Wrangling the 'Database of health facilities in Nigeria' using QGIS and Python

 A database of health facilities in Nigeria would be a valuable resource for both healthcare providers and the general public. It could provide information about the types of facilities available, their locations, and the services they offer. This information could help healthcare providers refer patients to appropriate facilities, and it could help the public find the nearest and most suitable facilities for their needs.

There are many different types of health facilities in Nigeria, including hospitals, clinics, and pharmacies. Some facilities may specialize in a particular area of healthcare, such as maternal and child health, while others may offer a range of general medical services. The availability and accessibility of these facilities can vary greatly depending on where you are in the country.

Having a comprehensive database of health facilities in Nigeria would allow for better planning and resource allocation by the government and healthcare organizations. It could also help to ensure that underserved areas are not overlooked and that there is an adequate distribution of healthcare resources throughout the country.

In addition to providing information about health facilities, such a database could also include information about the availability of healthcare professionals, including doctors, nurses, and other medical staff. This could be particularly useful for rural areas where access to healthcare professionals may be limited.

Overall, a database of health facilities in Nigeria would be a valuable tool for improving the country's healthcare system and ensuring that all citizens have access to the healthcare services they need.

Python for GIS data wrangling

 Python is a powerful and widely used programming language that is well-suited for GIS data wrangling tasks. There are a number of libraries and tools available in Python that can be used to perform a wide range of GIS data wrangling tasks, including reading and writing various geospatial data formats, performing spatial transformations and projections, and performing spatial analyses and visualizations.

Some of the most commonly used libraries for GIS data wrangling in Python include:

pandas: A powerful data manipulation and analysis library that is widely used for GIS data wrangling. It provides a range of functions for reading and writing geospatial data formats, including CSV, shapefile, KML, and GeoJSON.

geopandas: A library built on top of pandas that provides additional functionality for working with geospatial data. It allows you to manipulate and visualize geospatial data using familiar pandas dataframes, and includes support for geographic projections and spatial operations.

fiona: A library for reading and writing geospatial data formats, including shapefiles, GeoJSON, and KML. It can be used in combination with other libraries, such as geopandas, to perform advanced GIS data wrangling tasks.

shapely: A library for working with geometric objects such as points, lines, and polygons. It provides functions for creating, manipulating, and performing spatial operations on these objects, and can be used to clean and transform geospatial data.

Other useful libraries for GIS data wrangling in Python include rasterio, gdal, and pyproj, among others. These libraries can be used to perform a wide range of GIS data wrangling tasks, including reading and writing various geospatial data formats, performing spatial transformations and projections, and performing spatial analyses and visualizations.

Over the past few years, I have used python to wrangle different kinds of GIS data and some example can be reviewed below:-

1- Python GIS data wrangling - Harris County Appraisal District

2- GIS data wrangling with python - Gridded Street Finder

3- Geocoding and Reverse Geocoding with Python

4- Python GIS data wrangling - Mapping supper eagles head coaches since 1949

5- Python GIS Data Wrangling - U.S. Drought Monitor

6- Python Programming for GIS Data Processing in QGIS

7- GIS data wrangling with python - case study of 'African Journals Online' featured countries

If you need help with wrangling your GIS data, you may contact me via email/phone details on my website:  UmarYusuf.com

Cheers!

Using chatGPT (Generative Pretrained Transformer) to map first 100 regions to welcome the new year

 ChatGPT is a variant of the GPT (Generative Pretrained Transformer) language model that is specifically designed for generating human-like text in a conversational context. It is not a tool for creating maps or visual representations of data.

How do we now use it to make maps?

The answer is simple, we will use ChatGPT to generate text that lists the first 100 regions to welcome the new year by providing the model with a prompt in the form of a natural language question or statement, such as "Can you provide a list of the first 100 regions to welcome the new year?", and then allowing the model to generate the text.

We can also ask ChatGPT to geocode the places for us and that will return the geographic coordinates as seen below;-

Now, we got all we needed to map the first 100 regions to welcome the new year. Copy the chatGPT text into your GIS software of choice for the mapping, of course the text will be formatted appropriately as required be the GIS software you want to use.

As for me I use QGIS software and here is how it looks:-

The New Year is celebrated on January 1st in most countries around the world. However, different countries have different time zones, so the New Year is actually celebrated at different times in different parts of the world.

The new year is celebrated at different times around the world. celebrate the new year is typically Samoa and Christmas Island in Kiribati, which are the first to enter the new year due to their location west of the International Date Line. The last place to celebrate the new year is usually Baker Island and Howland Island, which are the last to enter the new year due to their location east of the International Date Line. However, these locations are uninhabited, so the last place where the new year is celebrated with a significant population is usually American Samoa.

Thank you for following.

Python GIS data wrangling - Harris County Appraisal District

 The task at hand is to prepare a spreadsheet file with links to "Appraisal District Maps in Harris County, Texas".

There twenty two (22) appraisal districts as seen on the facet-maps page above. So, now we need to links to the detailed map in PDF format.

For example, if you open the Houston facet map, you see several facet numbers that leads to the detail maps.

https://public.hcad.org/maps/Houston.asp

If you click on any of the numbers (example 5156A), it should take you to a page that looks like the one below where you will see that the map is subdivided into 12 detailed maps in PDF format.

https://public.hcad.org/cgi-bin/IMap.asp?map=5156A

Now it is the links to the detailed maps in PDF that we need to collect into spreadsheet table.

https://public.hcad.org/iMaps/Tiles/Color/5156A1.pdf

Scrapping the links was not easy as the website is had anti-scrape mechanisms implemented on it. So, we can manually collect the HTML source tags that contain what we wanted then use python scripting to wrangle it into the format we wanted.

For example for each district, the facet numbers are embedded in a '<map>...</map>' tag as seen below.

iMap = ''' <map name="ISDMap">
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        </map> '''

We can use python's beautifulsoup library to extract the facet numbers, then construct the detailed map links since they all follow the same pattern as seen below.

from bs4 import BeautifulSoup

soup = BeautifulSoup(iMap, 'html.parser')
facet_maps = soup.find_all("area")

facet_number = [ f['href'].split('=')[-1] for f in facet_maps ]
detailed_map = [ 'https://public.hcad.org/iMaps/Tiles/Color/' + f['href'].split('=')[-1] + str(a) + '.pdf' for f in facet_maps for a in range(1, 13) ]


appr_dist = 'Houston'
appr_dist_link = 'https://public.hcad.org/maps/Houston.asp'


df = pd.DataFrame([])
# df['Facet Number'] = facet_number
df['Detailed map'] = detailed_map
df['Appraisal Districts'] = appr_dist
df['Link'] = appr_dist_link

The final output is similar to the screen above.

Cheers!

Scrape online academic materials using python

 You know it can be a boring task to manually collect academic material you found online. In this blog post, I will demonstrate how I use python to collect some academic thesis, journals, and other materials for my profession.

Online Scientific Research Journals: 

Here my professor wants to have all the journals and their details published by "Scientific Research and Community Publishers" onlinescientificresearch.com neatly arranged in a spreadsheet table.

The specific details required are the journal name/title, the page URL, the description, cover image and ISSN number.

All the details should be organized in a spreadsheet as seen below.

The code:

import json
import requests
import pandas as pd
from bs4 import BeautifulSoup



# Section 1: Scrape journals page URLs and thumbnail images

url = 'https://www.onlinescientificresearch.com/journals.php'

# Get user-agent from: http://www.useragentstring.com/
headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36'}

response = requests.get(url, headers=headers)
html = response.text

soup = BeautifulSoup(html, 'html.parser')
journals = soup.find_all("div", {'class':'col-12 col-sm-6 col-lg-3'})

print(len(journals))
# ---------------------------------------------------------------

# Section 2: Extract paths to journals URL and thumbnail image...

url_list = []
image_list = []

for j in journals:
    url = j.find('a')['href']
    img = j.find('img')['src']
    
    url_list.append(url)
    image_list.append(img)
    
print('Done...')


# ---------------------------------------------------------------
# Section 3: Create dataframe and construct other details...

df = pd.DataFrame([url_list, image_list]).T
df.columns = ['Journal URL', 'Journal IMAGE URL']
# -------------------------------------
####### Construct Journal Name #######
df['Journal Name'] = df['Journal URL'].apply(lambda row: row.split('/')[-1].replace('.php', '').replace('-', ' ').title())


####### Construct Journal Description #######
def get_journal_descr(url):
    # Get user-agent from: http://www.useragentstring.com/
    headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36'}

    response = requests.get(url, headers=headers)
    html = response.text
    
    soup = BeautifulSoup(html, 'html.parser')
    journal_descr = soup.find("div", {'class':'card-body'})
    
    return journal_descr.text
# -------------------------------------
# Scrape Journal description into a list 
j_descr_list = []
i = 1

for url in df['Journal URL']:
    print(i, 'Processing...', url)
    j_descr = get_journal_descr(url)
    
    j_descr_list.append((url, j_descr))
    i = i+1

desc_df = pd.DataFrame(j_descr_list)
# -------------------------------------

# We have to access each journal url page to get its description...
# df['Journal description'] = df['Journal URL'].apply(lambda url: get_journal_descr(url))
df['Journal description'] = desc_df[1]


####### Construct Journal ISSN #######
# We have to use OCR on the journal thumb nail to get its ISSN...
# Using OCR API at: https://ocr.space/ocrapi....

headers = {
    'apikey': 'helloworld', # 'helloworld'
    'content-type': 'application/x-www-form-urlencoded',
}

issn_list = []

for thumbnail in df['Journal IMAGE URL']:
    print('Processing....', thumbnail)
    
    data = f'isOverlayRequired=true&url={thumbnail}&language=eng'

    response = requests.post('https://api.ocr.space/Parse/Image', headers=headers, data=data, verify=False)

    result = json.loads(response.content.decode()) # Convert the result to dictionary using json.loads() function
    # type(result)

    # Check the dict keys, the ISSN is in: ParsedResults >> 0 >> ParsedText
    issn = result['ParsedResults'][0]['ParsedText'].strip().split('\r\n')[-1]

    issn_list.append(issn)

df['Journal ISSN'] = issn_list

df

Extracting the journal ISSN was definitely the trickiest part as it requires working with OCR API.

M.Sc. in GIST Theses

Master of Science (Geographic Information Science and Technology) Theses by University of Southern California. 

Here our professor wants the thesis details arranged in a table seen above.

Lets start by inspecting the html tags on the web page.

Here I copied the parent div tag that contains the needed data into a local html file. With this we don't need to send request to the website.

import pandas as pd
from bs4 import BeautifulSoup

# Copy the parent div tag into a html/txt file...
html_file = r"C:\Users\Yusuf_08039508010\Documents\Jupyter_Notebook\2022\M.S. IN GIST THESES\M.S. IN GIST THESES.HTML"

# Use BeautifulSoup to read the html div tag....
with open(html_file, encoding='utf-8') as f:
    div_data = f.read()

soup = BeautifulSoup(div_data, 'html.parser')

thesis_years = soup.find_all("h3")

thesis_authors = soup.find_all("strong")
thesis_authors = [ a.text for a in thesis_authors ]

thesis_topics = soup.find_all("em")
thesis_topics = [ t.text for t in thesis_topics ]

thesis_advisor = soup.find_all("p")
thesis_advisor = [ a.text for a in thesis_advisor if 'Advisor:' in a.text ]

thesis_pdf = soup.find_all("a")
thesis_pdf = [ link.get('href') for link in thesis_pdf if 'Abstract Text' not in link.text ]

# --------------------------------------------
df = pd.DataFrame(thesis_authors, columns=['Author'])
df['Topic'] = thesis_topics
df['Advisor'] = thesis_advisor
df['PDF Link'] = thesis_pdf

df

The code below will download the PDF files to local disc using the requests library.

i = 1
for indx, row in df.iterrows():
    link = row['PDF Link']
    print('Processsing...', link)

    pdf_name = str(i) +'_'+ link.split('/')[-1]
    pdf_file = requests.get(link, timeout=10).content

    with open( f'Thesis PDF\\{pdf_name}', 'wb' ) as f:
        f.write(pdf_file)
        
    i += 1
    # break


print('Finished...')

Journal - Nigerian Institution of Surveyors

This was little bit trick because the web page had inconsistent html tags.

import requests
import pandas as pd
from bs4 import BeautifulSoup


url = 'https://nisngr.net/journal/'
response = requests.get(url, verify=False)
html = response.text
# ----------------------------


soup = BeautifulSoup(html, 'html.parser')
div_boxes = soup.find_all("div", {'class':'wpb_text_column wpb_content_element'})
# ----------------------------


papers_dict = {}
for div in div_boxes:
    papers = div.find_all('a')
    
    for link in papers:
        papers_dict[link.text] = link['href']
# ----------------------------

df = pd.DataFrame([papers_dict]).T
df

Thank you for reading.

Automate boring tasks in QGIS with PyQGIS

 In this post, I will use PyQGIS to automate some boring tasks I often encounter in QGIS. Hope you will find something useful to your workflow. Lets get started...

If you don't know what pyqgis is, then read this definition by hatarilabs.com: "PyQGIS is the Python environment inside QGIS with a set of QGIS libraries plus the Python tools with the potential of running other powerful libraries as Pandas, Numpy or Scikit-learn".

PyQGIS allows users to automate workflow and extend QGIS with the use of Python libraries and the documentation can be accessed here.

This means knowledge of python programming is required to understand some of the codes below.

  Task 1~ Count number of opened/loaded layers in the layer panel

I often find myself trying to count the layers in my QGIS project layer panel, so a simple pyqis script to automate the process will be ideal especially when there are many layers on the layer panel to count.

# This will return the all layers on the layer panel
all_layers = QgsProject.instance().mapLayers().values()
print('There are', len(all_layers), 'on the layer panel.')

  Task 2~ Count features in loaded vector layer

In this task, I want to get the number of features in each layer am working on. This is similar to 'Show Feature Count' function when you right-click on a vector layer.

# Get all layers into a list....
all_layers = list(QgsProject.instance().mapLayers().values())

# Get all displayed names of layer and corresponding number of features ...
ftCounts = [ (l.name(), l.featureCount()) for l in all_layers ]
print(ftCounts)

  Task 3~ Switch on/off all layers

To turn ON or OFF all layer can be frustrating when you got many layers to click through. So why not auto mate it in just a click.

# Get list of layers from the layer's panel...
qgis_prjt_lyrs = QgsProject.instance().layerTreeRoot().findLayers()

# Use index to Set layer on or off....
qgis_prjt_lyrs[20].setItemVisibilityChecked(True) # True=On, False=Off

# Do for all...
for l in qgis_prjt_lyrs:
    l.setItemVisibilityChecked(False)

  Task 4~ Identify layers that are on/off

Lets extend task3 above, so we know which layers are on (visible) and which layers are off (hidden).

# Get list of layers from the layer's panel...
qgis_prjt_lyrs = QgsProject.instance().layerTreeRoot().findLayers()

# Check if a layer is visible or not...
layer_visibility_check = [ (l.name(), l.isVisible()) for l in qgis_prjt_lyrs ]
print(layer_visibility_check)

visibility_ture = [ l.name() for l in qgis_prjt_lyrs if l.isVisible() == True ]
print('Number of visible layers:', len(visibility_ture))

visibility_false = [ l.name() for l in qgis_prjt_lyrs if l.isVisible() == False ]
print('Number of visible layers:', len(visibility_false))

  Task 5~ Read file path of layers

This is useful when you have many layers and don't know where they are located on your machine. You will also see interesting paths to other remote layer such as WMS, etc

# Returns path to every layer...
layer_paths = [layer.source() for layer in QgsProject.instance().mapLayers().values()]
print(layer_paths)

  Task 6~ Read layer type of layers

We can check the 'type' of a layer.

# Get dict of layers from the layer's panel...
layersDict = QgsProject.instance().mapLayers()


for (id, map) in layersDict.items():
    print(map.name(), '>>', map.type())

  Task 7~ Create multiple attribute fields/columns

Lets say we want to add multiple integer fields/columns to a vector layer. The code below will create attribute fields for year 2000 to 2023, that is twenty three (23) attribute columns/fields on the selected vector layer.

# Get Layer by name...
layer = QgsProject.instance().mapLayersByName("NIG LGA")[0]

# Define dataProvider for layer
layer_provider = layer.dataProvider()

# Add an Integer attribute field and update fields...
layer_provider.addAttributes([QgsField("2000", QVariant.Int)])
layer.updateFields()

# Add bulk attribute fields...
for x in range(2001, 2023):
    layer_provider.addAttributes([QgsField(str(x), QVariant.Int)])
    layer.updateFields()

print('Done...')

  Task 8~ Read/List all names of layers on layer panel

Here we just want to return the displayed names of layers.

# Get all layers into a list....
all_layers = list(QgsProject.instance().mapLayers().values())

# Get all displayed names of layer
all_layers_names = [ l.name() for l in all_layers ]
print(all_layers_names)

  Task 9~ Save attribute table to dataframe

# Save attribute table into Dataframe...

import pandas as pd

# Get Layer by name...
layer = QgsProject.instance().mapLayersByName("NIG LGA")[0]

# get attribute columns names
col_names = [ field.name() for field in layer.fields() ]

lga_list = []
state_list = []
apc_list = []
pdp_list = []
lp_list = []
nnpp_list = []
winner_list = []


for feature in layer.getFeatures():
    lga_list.append(feature['lga_name'])
    state_list.append(feature['state_name'])
    apc_list.append(feature['APC'])
    pdp_list.append(feature['PDP'])
    lp_list.append(feature['LP'])
    nnpp_list.append(feature['NNPP'])
    winner_list.append(feature['Winner'])

df = pd.DataFrame([state_list, lga_list, apc_list, pdp_list, lp_list, nnpp_list, winner_list]).T

df.to_csv(r'C:\Users\Yusuf_08039508010\Desktop\...\test.csv')

print('Done....')

  Task 10~ Select from multiple layers and attribute fields

Here we want to conduct multiple selection of given keywords from all listed layers and all attribute fields.

# Query to Select from all listed layers and all attribute fields
search_for = {'Bauchi', 'SSZ', 'Edo', 'Yobe'}

for lyr in QgsProject.instance().mapLayers().values():
    if isinstance(lyr, QgsVectorLayer):
        to_select = []
        # fieldlist = [f.name() for f in lyr.fields()]
        for f in lyr.getFeatures():
            # Check if any of the search keyword intersects to
            # feature's row attribute. If true, get the feature ID for selection...
            if len(search_for.intersection(f.attributes())) > 0:
                to_select.append(f.id())
        if len(to_select) > 0:
            lyr.select(to_select)

  Task 11~ Convert multiple GeoJSON files to shapefiles

import glob

input_files = glob.glob(r'C:\Users\Yusuf_08039508010\Desktop\Working_Files\GIS Data\US Zip Codes\*.json')
for f in input_files:
    out_filename = f.split('\\')[-1].split('.')[0]
    input_file = QgsVectorLayer(f, "polygon", "ogr")
    
    if input_file.isValid() == True:
        QgsVectorFileWriter.writeAsVectorFormat(input_file, rf"C:\Users\Yusuf_08039508010\Desktop\Working_Files\Fiverr\2021\05-May\Division_Region_Area Map\SHP\US ZipCode\{out_filename}.shp", "UTF-8", input_file.crs(), "ESRI Shapefile")
    else:
        print(f, 'is not a valid input file')
        
print('Done Processing..., ', f)

Thank you for reading.

Search nearby places - Comparing three API (Google Places API, Geoapify API and HERE API)

 In the post, I will compare API from three different providers to search nearby places the three API to compare are: Google Places API, Geoapify API and HERE API.

For each of the platforms, you need to register and get a developer API key to use. All the platform offer a limited free API quota to start with.

Google Places API

import requests
import pandas as pd
from datetime import datetime

df = pd.read_csv('datafile.csv')


YOUR_API_KEY = 'AIza......'

i = 1
for row, col in df.iterrows():
    lat = col['Latitude']
    long = col['Longitude']
    print(i, 'Processing...', lat, long)
    
    url = f'https://maps.googleapis.com/maps/api/place/nearbysearch/json?location={lat}%2C{long}&radius=4850&type=laundry&keyword=laundromats&key={YOUR_API_KEY}'

    payload={}
    headers = {}

    response = requests.request("GET", url, headers=headers, data=payload)

    # Get current time...
    now = datetime.now()
    current_time = now.strftime("%Y%m%d__%H%M%S")

    # Write to file....
    with open(fr'JSON folder\\GoogleAPI\\{state_folder}\\{current_time}.json', 'w') as outfile:
        json.dump(response.json(), outfile)

    i = i+1
    
    
print('Done...')

Geoapify API

GeoApify_API_KEY = '378122b08....'

url = 'https://api.geoapify.com/v2/places'

params = dict(
    categories: 'commercial',
    filter: 'rect:7.735282,48.586797,7.756289,48.574457',
    limit: 2000,
    apiKey=f'{GeoApify_API_KEY}'
)

resp = requests.get(url=url, params=params)
data = resp.json()

print(data)

HERE API

HERE_API_KEY = 'WEYn....'
coord = '27.95034271398129,-82.45670935632066' # lat, long
url = f'https://places.ls.hereapi.com/places/v1/discover/here?apiKey={HERE_API_KEY}&at={coord}&laundry'

response = requests.get(url).json()
# print(response.text)

# Get current time...
now = datetime.now()
current_time = now.strftime("%Y%m%d__%H%M%S")


# Write to file....
with open(fr'JSON folder\\{current_time}.json', 'w') as outfile:
    json.dump(response, outfile)
    
print('Done...')

Making an Unofficial Atlas Map of Nigeria General Election

Unofficial Atlas Map of Nigeria Presidential Election Results

Step-by-step guide

Atlas maps