Welcome to the Create Smart Maps in Python and Leaflet Tutorial Series. In this tutorial series we will be building a GIS application from scratch using a variety of open source technologies. The purpose of this tutorial and many more to follow, is to take geospatial analytics and convert it into a functional application.
We will be powering our application with a PostgreSQL and PostGIS database. In the front-end we'll use Bootstrap, JavaScript and Ajax. On the server side we'll be using Python 3 Django combined with the use of scientific libraries like pandas, for our data transformation and conversion operations. The operating system that we will be working on is Ubuntu Linux 16.04.
At a later stage we'll be using time series forecasting to predict the consumption values for the following month using our historical data.
You can watch the Create Smart Maps in Python and Leaflet - Introduction Video here:
Create Smart Map In Python Tutorial Series - Introduction (Video)
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| Bubble plot on open street map |
Let's go over some important technical concepts mentioned in this series
GIS (Geographic Information Systems), Geospatial
A geographic information system (GIS) is a framework for gathering, managing, and analyzing data. Rooted in the science of geography, GIS integrates many types of data. It analyzes spatial location and organizes layers of information into visualizations using maps and 3D scenes. With this unique capability, GIS reveals deeper insights into data, such as patterns, relationships, and situations—helping users make smarter decisions.
Source: https://www.esri.com/en-us/what-is-gis/overview
Geodjango
GeoDjango intends to be a world-class geographic Web framework. Its goal is to make it as easy as possible to build GIS Web applications and harness the power of spatially enabled data.
Source: https://docs.djangoproject.com/en/2.0/ref/contrib/gis
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| Anatomy of A GIS Application |
Time Series Forecasting
Making predictions about the future is called extrapolation in the classical statistical handling of time series data.
More modern fields focus on the topic and refer to it as time series forecasting.
Forecasting involves taking models fit on historical data and using them to predict future observations.
Descriptive models can borrow for the future (i.e. to smooth or remove noise), they only seek to best describe the data.
An important distinction in forecasting is that the future is completely unavailable and must only be estimated from what has already happened.
Source: https://machinelearningmastery.com/time-series-forecasting
Conclusion
That is about all we need to cover for the introduction to this series. I hope you will enjoy it and it will add some value to the projects you are currently busy with.
Stay tuned for the next tutorial in the Create Smart Map Tutorial Series where we will be covering how to Install and Configure PostgreSQL and PostGIS.
