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Geospatial Data Science Quick Start Guide
Geospatial Data Science Quick Start Guide

Geospatial Data Science Quick Start Guide: Effective techniques for performing smarter geospatial analysis using location intelligence

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Profile Icon Abdishakur Hassan Profile Icon Jayakrishnan Vijayaraghavan
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Full star icon Full star icon Full star icon Full star icon Empty star icon 4 (6 Ratings)
Paperback May 2019 170 pages 1st Edition
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Arrow left icon
Profile Icon Abdishakur Hassan Profile Icon Jayakrishnan Vijayaraghavan
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$32.99
Full star icon Full star icon Full star icon Full star icon Empty star icon 4 (6 Ratings)
Paperback May 2019 170 pages 1st Edition
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Geospatial Data Science Quick Start Guide

Consuming Location Data Like a Data Scientist

Location comes in different forms, but what if it comes in a simple structured data format and we overlooked it all this time? Most machine learning algorithms, such as random forests, are geared toward creating insights from structured data in tabular form. In this chapter, we will discuss how to leverage spatial data that is masquerading as tabular data and apply machine learning techniques to it as any data scientist would. For this chapter, we will be using New York taxi trip data to predict trip duration for any given New York taxi trip. We are choosing this dataset because of the following reasons:

  • Predicting trip duration has the right mix of geospatial analytics and machine learning
  • Finding the time it takes to travel from point A to point B is a routing problem, which will be dealt with in Chapter 6, Let's Build a Routing...

Exploratory data analysis

For this chapter, we will be using curated data from the New York taxi trip dataset provided by the city of New York. The original source for this data can be found here: https://data.cityofnewyork.us/api/odata/v4/hvrh-b6nb.

Visit the following website for more details about the data that's included in this dataset: https://data.cityofnewyork.us/Transportation/2016-Green-Taxi-Trip-Data/hvrh-b6nb.

For starters, let's have a peek at the data at hand using pandas. The curated data (NYC_sample.csv) that we will be using here can be found at the following download link: https://drive.google.com/file/d/1OkkYZJEcsdCkU0V42eP6pj6YaK2WCGCE/view.

df = pd.read_csv("NYC_sample.csv")
df.head().T

The curated New York taxi trip data that we are using has around 1.14 million records and has columns related to taxi fare, as well as trip duration, as...

Spatial data processing

We will be discussing three things in this section: taxi zones, spatial joins, and the calculation of distances.

Taxi zones in New York

Analyzing and processing a taxi zone spatial data helps us achieve two objectives:

  • Substitute the missing coordinates for pickup and dropoff locations with the taxi zone's centroid
  • Use the taxi zone as a feature in the model

Visualization of taxi zones

We have provided the shapefile for the taxi zones in the data repository. Shapefiles can be read as (Geo)DataFrames with the Python library known as GeoPandas...

Error metric

If we visit the evaluation section of the Kaggle competition, the evaluation metric is defined as the RMSLE. In the competition, the objective is to minimize this metric for the test data. An error is simply the difference between actual values and predicted values:

error = predicted value - actual value

The Root Mean Squared Error (RMSE) would literally be the square root applied over the mean of all the squared error terms for each observation.

However, our metric in the Kaggle competition needs to be a log error:

log_error = log(predicted value + 1) - log(actual value + 1)

Therefore, it is important to apply a log transform over the trip_duration column as we did earlier:

df["trip_duration"] = np.log(df["trip_duration"] + 1) 

Now, we can use a function that can calculate RMSE rather a function that calculates RMSLE:

import math 
def rmse(x,y...

Building the model

Let's build the final model using a random forest regressor. A random forest is a universal machine learning technique, that is, it can handle different kinds of data; it could be a category (classification), a continuous variable (regression), or features of any kind, such an image, price, time, post codes, and so on (that is, both structured and unstructured data). It doesn't generally overfit too much, and it is very easy to stop it from overfitting. For these reasons, random forest is a versatile ML technique which we can effectively use to solve our problem.

Validation data and error metrics

Our initial step is choosing a suitable size for validation data. Before delineating the validation...

Summary

In this chapter, we chose a pertinent problem that had both analytics and geospatial components and tried to apply a very robust ML technique known as random forest to it. Before building the model, we had to handle the date component, the spatial component of data, as well as the categorical and continuous variables. We were able to achieve a good score in our first pass and build a world-class model with a few lines of code and a little bit of spatial data processing.

In the next chapter, we will discuss more accurate real-world distance metrics and perform other spatial computations, such as intersection, to make the model better.

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Key benefits

  • Manipulate location-based data and create intelligent geospatial data models
  • Build effective location recommendation systems used by popular companies such as Uber
  • A hands-on guide to help you consume spatial data and parallelize GIS operations effectively

Description

Data scientists, who have access to vast data streams, are a bit myopic when it comes to intrinsic and extrinsic location-based data and are missing out on the intelligence it can provide to their models. This book demonstrates effective techniques for using the power of data science and geospatial intelligence to build effective, intelligent data models that make use of location-based data to give useful predictions and analyses. This book begins with a quick overview of the fundamentals of location-based data and how techniques such as Exploratory Data Analysis can be applied to it. We then delve into spatial operations such as computing distances, areas, extents, centroids, buffer polygons, intersecting geometries, geocoding, and more, which adds additional context to location data. Moving ahead, you will learn how to quickly build and deploy a geo-fencing system using Python. Lastly, you will learn how to leverage geospatial analysis techniques in popular recommendation systems such as collaborative filtering and location-based recommendations, and more. By the end of the book, you will be a rockstar when it comes to performing geospatial analysis with ease.

Who is this book for?

Data Scientists who would like to leverage location-based data and want to use location-based intelligence in their data models will find this book useful. This book is also for GIS developers who wish to incorporate data analysis in their projects. Knowledge of Python programming and some basic understanding of data analysis are all you need to get the most out of this book.

What you will learn

  • Learn how companies now use location data
  • Set up your Python environment and install Python geospatial packages
  • Visualize spatial data as graphs
  • Extract geometry from spatial data
  • Perform spatial regression from scratch
  • Build web applications which dynamically references geospatial data
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Table of Contents

8 Chapters
Introducing Location Intelligence Chevron down icon Chevron up icon
Consuming Location Data Like a Data Scientist Chevron down icon Chevron up icon
Performing Spatial Operations Like a Pro Chevron down icon Chevron up icon
Making Sense of Humongous Location Datasets Chevron down icon Chevron up icon
Nudging Check-Ins with Geofences Chevron down icon Chevron up icon
Let's Build a Routing Engine Chevron down icon Chevron up icon
Getting Location Recommender Systems Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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2 star 33.3%
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NK Jul 09, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The contents of the book shows how much of hard work authors have put in. Its a must read for Data Scientist looking to propel GIS career. The authors have taken a very clear cut approach in show casing all aspects of techniques for performing smarter geospatial analysis using location intelligence. Loved reading it.
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Kurt Jan 02, 2021
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This book is a straightforward guide about geospatial analysis with python. A github repository accompanies the book as well. The only drawback is the black-white print out.
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Iyyanki Jun 16, 2022
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Contents are good
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Vinicius Oct 29, 2019
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Livro básico para uma área carente de fontes. Muito bom!
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Johann H. Aug 11, 2019
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
I really wanted to like the book, but I didn't and can't really recommend it for Data Scientists.The book reads like a medium article. That means, very little to no theory explained, a lot of: how to implement this and that, what tools to use etc. For my personal taste this book has to little content. I felt like I learned close to nothing, even though I am not an experienced GIS user. Hence I wonder what the target group is for this book: Data Scientists know how to use pandas and people who don't know Data Science wont learn it from this book since there is to little theory.Furthermore there are a lot of spelling errors, repetitions etc. that makes me wonder if this book had an editor.One thing made me particularly angry: In pretty much every chapter starts with a formulation like "In this chapter we will learn to do Task X", then they call a python function from a loaded package task_x(your_data_goes_here) and close the chapter with a sentence: "In this chapter we learned Task X". No you did not. You simply loaded a damn library, that has nothing to do with learning.There might be a subset of people for whom this book is useful, for me personally it was not.
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