Thursday, October 24, 2019

Generating Zip code map download links

Overview
For each of the US states zip code map listed on this page, we want to construct the map download link.

There several ways to get this task completed including using selenium or request/beatifulsoup modules. However, in this exercise, we are going to keep it simple and assume that we know the pattern at which the maps download links are made-up (indeed the pattern is same for all states and readily known) and we just need to generate them based on the file names.

The download link pattern is: URL + state_name + -zip-code-map.png
Examples:-
https://www.unitedstateszipcodes.org/maps/alabama-zip-code-map.png, 
https://www.unitedstateszipcodes.org/maps/alaska-zip-code-map.png, 
https://www.unitedstateszipcodes.org/maps/arizona-zip-code-map.png, 
e.t.c

So, we can easily construct each state's download link from their respective names...



Objectives
At the end of this tutorial, you should become familiar with:-
a) To become familiar with using string concatenation
b) To become familiar with using string split(), replace() and lower() methods
c) To become familiar with convert pandas series to list using tolist() method
d) To be able to use for loop to append string to empty list



Pseudocode
a) Read the spreadsheet containing the file names into a list
b) Clean the names to remove unwanted characters such as ' ('
c) Concatenate the strings to form the URLs



Code Snippet

import pandas as pd

# Read the spreadsheet file...
zip_df = pd.read_csv(r"C:\Users\Yusuf_08039508010\Desktop\GIS Data Processing Scripts\US_ZipMap_Size.csv")

# Convert the column to a list...
zip_list = zip_df['Maps'].tolist()


# ----------------------
# For each item in the list, split at ' (' and keep the first part...
name_list = []
for item in zip_list:
    name_list.append(item.split(' (')[0])


# ----------------------
# Download link URL is: 'https://www.unitedstateszipcodes.org/maps/' + stateName + '-zip-code-map.png'
download_link = []
for name in name_list:
    download_link.append('https://www.unitedstateszipcodes.org/maps/' + name.replace(' ', '-').lower() + '.png')

download_link




Explanation

Step 1: First we import pandas module and read the spreadsheet file into a dataframe.

import pandas as pd

# Read the spreadsheet file...
zip_df = pd.read_csv(r"C:\Users\Yusuf_08039508010\Desktop\GIS Data Processing Scripts\US_ZipMap_Size.csv")

# Convert the column to a list...
zip_list = zip_df['Maps'].tolist()

Step 2: Next, we need to split the sting and keep the useful part. The part needed is that before the ' (' character. Note that the character has a space followed by the open brace/parentheses.

# ----------------------
# For each item in the list, split at ' (' and keep the first part...
name_list = []
for item in zip_list:
    name_list.append(item.split(' (')[0])

Step 3: The last step is to replace spaces within the string by '-' and concatenate the url string to the variable string. The first part of the string is: 'https://www.unitedstateszipcodes.org/maps/' while the end part of the string is: '.png'

download_link = []
for name in name_list:
    download_link.append('https://www.unitedstateszipcodes.org/maps/' + name.replace(' ', '-').lower() + '.png')



Assignment Takeaway
An exercise to help you learn further is: Write a script that will extend the above script by adding the resulting list to a new column that corresponds to the file names as seen below, then save the result to spreadsheet file. The result will look like this:-




Reference Material

1] https://www.programiz.com/python-programming/methods/string/split
2] https://www.w3schools.com/python/ref_string_split.asp
3] https://www.programiz.com/python-programming/methods/string/replace

Monday, October 21, 2019

Building SQL Expressions in ArcGIS and QGIS

In the modern GIS industry, programming skill is an essential asset and one of the programming languages the is very popular within the industry is Structure Query Language (SQL) as you will later find out how it is been used in few moments.

Most of the query expressions used in ArcGIS or QGIS desktop software are derived from SQL. SQL is a standard language for storing, manipulating and retrieving data in databases.

Both ArcGIS and QGIS support the following common database engines: SQLite, MySQL, SQL Server, MS Access, Oracle, Sybase, Informix, PostgreSQL, and other database systems. When connected to any of them, you can take full advantage of SQL in GIS.

For small GIS projects where database isn't required, we make use of traditional GIS files type such as "Shapefile" which allow us to use query expressions that adhere to standard SQL expressions.


What is an expressions?
An expressions is a combination of "Constants, Variables, Operators and Functions" organized in an ordered statement that returns an output value. Expressions are unique to the computer language they are created in. An example of SQL expressions is: "SELECT * FROM <Layer_name> WHERE <Field_name> <Operator> <Value or String>".

If you data resides in a shapefiles or any of these (coverages, INFO tables, and dBASE tables), this part of the SQL expression (SELECT * FROM <Layer_name> WHERE) is automatically supplied for you, so you only provide this part (<Field_name> <Operator> <Value or String>) to query your data.

Since GIS data is made up of Spatial and Attribute, it is worth noting that 'Attribute Query' is similar to the standard SQL queries found in traditional database systems and this will be our focus in this article. On the other hand, 'Spatial Query' which allow operations such as "Contains, Equals, Intersects, Is Disjoint, Overlaps, Touches, Within and Crosses" requires some extension installed on traditional database systems to make them work.


Building SQL Expressions for Attribute Query

There are many places you can build expressions within both ArcGIS and QGIS software, some of the common places are listed below:-

ArcGIS Tools:
~ Select By Attribute
~ Definition Query
~ Field Calculator
~ Label Expression Dialog Box
~ Add Query Layer



QGIS Tools:
~ Select By Expression
~ Filter Query Builder
~ Field Calculator
~ Label Expression Dialog Box
~ Layer Property Display
~ Database Manager


Wednesday, October 9, 2019

Calculating the total size of zip code maps


On this US printable zip codes maps page, there is a list of all the US states zip code maps with their respective sizes in braces like this "Alabama ZIP Code Map (3.59MB)" as seen below...



Lets calculate the total size of all the maps using python scripting!

Off course, there are several or even better ways to get this done. But here we want to test our python skills on this, let us stick to using python 😏.

Some other reasons it is good idea we use python is that we can easily use our python skill to:-
1) make HTTP request to scrape/download the map data
2) generate the download links on the fly
3) create a bot to monitor change in map size (which could indicate the map has been updated).
4) visualization of the string including map/geographic visualization.

The list can go on and on, but I will keep it simple here to just calculate the total sum the map sizes.

Step 1:
First thing is to get the string/text off the web page into our python environment. There are several ways to do this as I have mentioned above, but I will just select, copy and paste it in a CSV file as seen below.



Step 2:
Read the CSV file in python. Here I will use the pandas module to read the CSV file, could have also used the CSV module to do this.



Monday, October 7, 2019

Filtering Missing Zip codes out of master Zip codes list

Here I have a list of zip codes, I want to know the missing zip code from the given list (these are the postal code in Texas, USA).




List 'available_zipcodes' contains the master zip codes and list 'given_zipcodes' contain the provided or working zip codes. Now I want check and filter out those zipcode that are NOT in the master zip codes.

These three lines of python code below will do it. It uses the 'for' loop with and 'if' statement. Basically, we loop through the list of 'given_zipcodes' and if it is not in the 'available_zipcodes', then we print it out.




If you care to run the script and don't want to type all that out, here below is the Code is...

available_zipcodes = [77389, 77086, 77346, 77018, 77040, 77388, 77065, 77080, 77041, 77396, 77385, 77354, 77382, 77067, 77066, 77090, 77345, 77355, 77373, 77339, 77043, 77302, 77304, 77070, 77375, 77095, 77433, 77069, 77038, 77091, 77380, 77092, 77316, 77429, 77377, 77379, 77064, 77088, 77338, 77449, 77386, 77381, 77493, 77356, 77068, 77014, 77084, 77055, 77301, 77303, 77384]

given_zipcodes = [77325, 77339, 77345, 77346, 77380, 77381, 77382, 77383, 77384, 77385, 77386, 77301, 77302, 77303, 77304, 77316, 77354, 77356, 77389, 77014, 77018, 77038, 77040, 77041, 77043, 77055, 77064, 77065, 77066, 77067, 77068, 77069, 77070, 77080, 77084, 77086, 77088, 77090, 77091, 77092, 77095, 77375, 77377, 77379, 77388, 77429, 77433, 77449, 77493, 77373, 77338, 77347, 77391, 77396, 77355]


for zipcode in given_zipcodes:
    if zipcode not in available_zipcodes:
        print(zipcode)

In the case above, the missing zip codes are: 77325, 77383, 77347, 77391

Note: In a production job. these zip codes will probably come in a text file, just read the file into python lists and loop through as seen above.

That is it!

Tuesday, September 24, 2019

Limitations of a Shapefile

For along time, shapefile has being my primary GIS file for working with vector data. I have never had any reason to look beyond shapefile for handling my GIS vector datasets not until recently when I have a need to store some large quantity of text string in the attribute table.

Before I share my story, let make a point to what a shapefile is just in case you don't know it.

Shapefile is a file type developed by ESRI to handle vector map data in the form of points, polylines and polygons. More details can be found on the Wikipedia page as summarized in the picture below, also on the 'Shapefile Technical Description' document.



Limitations of a Shapefile
Specifically, I was trying to convert a KML file to shapefile. Then one of the columns that had alot of text/string content gets truncated when converted to shapefile. I couldn't figure out why and what caused that until I found this website (Switch from Shapefile) that listed listed some its limitations and that one that affected my situation directly was that the maximum characters is 254.


No way! My attribute table has way more than 254 characters. Then I had to look beyond a shapefile. I actually settled with a GeoJSON file type.

Once again as listed on the website Switch from Shapefile, other limitations include:-
~ No coordinate reference system definition.
~ It's a multifile format.
~ Attribute names are limited to 10 characters.
~ Only 255 attributes. The DBF file does not allow you to store more then 255 attribute fields.
~ Limited data types. Data types are limited to float, integer, date and text with a maximum 254 characters.
~ Unknown character set. There is no way to specify the character set used in the database.
~ It's limited to 2GB of file size. Although some tools are able to surpass this limit, they can never exceed 4GB of data.
~ No topology in the data. There is no way to describe topological relations in the format.
~ Single geometry type per file. There is no way to save mixed geometry features.
~ More complicated data structures are impossible to save. It's a "flat table" format.
~ There is no way to store 3D data with textures or appearances such as material definitions. There is also no way to store solids or parametric objects.
~ Projections definition. They are incompatible or missing.
~ Line and polygon geometry type, single or multipart, cannot be reliably determined at the layer level, it must be determined at the individual feature level.


Now you know some troubles you may encounter with you shapefile data are due to some of these limitations, so no need to full your hair just switch to a more advanced GIS file type.

Thursday, September 19, 2019

QGIS Calculate the Mid Coordinates of Polygons

In QGIS field calculator, you can calculate the center point of all polygons within a polygon layer.

Formula 1:
x($geometry), y($geometry)

Formula 2:
xmin(centroid($geometry)), ymin(centroid($geometry))

Formula 3:
x(centroid($geometry)), y(centroid($geometry))


Note that: 'x' standards for Longitude while 'y' standards for Latitude. $geometry represent the variable polygon geometry.


As you can see the preview result for the three formulas are the same.

Sunday, September 1, 2019

Map from GIS to CAD

Introduction

No doubt, on the desktop ESRI ArcGIS is the top GIS software while AutoDesk AutoCAD is the top CAD software.

Both are capable of making maps and in this article, I will demo how to convert existing map in ArcGIS to AutoCAD. But before I go into that, lets get to know what GIS and CAD mean.



What is GIS and CAD?

GIS = Geographic Information System
CAD = Computer Aided Design



What is the Difference between GIS and CAD?

Both GIS and CAD can be used for making maps however, they are very different technologies with different applications.

GIS: analyzing/visualizing map data
CAD: creating/editing accurate map data

GIS allows data to be attached to the points, lines, and polygons used in the map. This makes GIS the best tool for analyzing and visualizing data through the use of a map.

CAD easily allows a user to create a very accurate drawing whether it is a map, site plan, profile etc. CAD allows the drawing of maps by the use of coordinates or through distances/bearings in different types of unit.


Map displayed in ArcGIS



Map displayed in AutoCAD




How to converting map data from GIS to CAD and vice versa

GIS to CAD:
In ArcGIS, you use the command at: ArcToolBox >> ConversionTools >> To CAD to concert map layer to CAD.





CAD to GIS:
In AutoCAD you simply save the map as .dxf or .dwg file to have it usable in GIS.




That is it!

Wednesday, August 28, 2019

QGIS Remove Black Background Boarder from Raster Image


Often times, you are left with black boarder around an image you manipulated in QGIS as seen below. This is often cause because there is no data to display around data part of the image.



Here is how to get ride of the black background in QGIS 3.

Open the raster image layer property window and select the 'Transparency' tab. Then enter '0' under No data value >> Additional no data value.



Click 'Ok' to apply the changes. Your raster image should now have no black background color surrounding it as seen below.




That is it.

Monday, August 26, 2019

Get the row count of multiple excel spreadsheet files

Here I have many excel spreadsheet files within a folder as seen below...


The task is to return the number of rows in each of the excel files. I can go manually, open each file, scroll to the bottom and note down the row number. That will be cumbersome and time consuming given that number of files I have to cover.

So, I have to write a simple script in python that will handle this boring task accordingly as follow:-

Step 1: First things first, lets find a way to read all the .xlsx files. Here I used the glob module to handle this.

import glob

folder_xlsx = r"C:\Users\Yusuf_08039508010\Desktop\my-xlsx-folder"

# read all the individual order xlsx files
xlsx_files = glob.glob(folder_xlsx + '/*.xlsx')
what I have above is a list that contains path to all the excel files in the folder. Lets move on...


Step 2: Next step is to read each excel file into a pandas dataframe and use a function to count the number of rows in the dataframes. There are many functions to count the number of rows as seen below, but I will use this function 'len(df.index)'.


Here is the solution for the fisrt dataframe.

df = pd.read_excel(xlsx_files[0])

row_count = len(df.index)

To do for the whole excel files, we just write a for loop and save the into a list as seen below. Noticed that I used rsplit() function to get the file names to print it along its corresponding row count.

import pandas as pd
row_count_list = []
for xls_file in xlsx_files:
    df = pd.read_excel(xls_file)
    row_count = len(df.index)
    
    file_name = xls_file.rsplit('\\', 1)[1]
    
    file_details = file_name, row_count
    
    row_count_list.append(file_details)
    
print (row_count_list)



That is it!


P.S: You could easily extend the script above to do many other thing with the files. An example will be to merge all the files into one file using the pandas concat() method. So, instead of appending the file names and the row counts, we will simply append the dataframe as seen below.

df_list = []
for xls_file in xlsx_files:
    df = pd.read_excel(xls_file)
    
    df_list.append(df)
    
merge_df = pd.concat(df_list)

Thursday, August 8, 2019

Split string at the last occurrence of a string


I have a list of strings with varying length. However, the each string always end with certain same information (country in this case) as seen below.


data_list = ['Adams Smith, white, UK', 
             'Samuel Tom, Black, 29 leen st. NY, USA', 
             'Yaks Ramson, New Student, Yet to register, Romania']
    

As you can see, there are three items in the list and each item ends with a country name after a comma (,) sign.

When you loop through the items, you can split each item by comma like this: item.split(','). However, this isn't what I wanted, I want to split just at the last comma. In other words, I want to plit each of the string at the last occurrence of the comma (,) sign.

So, here the solution is to use a list method call rsplit(',', 1), which accept a second argument that tells how many times you want to split a string. Here I want to split the string just once, so my script will look like this...

data_list = ['Adams Smith, white, UK', 
             'Samuel Tom, Black, 29 leen st. NY, USA', 
             'Yaks Ramson, New Student, Yet to register, Romania']

item_list = []
for item in data_list:
    item_1 = item.rsplit(',', 1), # Not item.split(',')
    
    item_list.append(item_1)

Now, each item is split into two and you can access the individual countries as seen below:-