Geospatial data analysis involves studying geography, maps, and spatial relationships to derive insights from data that has a location component. It helps understand and interpret information tied to specific places, like cities or buildings. For instance, it aids city planners in identifying optimal park locations by analyzing green space availability and foot traffic. Similarly, it enables tracking disease spread using location-based data. Geospatial analysis empowers organizations to make informed decisions using geographic and location-based data. This article explores how to perform data analysis on geospatial information.
This article was published as a part of the Data Science Blogathon.
Geospatial data refers to information that is tied to specific geographic locations on the Earth’s surface. It includes data such as coordinates, addresses, maps, satellite imagery, and any other data with spatial references. Geospatial data enables analysis, visualization, and understanding of the spatial relationships, patterns, and attributes of features and phenomena in the real world.
Geospatial data analysis is a type of data analysis that focuses on the study and exploration of geographical data. It involves using geographical information to understand patterns, relationships, and trends between different spatial features and attributes. Geospatial data analysis can answer various questions, such as where certain phenomena are more likely to occur, how human activities affect the environment, and the distribution of certain population groups. Geospatial data analysis involves collecting, cleaning, and transforming geospatial data, creating maps and visualizations, and conducting statistical analysis to identify patterns and relationships.
Geospatial data refers to information tied to a specific location on the Earth’s surface, often represented as coordinates on a map or in a geographic information system (GIS). This data type is crucial for many industries and fields, including urban planning, environmental management, marketing, and transportation. The importance of geospatial data lies in its ability to help organizations better understand and make decisions about complex real-world phenomena linked to specific locations. This data can be used to visualize patterns and relationships, analyze trends, and predict future outcomes. In addition, integrating geospatial data with other forms of data, such as demographic and economic data, can provide a better picture of a given area and support informed decision-making.
Collecting and preprocessing geospatial data is a crucial step in geospatial data analysis. This involves collecting data from various sources, such as satellite imagery, GPS, and GIS systems, and converting it into the correct format that can be analyzed and visualized. Preprocessing involves cleaning, transforming, and integrating the data to ensure it is in a consistent and usable format. This step is important as it lays the foundation for accurate and meaningful analysis and visualizations. It also involves defining the coordinate reference system, which provides a standard frame of reference for the data and helps make accurate spatial comparisons. The analyst can make informed decisions and draw meaningful insights from the data by properly preprocessing geospatial data.
When it comes to visualizing geospatial data, there are several tools and techniques available that data scientists can use. Some of the most common ones include:
Using these tools and techniques, data scientists can effectively visualize and analyze geospatial data, leading to valuable insights and informed decision-making.
Spatial data analysis involves evaluating and modeling geographical or spatially referenced data. Some of the commonly used techniques and algorithms in this field include:
These methods are applied to various geospatial data sources, including satellite imagery, remote sensing data, and geographic information systems (GIS) data, to support a wide range of applications, such as land use planning, environmental monitoring, and public health surveillance. These techniques provide a way to analyze spatial data and uncover patterns and relationships that would be difficult to discern otherwise.
Integrating geospatial data with other data sources involves combining and analyzing geospatial data with data from other sources to gain more insights and context. For example, integrating geospatial data with demographic data can help analyze the relationship between population density and the spread of disease. Combining it with economic data can provide insights into the impact of natural disasters on local businesses. Integration can be done using data warehousing, data management, and data analysis tools and techniques, such as SQL databases, data visualization tools, and machine learning algorithms. This integration allows for more comprehensive analysis, leading to better decision-making and outcomes.
These are only a few real-world examples of the many applications of geospatial data analysis in data science. With the rapid growth of geospatial data, the demand for geospatial analysis skills is increasing, and the field of geospatial data analysis continues to evolve and grow.
By following these best practices, organizations can overcome the challenges of geospatial data analysis and make the most of the insights and opportunities that geospatial data provides.
Also, Future trends in geospatial data analysis in data science include open data, geospatial science, and predictive analytics. These advancements will improve data quality, increase computational efficiency, enhance visualization and communication, foster interdisciplinary collaboration, and enable predictive insights. As a result, organizations embracing these trends will be better positioned to take advantage of geospatial data insights.
Let us perform a basic geospatial Data analysis with python implementation on a dataset from Kaggle. You can download the data from here.
This dataset identifies hazardous areas for driving according to harsh braking and accident-level events within a specific area. Each month a new set of dangerous driving areas is produced and encapsulates one year of rolling data (i.e., from the previous month back 1 year). Associated with each area is a severity score based on the frequency of occurrences and the severity of said occurrences. Data is aggregated over the previous 12 months.
You can find the detail about the dataset columns here.
Some variables to point out:
Note: To perform geospatial analysis using the geopy library, you would require a laptop with graphics for better use. If you do not have one would recommend you use google collab with GPU enabled in the runtime type. For demonstration, I would be using collab to perform the geospatial analysis.
Python Code:
#importing required Libraries
import pandas as pd
import numpy as np
from shapely.geometry import Point
import geopandas as gpd
import folium
import matplotlib.pyplot as plt
import seaborn as sns
#Load the dataset
#store the data into a data frame
data = pd.read_csv("Hazardous_driving_areas.csv")
# Read the First five rows in the dataset
print(data.head())
#Check the size of the dataset
print(data.shape)
# Check for duplicate entries
duplicate_rows = data.duplicated().sum()
print(f"duplicate rows = {duplicate_rows}")
o/p--> duplicate rows = 0
# check for missing values
data.isnull().sum()
We can see that city, county, state, ISO_3166_2, and country variables have a significant number of missing values.
# for any given latitude and location, we can derive the country using the geopy library
# For example
from geopy.geocoders import Nominatim
geolocator = Nominatim(user_agent="geoapiExercises")
def get_location_info(lat, long):
location = geolocator.reverse(f"{lat}, {long}", exactly_one=True)
address = location.raw['address']
country = address.get('country', '')
return country
lat = 49.024
long = -123.155
country = get_location_info(lat, long)
print(f"country: {country}")
o/p –> Country: Canada
As we have a significant number of missing values in the city, county, state, and country variables have missing values and to impute them using the geopy library is difficult as the server will not be able to retrieve the address for all the data points at a time. And as we are focusing more on the country with the highest hazardous spots. Let’s impute the missing values in the country column.
# Create an instance of the Nominatim geolocator
geolocator = Nominatim(user_agent="geoapiExercises"
# Impute the missing values for the Country column
for index, row in data.iterrows():
if pd.isna(row['Country']):
location = geolocator.reverse(f"{row['Latitude']}, {row['Longitude']}", exactly_one=True)
data.at[index, 'Country'] = location.raw['address']['country']
print(f"missing values in country Variable :{data.Country.isnull().sum()}")
o/p –> missing values in country Variable :0
we can see that the missing values in the country variable are imputed
# plot histograms for each numeric feature
df.hist(bins=50, figsize=(20,15))
plt.show()
We can see the distribution of each of the variables independently.
Let us plot a heat map
# Create a correlation matrix
corr_matrix = df.corr()
# Plot the heatmap
sns.heatmap(corr_matrix,annot=True)
plt.show()
From the Heatmap, we can get to know the correlation between the different variables. Here we observe that severity score and total incident variables are positively correlated. These observations are important in deciding the model we can choose for prediction, etc.
# visualizing the datapoints spatially
import folium
# Create a map centered at the mean latitude and longitude
mean_latitude = df['Latitude'].mean()
mean_longitude = df['Longitude'].mean()
map_data = folium.Map(location=[mean_latitude, mean_longitude], zoom_start=10)
# Add a marker for each location
for index, row in data.iterrows():
folium.CircleMarker([row['Latitude'], row['Longitude']],
radius=5,
color='red',
fill=True,
fill_color='red',
fill_opacity=0.7).add_to(map_data)
# Display the map
map_data
Note: The above visualization contains the images after Zoomimng the output for better visualization of all the data points.
data.SeverityScore.value_counts()
#Create a base map centered around the mean latitude and longitude of the data
mean_lat = df['Latitude'].mean()
mean_lon = df['Longitude'].mean()
m = folium.Map(location=[mean_lat, mean_lon], zoom_start=10)
# Create a scatter map where the color of each marker is based on the value of the 'SeverityScore' column
for lat, lon, severity_score in zip(df['Latitude'], df['Longitude'], df['SeverityScore']):
folium.CircleMarker(
[lat, lon],
radius=5,
color='red' if severity_score > 0.75 else 'yellow' if severity_score > 0.50 else 'green',
fill=True,
fill_opacity=0.7
).aof(m)
# Show the map
m
The red color InIndicate the Spots with a high Severity Score,Yellow – is a medium severity score, and Green with a low Severity score.
import folium
# Group the data by country and calculate the number of incidents in each country
grouped_data = df.groupby('Country').agg({'IncidentsTotal': 'sum'}).reset_index()
# Find the country with the highest number of incidents
max_country = grouped_data.loc[grouped_data['IncidentsTotal'].idxmax(), 'Country']
# Create a base map centered around the mean latitude and longitude of your data
mean_lat = df['Latitude'].mean()
mean_lon = df['Longitude'].mean()
m = folium.Map(location=[mean_lat, mean_lon], zoom_start=1)
# Highlight the country with the highest number of incidents
folium.GeoJson(
world_geo,
style_function=lambda feature: {
'fillColor': 'red' if feature['properties']['name'] == max_country else 'gray',
'fillOpacity': 0.5,
'color': 'black',
'weight': 1,
}
).add_to(m)
# Show the map
m
Observation: Mexico is the country that has the highest number of incidents in total
import folium
# Group the data by country and calculate the number of incidents in each country
grouped_data = data.groupby('Country').agg({'IncidentsTotal': 'sum'}).reset_index()
# Find the country with the highest number of incidents
min_country = grouped_data.loc[grouped_data['IncidentsTotal'].idxmin(), 'Country']
# Create a base map centered around the mean latitude and longitude of your data
mean_lat = data['Latitude'].mean()
mean_lon = data['Longitude'].mean()
m = folium.Map(location=[mean_lat, mean_lon], zoom_start=1)
# Highlight the country with the highest number of incidents
folium.GeoJson(
world_geo,
style_function=lambda feature: {
'fillColor': 'red' if feature['properties']['name'] == min_country else 'gray',
'fillOpacity': 0.5,
'color': 'black',
'weight': 1,
}
).add_to(m)
# Show the map
m
This simple demonstration showed how geospatial analysis would give you a clear picture of your data and how you can use different plots to derive insights. Also, you could create a predictive model on the data using different spatial classification and clustering techniques depending on the problem statement.
Geospatial data analysis is a required field in data science with various applications, including land use planning, environmental monitoring, and public health surveillance. The field encompasses several techniques and algorithms, such as spatial interpolation, spatial regression, spatial clustering, and spatial autocorrelation analysis, which help extract insights from various geospatial data sources. Best practices in geospatial data analysis include ensuring data quality, efficient data management, computational efficiency, effective visualization and communication, and privacy protection.
The future of geospatial data analysis looks promising, with advancements in AI/ML, 5G/IoT, cloud computing, VR/AR, open data, geospatial data science, and predictive analytics set to play a significant role. Organizations that embrace these trends will be better positioned to take advantage of the insights that geospatial data provides and make informed decisions. Geospatial data analysis is a valuable tool for organizations looking to unlock the potential of their geospatial data.
A. An example of geospatial information is a map that displays the locations of various landmarks, such as roads, buildings, and natural features like rivers or mountains.
A. Geospatial data provides information about the Earth’s surface and features, allowing us to analyze, visualize, and make informed decisions based on the spatial relationships and patterns in the data.
A. Geospatial data and spatial data are often used interchangeably. However, geospatial data typically refers to data that contains geographic or location information. In contrast, spatial data can encompass a broader range of data that deals with spatial relationships and patterns, irrespective of geographic context.
A. GPS (Global Positioning System) is a technology used to determine precise location on the Earth’s surface. While GPS can be used to collect geospatial data, the raw GPS data alone does not constitute geospatial data. Geospatial data involves additional information, such as attributes or characteristics associated with specific locations.
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