GIS programming for Beginners

Introduction to GIS programming (vector)

GIS is a technological field that is use to store, creates, manages, analyzes, and display spatial data along with none spatial data called attributes

Types of spatial data

1. vector data types (points,lines and polygons)
2. raster( Stored in Grid/pixel)

GIS programming

it is writing code or programs that automate (storage,creation,analysis , management and display) of spatial data.

Area of specialization in GIS programming

1. web mapping (web developer)
2. Desktop GIS
3. GIS database

why do i need GIS programming
in most cases existing software and programs was written to ‘fit’ individual needs at that particular time hence some functionalities might not address current problem on hand.

make work easy by automating boring process (combining different process that you could have been waiting and clicking in existing software)
2. flexibility — you can be able to manipulate process to fit your desire
3. safes on time

Best libraries to start with and not limited to
1. Pandas
2. matplotlib
3. geopandas
4. shapely
5. rasterio
6. numpy

Pandas

it is a open source easy to use rich python library desighned for data anallysis
Reading data from Different file types to pandas

1. CSV — → read_csv()
   2. JASON — ->read_json()
   3. HTML — -> READ_HTML()
   4. XML — → read_xml()
   5. SQL — → read_sql()
   6. excel — → read_excel()

Creating a DataFrame and Series

DataFrame is a 2-d (table -like) and is 1–D data structure

import required libraries

import matplotlib.pyplot as plt
import pandas as pd
import geopandas as gpd
import shapely.geometry
import numpy as np
from shapely.geometry import Point,Polygon

Lets create an empty dataframe

data=pd.DataFrame()
print(data)

ways of creating dataframe

Method 1. dictionary of list

coordinates={
    "x":[2,3,4,5,6,],
    "y":[7,8,9,0,11]
}
#DataFrame creation
df=pd.DataFrame(coordinates)
df

Method 2. pandas series

#creating a dataframe from a dictionary of series
x=pd.Series([1,2,3,4,5,6,7,8,9])
y=pd.Series([1,2,3,4,5,6,7,8,9])
#DtataFrame
df1=pd.DataFrame({"x":x,'y':y})
df1

Method 3. list

#creating from list
data=[
    [1,2],
    [2,2],
    [3,4],
    [2,7]
]
df2=pd.DataFrame(data,columns=["x","y"])
df2

Method 4. Dictionaries

#fromlist of dictionaries
data=[
    {"name":"Amos","age":23},
    {"name":"John","age":20}
]
df3=pd.DataFrame(data,index=['a','b'])
df3

exploring numpy functionalities.

creating a function that create random points from a polygon it takes 2 arguments
1. polygon
2. number of random points
polygon has xmin,ymin, xmax,ymax

def randm(poly, number):
    xmin,ymin, xmax,ymax = poly.bounds
  
    x=np.random.uniform(xmin,xmax,number)
  
    y=np.random.uniform(ymin,ymax,number)
    return x,y

polygon = Polygon([[0,0],[0,20],[30,20],[30,-0.5],[0,0]]) #create a polygon using shapely geometry
x,y=randm(polygon,100)
x = x.round(3)
y= y.round(3)

convert random points to list of points each representing location in Cartesian plane. first create an empty dataframe and pass there results

df = pd.DataFrame()
df['geometry'] = list(zip(x,y))
df

pandas dataframe from random points

convert the list of coordinates to Shapely points using the following line of code. then convert pandas dataframe to Geopandas dataframe

df['geometry'] = df['geometry'].apply(Point)

dta=gpd.GeoDataFrame(df)
dta

GeoDataFrame from Pandas DF

plotting

dta.plot()

# plotting specific column and adding legend
#dta.plot(column='column_name',legend=True)

plotting (X,Y) of points

Find code from GitHub KIPYEGON

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