Saturday, November 26, 2016

Advantages of GeoDatabase over Shapefile

Both GeoDatabase and Shapefile are excellent storage containers for Geospatial datasets. However, the former (geodatabase) has more robust advantages when handling big spatial data over the later (Shapefile).

Here are some of the reasons why you should move to using geodatabase asap when dealing with very big data:-

~ Allow multiple users management and privileges on big geospatial datasets

~ One-to-Many and Many-to-Many relationship between geodata sets.

~ A shapefile isn’t made of a single file. Shapefiles are quite unhandy when working with many of them very often. Four files at least makes a valid shape file (.shp, .dbf, .shx and .prj).

~ Shapefiles are binary files that can't be edited by hand using any text editor. They most be viewed and edited in a GIS software, that is not the case with Geodatabases (SpatiaLite and GeoJSON). You can edit them by hand (using text editor), rather than using GIS software, if you’re so need to do so.

~ Long attribute fileds/column names like “nig_geodb_majortrunck_RoadNetwork” can't be used as 10 characters at maximum is allowed for field name.

~ The maximum file size for shapefile (.shp / .dbf) is 2GB. With a geodatabase, you are not limited by the file size.

~ There are no much support for table field type. Numeric attributes are stored as characters (integers/floats), causing potential problems with rounding etc.

~ NULL values are interpreted differently between systems


Example to clarify the points above


Go to NaturalEarthData.com and download all vector geographical themes of the world as SHAPEFILE and GeoDataBase.

You will easily see that because the dataset contains over 100+ layer/themes, the GeoDB is easier to hand as it holds all the layers in one single file while the shapefile option has at least three files for each of the 100+ layers to deal with.



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Tuesday, November 15, 2016

My Favorite GIS Blogs

Hello,

Today I want to share with you a list of "My Favorite GIS Blogs".

These are the GIS online blogs you should definitely keep your eyes on for getting latest up to date trends in the ever changing Geospatial industry...

These are my personal favorite GIS and related blogs:-


Be free to add anyone you feel is missing from the list in the comment section below.


Thanks for following the post.

Wednesday, November 9, 2016

Python List Comprehension Simplified

Hello,

This is going to be as simple introduction to Python List Comprehension.

A "list comprehension" in python provides a concise way to create lists. Basically, it nests 'expression', 'loops' and 'conditions' within a list on a single line. The basic structure of the python's list comprehension is like this: [*expression*  *for object in loop*  *if condition*] - without the "*" of-course.


Example


Assuming we have a list called "my_list" and we want to get the length of individual elements within the list, a 'for' loop can do it as follow:-


When you run above code it will output the result as: 4 10 2 5 1 2

Now lets use "list comprehension" to achieve the same result as above:-


The result returns a list of the lengths like so: [4, 10, 2, 5, 1, 2]

Note that we didn't add a condition to the list comprehension above. len(item): is the expression value and for item in my_list: is the loop part of the "list comprehension". We will add a condition in a moment.

List comprehension has two quick noticeable advantages over using the "for" loop namely:-
1) the code is shorter in length.
2) the result is in an object list instead of individual numbers as obtained using "for" loop.

To add a condition, we simply add an "if" statement to the end of the "list comprehension" as follow;-
Lets add a condition to display only length of items greater than four: if len(item) > 4. Add that syntax to the end of the "list comprehension".


The result returns a list of the lengths greater than four like so: [10, 5]


Conclusion


Even though "list comprehension" may sound very nice to use it has a little draw back in the sense that it can easily make your code very difficult to read in the nearest feature.

Imagine a situation where you compressed hundreds lines of code using "list comprehension" into a few lines. It become unreadable or difficult to read when time passes.

So use it with caution.


Thank you for following the blog post.

Saturday, November 5, 2016

Understanding the different concepts of GIS - GISystem, GIScience, and GIService

Difference between GISystem, GIScience and GIService


There are three major conceptual frameworks of the acronym "GIS" and without any doubt, the most common or most talked about is the GISystem - Geospatial/Geographic Information System.

However, GIScience and GIService have recently gained popularity within the geospatial industry. For example, in the academia GIS is often viewed as a GIScience while in the business market places, GIS is often viewed as a GIService. Casual GIS users often viewed it as a tool/system (i.e. GISystem) to support their spatial problem solving - GIS is fundamentally about solving real-world problems.


GISystem, GIScience, and GIService


Over the years, the concept of GIS has experienced continues changes, from Geographical Information System (GISystem) to Geographical Information Science (GIScience), and then the development of Geographical Information Services (GIServices).

Tuesday, November 1, 2016

GeoJSON in Python

Introduction


Few days ago, I wrote a blog post on using QGIS to convert a Shapefile to GeoJSON.

In this post, I will talk about working with GeoJSON object in Python programming language.

Basically we know a GeoJSON is an object contains spatially related datasets. Conventions used by GeoJSON are known to programmers, which include C, C++, Java, Python, Perl, etc.

GeoJSON has the benefit of having implementations in many languages (especially JavaScript), making it suitable for inter-application communication of spatial data.


GeoJSON in Python


There are many python libraries that allow you to load and manipulate JSON/GeoJSON objects within the python programming ecosystem. The common and default library is the JSON which comes by default with any python installation.

All you need to do to use it is to call this line "import json".

Code Demo


Am going to use a GeoJSON file containing polygon features and saved with the name "Polygon_3.geojson" on my local disk for this demonstration.



There are two most important functions in the json library for converting python objects to json/geojson or vise-versa named: dumps() and load() functions.

In this demo, I will just read in the geojson file above into the python development environment using the with open() python command. This will allow us have access to the individual polygon elements within the geojson file as follow:-


.As you can see from above, when you load a GeoJSON file using the JSON library, you get a dictionary that contains an entry features, which contains the list of features.

Each feature in turn consists of a dictionary, which among other things, contains an entry geometry.

The geometry is a dictionary containing the entries type (polygon in this case) and coordinates of the vertices. So you can traverse the GeoJSON file like this:

import json

with open('Polygon_3.geojson') as f:
    data = json.load(f)

for feature in data['features']:
    print (feature['geometry']['type'])
    print (feature['geometry']['coordinates'])



As you can see, the GeoJSON file contains five polygons of different number of vertices and shape as indicated by the amount of pairs of coordinates for each polygon.

That is it.
Thanks for reading.