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Discover the power of location data to build effective, intelligent
data models with Geospatial ecosystems Key Features 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 Book
DescriptionData 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. 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 Who this book is forData
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.
Recently many governments embraced electronic means to engage with
citizens in service provision and interacting with their citizens.
Therefore the study of E-government has gained rather considerable
attention. Unfortunately many of the E-government studies focused
on the supply side. this study has tried to breach that gap by
focusing on the demand side of the E-government. E-grievance
systems is the main theme of this book because it is one of the
main reasons citizens contact to their governments. in fact, more
people complain or suggest improvements to their governments than
telling them "job well done." Therefore, it is considered essential
that processes of complaint redresal are clearly defined and
publicly available. but little has been discussed about the
development of an integrated system to handle citizen's electronic
complaints( E-grievance) and its consequences which will be
elaborated in this book. This book will be beneficial to public
administrators, local government workers and E-government
researchers.
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