Map of 2018 Ekiti State Gubernatorial Election Final Result

The much anticipated 2018 Ekiti state gubernatorial election has come to pass and 24 hours after the result has been announced by INEC, Nigeria social and news medias are flooded with the result.

Unfortunately, non has analysed the result on a map. In this post, I will related the result to map of Ekiti state so we can see the spatial relationship in the result.

Ekiti state has 16 local governments and 177 electoral wards.

Geo-visualization of FIFA 2018 World Cup

As stated on Wikipedia page: The 2018 FIFA World Cup is the 21st FIFA World Cup, a quadrennial international football tournament contested by the men's national teams of the member associations of FIFA. It is currently ongoing in Russia starting from 14 June and will end with the final match on 15 July 2018.

In this post, I will attempt to visualize the participants on a world map starting from qualifying countries, group stage, round of 16, quarter finals, semi finals etc.


Let's get started....

Python and Data Science Blogs

The list below are some of my favorite blogs I have followed and gained alot of from over the years. I hope you will also like them.

Some useful blogs for python and data science enthusiasts:-

~ dataquest.io

~ datasciencelearner.com

~ datacamp.com

~ dataschool.io

~ medium.com

~ dataelixir.com

~ towardsdatascience.com

~ stackabuse.com

~ ImportPython.com

~ pyimagesearch.com

~ elitedatascience.com

~ pythonforengineers.com

~ flowingdata.com

~ kdnuggets.com

~ quora.com

Geo-spatial Professional Areas of Interests

The Geospatial industry is quite a vast industry that encompasses many areas of interest. In this article, I will briefly take a look at the most common areas.

As someone who works in the Geospatial industry, you will find yourself doing one or more combinations of this areas of interests:-

Modern Map of Ancient Kano City Gates (Kofa)

Kano City Gates - Kofofin Garin Kano

The amazing structure (the ancient walls of Kano city, Nigeria) was built during the reign of King Kijimazu (Sarki Gijimasu) between 1095 through 1134 and completed in the middle of the 14th century. They are ancient defensive walls built to protect the inhabitants of the ancient city of Kano and were described as "'the most impressive monument in West Africa".

Below is a copy of a Map produce in 2004 by 'Geography Dept, Bayero University, Kano (BUK)' showing the accient Wall and Gates.

The ancient wall is a 14km radius earth structure that surrounds the Dala Hills, Kurmi Market and Emir's Palace located in the heart of the modern day city of Kano as seen as the darkest spot on the map below.

The Ancient Kano City Walls originally had an estimated height of 30 to 50ft and about 40ft thick at the base with 15 gates (kofa) around it.

The names of the gates are:-

1~ Kofar Kansakali
2~ Kofar Kabuga
3~ Kofar Gadonkaya
4~ Kofar Dukawuya
5~ Kofar Waika/Adama
6~ Kofar Wambai
7~ Kofar Mazugal
8~ Kofar Mata
9~ Kofar Nassrawa
10~ Kofar Dan Agundi
11~ Kofar Na'isa
12~ Kofar Ruwa

13~ Kofar Fampo
14~ Sabuwar Kofa

15~ Kofar Dawanau

In todays modern Kano city, the gates are all that is left. Ninety percent of the walls have since disappeared, they are either gone or crumbling, very soon there will be nothing left of it and history of the walls would be erased completely.

Most of the gates have been rehabilitated and rebuild by the past Kano state government.

Adeyemi and Bappah coducted field survey in 2010 and obtained the following "Summary of Wall Measurements and Observations" below as published in their research paper.

Source: Conservation of Kano Ancient City Wall and Gates: Problems and Prospects in Nigeria, (2010)

Street-level Map of Nigeria Cities

Most at time it is almost impossible to find an updated and editable version of Nigeria city map at street-level that shows details such: Buildings, Landuse, Natural features, Places, Places of worships, Railways, Roads/Streets, Traffic, Transport stations, Waterways etc.

Such detailed maps are great for many task including for city planning. In this post, I present to you a street-level map data at scale for all major cities in the country.



Lagos City, Lagos (State) - Badagry, Epe, Ikeja, Ikorodu, Lagos, Mushin, Shomolu

Eastern Cities, Enugu (State) - Enugu, Nsukka | Anambra (State) - Awka, Onitsha | Imo (State) - Owerri | Abia (State) - Aba, Arochukwu, Umuahia

Unable to parse KML file in python 3 with PyKML module

Introduction

Recently, I picked up a project where I had to read/parse in a point KML file and do reverse geocoding on the latitudes and longitudes coordinates of the points. So, I found a nice python module that will help me accomplish this. But I found out that it doesn't work fine on python 3 installation but works great on python 2.

When I import pyKML as follow "from pykml import parser" in python3, it returns the error as thus: ModuleNotFoundError: No module named 'urllib2'

I knew the pyKML module was trying to work with 'urllib2' module which has changed in python3. Since python2 will soon be discontinued, I have to find a solution for it to work on python3.

Ok, just in case you don't know pyKML, according to the documentation, pyKML is a Python package for creating, parsing, manipulating, and validating KML documents/files. A KML is language for encoding and annotating geographic data used by Google Earth, Google Maps and a number of other GIS platforms.

Find the Elevation of any Location

According to WikiPedia: the elevation of a geographic location is its height above or below a fixed reference point, most commonly a reference geoid, a mathematical model of the Earth's sea level as an equipotential gravitational surface.

In the geo-spatial industry, getting and working with the elevations of points is a very common task for many obvious reasons. Elevation value of points can be obtained through various techniques such as land surveying and GPS observations.

Using the techniques above, there will be a challenge when you need to obtain the points that are difficult to access. In such situations, you will have to consider a remote service to get such elevation values. One of such remote services that provide access to elevation data we will discuss in this blog post is: Open-Elevation.com

Jupyter Notebook Python user input() function

Jupyter notebook has added a feature I have always wished it had long ago. Before now, writing a script that accepts input from user with the input() function in python3 or raw_ input() function in python2 was almost impossible and you have to switch between the terminal window and the jupyter notebook all the time.

The newer version of jupyter notebook has made available a text-box right inside the browser whenever you call the input() function as seen below;-

Enjoy the new GUI feature of the jupyter notebook.

Scrape data from Google maps

Scrapping data from Google maps made by third party

Often, you will find Google maps published by someone you don't have access to and you really want to extract some data from the map.

Lets consider this map below, assuming it was published by someone you don't know and you have to have access to the raw data behind the map.

When you click on individual features on the map, the required data of that point is displayed on the left side panel. Now, the task is to extract or collect those data for every point feature on the map into a spreadsheet for further use/consumption.

Step to extract the data

Here is how to go about extracting such dataset.

Step 1: On the main page, click on the three vertical dots to expand the menu

Step 2: Select "Download KML" to download a file that can be read by Google Earth software, text editor and other GIS software.

Step 3: Open this downloaded KML file in any software of your choice to access the data contained there in. In my case, I used QGIS to open the kml file and exported the content into a CSV/Excel spreadsheet.

That is it!
Now if you come across any Google maps that you want to access the data used in preparing the map, that is how to go about accessing it. Enjoy!