthcare, and infrastructure, or for pure useful resource administration. Finally, these maps can function enter for evidence-based decision-making. On this weblog submit, we’ll use Python to create a topographic map for Nepal, a rustic with a very fascinating topographic setting.

Information Description

To generate our map, we’ll depend on information printed by the United States Geological Survey (USGS). USGS is a scientific company of the USA federal authorities that generates information and analysis round pure sources, geology, geography, water sources, and pure hazards. To get to their information web page, sort “USGS Information” in Google or click on the hyperlink that directs you to their Earth Explorer. The Earth Explorer is an internet instrument and information portal that permits you to search, entry, and obtain a variety of Earth science information. You should arrange an account and log in to totally use the information.

Information Obtain

This weblog submit will use Nepal for example on account of its distinctive topographic traits. Nepal has one of the vital difficult and fascinating topographies on the earth. 8 out of the 14 mountains above 8,000 m are in Nepal (Trekking Path Nepal), and the nation is split into three very totally different topographic areas: the Mountains, Hills, and Terai (or plains) (DHS). Whereas these traits make the nation distinctive and fascinating, some analysis exhibits that the topography of Nepal makes it difficult to attach the nation, ship important companies to its inhabitants, and impose dangers and obstacles to a sustainable growth path.

To this finish, we’ll filter for Nepal within the Search Standards, as indicated within the image under. As soon as we chosen Nepal, we chosen our dataset of curiosity. To take action, click on the Information Units tab and select Digital Elevation. There are a number of choices for Digital Elevation Information, and whilst you might use a number of of those datasets, we’ll use the International Multi-resolution Terrain Elevation Information 2010 GMTED2010 information. This information supplies international protection of the Earth’s terrain at a number of resolutions (starting from 7.5 arc-seconds (roughly 250 meters) to 30 arc-seconds (roughly 1 kilometer)). It’s generated from spaceborne and airborne distant sensing information, together with satellite tv for pc altimetry, stereo-imagery, and topographic maps.

When you select the information, click on on the Outcomes tab. Now you can obtain the information by clicking the image with obtain choices. You may as well show the information by way of the footprint icon. We obtain the information in its highest decision (7.5 arc seconds). Importantly, to cowl all of Nepal, we have to obtain two totally different mosaics (elements) of the underlying information and mix them later. You will notice that the ensuing information set is in a tif format, which signifies raster information.

Python supplies a number of instruments for geospatial evaluation. On this weblog submit, we depend on the Rasterio library that makes it doable to learn and write geospatial raster information (gridded information). Let’s get began and skim the primary mosaic (half) of the information we beforehand downloaded into our Jupyter Pocket book:

#import related libraries (after putting in them)
import rasterio
import matplotlib.pyplot as plt
import numpy as np

#Learn the information and present the form of the dataset
file = rasterio.open(r'path10n060e_20101117_gmted_mea075.tif')
dataset = file.learn()
print(dataset.form)

Let’s additionally add the second mosaic and mix them by merging them. To this finish, we observe commonplace raster information studying and manipulation methods in Python as follows:

#Add second dataset and present the form of the dataset
file2 = rasterio.open(r'path30n060e_20101117_gmted_mea075.tif')
dataset2 = file2.learn()
print(dataset2.form)


#Mix each datasets
from rasterio.merge import merge
from rasterio.plot import present

#Create empty listing
src_files_to_mosaic = []

#Append the listing with each information
src_files_to_mosaic.append(file)
src_files_to_mosaic.append(file2)
src_files_to_mosaic

#Merge each information
mosaic, out_trans = merge(src_files_to_mosaic)

# Copy Metadata
output_meta = file.meta.copy()

#Replace Metadata
output_meta.replace(
    {"driver": "GTiff",
        "peak": mosaic.form[1],
        "width": mosaic.form[2],
        "rework": out_trans,
    }
)

#Write to vacation spot
# Write the mosaic raster to disk
out_fp = r"pathNepal_Mosaic.tif"

with rasterio.open(out_fp, "w", **output_meta) as dest:
        dest.write(mosaic)

#Open the mixed raster information
file_mosaic = rasterio.open(out_fp)

#Learn the information
dataset_mosaic = file_mosaic.learn()
print(file_mosaic.form)

#Present the information
plt.imshow(dataset_mosaic[0], cmap='Spectral')
plt.present()

International Multi-resolution Terrain Elevation Information

We now have a mixed International Multi-resolution Terrain Elevation Information 2010 GMTED2010 information for all of Nepal, however the file additionally covers massive elements of the encompassing space that aren’t a part of Nepal. Let’s prohibit the world to Nepal through the use of a shapefile of Nepal. We are going to use a shapefile with nation borders for the world. You’ll be able to obtain this dataset right here. Let’s then clip the raster information and shapefile utilizing the masks perform. We are going to solely use the primary row of the shapefile and the geometry column. The results of this operation is saved in clipped_array, which is the clipped raster information, and clipped_transform, which represents the transformation data of the clipped raster.

import geopandas as gpd
from shapely.geometry import mapping
from rasterio import masks as msk#import csv

#Add shapefile with nation boarders of the world
df = gpd.read_file(r'path/world-administrative-boundaries.shp')

#Prohibit to Nepal
nepal = df.loc[df.name=="Nepal"]
nepal.head()

#Clip information
clipped_array, clipped_transform = msk.masks(file_mosaic, [mapping(nepal.iloc[0].geometry)], crop=True)

#

There may be one remaining downside. The no information values in raster information are extremely damaging. Due to this fact, would distort the visualization of our map, as these kind a part of the worth vary.

Perceive the Downside

Let’s maintain this downside as follows, as described in this weblog submit:

• Let’s construct a perform that takes care of no information values. We assemble a no-data parameter to specify the worth thought-about “no information” within the clipped array. On this case, it’s set to (np.amax(clipped_array[0]) + 1), which signifies that it is the same as the utmost worth within the clipped array plus one. This worth shall be thought-about because the “no information” worth.
• Regulate the clipped array by including absolutely the worth of the minimal worth within the clipped array to the primary band (index 0) of the clipped array. This step ensures that each one values within the clipped array change into non-negative.
• We additionally calculate the worth vary of the clipped array. It provides the utmost and absolute worth of the minimal worth within the clipped array. The value_range variable will maintain the calculated worth vary.
• Use a manually constructed color-value dictionary based mostly on an present one (the seismic one) and outline our background shade for the “no information” values.
• Within the final step, we plot the map with the brand new shade vary referred to as new_seismic.

#Let's examine no information values

nodata_value = file_mosaic.nodata 
print("Nodata worth:", nodata_value)
#Nodata worth: -32768.0

#Change worth of nodata to at least one greater than the utmost elevation
 def clip_raster(gdf, img):
     clipped_array, clipped_transform = msk.masks(img, [mapping(gdf.iloc[0].geometry)], crop=True)
     clipped_array, clipped_transform = msk.masks(img, [mapping(gdf.iloc[0].geometry)],
                                                           crop=True, nodata=(np.amax(clipped_array[0]) + 1))
     clipped_array[0] = clipped_array[0] + abs(np.amin(clipped_array))
     value_range = np.amax(clipped_array) + abs(np.amin(clipped_array))
     return clipped_array, value_range

nepal_topography, value_range = clip_raster(nepal, file_mosaic)


#Examine that this labored
print(value_range)


#Let's give the nodata worth a brand new background shade
from matplotlib import cm
from matplotlib.colours import ListedColormap,LinearSegmentedColormap

#Sesmic
new_seismic = cm.get_cmap('seismic', 8828)

#Outline background shade
background_color = np.array([0.9882352941176471, 0.9647058823529412, 0.9607843137254902, 1.0])

#Use shade map
newcolors = new_seismic(np.linspace(0, 1, 8828))

# Add the background shade because the final row to the newcolors array.
newcolors = np.vstack((newcolors, background_color))

#Use new Italy Shade Map
new_seismic = ListedColormap(newcolors)

#Create last map and save
plt.determine(figsize=(10,10))
c = plt.imshow(nepal_topography[0], cmap = new_seismic)
clb = plt.colorbar(c, shrink=0.4)
clb.ax.set_title('Elevation (meters)',fontsize=10)

plt.savefig(r'pathTopographic_Map_Nepal.png', bbox_inches="tight")
plt.present()

Voilá! Now we have a topographic map of Nepal that clearly signifies the totally different elevations within the nation and the three topographic zones.

Conclusion

You discovered to generate a topographic map in Python utilizing geospatial information from the United States Geological Survey (USGS). You additionally discovered the significance of caring for lacking values within the last dataset for visualization.

Policymakers or practitioners can now use this map for additional evaluation, reminiscent of combining it with different maps, reminiscent of maps of poverty, or pure disasters, to investigate if there’s some connection. Now we have generated a priceless instrument that may inform evidence-based decision-making in politics!

Key Takeaways

• Topographic Maps are helpful instruments for evidence-based decision-making.
• Topography and elevation play a vital function in city planning, service supply, and inequality.
• Python has helpful instruments for analyzing geospatial information.
• Caring for no information values in any such information is essential for visualization.
• Visualizing geospatial information can generate priceless data at disaggregated ranges.

Hope you discovered this text informative. Be at liberty to achieve out to me on LinkedIn. Let’s join and work in direction of leveraging information for constructive change.

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