Jerod  Mante

Jerod Mante

1603959840

Types of Digitization and Its Error in GIS | Digitizing Map Data

Digitization is a crucial technique for data and storage in GIS Development. It is used to capture the coordinates in point, line, or polygon format. The process of Digitization is expensive and time-consuming.

Digitization is converting hardcopy / scanned copy or satellite/Aerial base maps into vector data. Features are extracted from the existing maps or satellite images.

The Role of Digitizer

The digitizer plays a major role in managing the accuracy of the digitized features or maps. A well-experienced digitizer should know to interpret maps and digitized the features accurately and lesser time consumption. Most importantly, the quality of the digitization is the much-expected quality for a Digitizer role.

Types of Digitization

Today, there are various ways to do Digitization. Let’s take a look at the common types.

1. Manual Digitizing

Manual Digitizing is done by digitizing tablet. The digitizer manually traces all the lines from the hardcopy map (eg.Toposheet), and parallelly. The digital maps are created on the computer. It is only less time consuming but also has high accuracy when comparing with other digitizing methods.

2. Heads-up Digitizing

Heads-up Digitizing is similar to manual digitizing. In the manual digitizing process, it digitizes in hardcopy, but in this method, it scans the map directly and displays it on the desktop screen.

3. Interactive Tracing Method

The interactive tracing method is an advanced technique that has evolved from Heads-up digitizing. It is quite excellent in terms of accuracy and speed.

4. Automatic Digitizing

Automatic Digitizing is the process of converting raster to vector in an automated method using pattern recognition and image processing techniques. In this technique, the computer traces all the features on the map; it gives high accuracy with low time consumption. It allows customization and improved quality of images. This process is also known as Vectorisation.

#digitization #geographic information system

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Types of Digitization and Its Error in GIS | Digitizing Map Data
Siphiwe  Nair

Siphiwe Nair

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Gage  Monahan

Gage Monahan

1618487220

5 Essential Elements for Developing and Delivering GIS Solution

Developing a GIS solution is important when performing analysis tasks over specific geographic areas. A GIS or Geographic Information System is a software that captures geographic information and utilizes the data for manipulation, viewing, and analysis depending on which context or parameters it is required.

The potential solution that a GIS can provide is endless.

Big or small, many businesses and institutions rely heavily on geographic data to understand the demographic areas of possibilities. For example, a fast-food center aiming to maximize its catchment area and accessibility will look for demographic information collected from the busiest roads, the best junctions, and leisure centers such as shopping malls and multiplex theatres.

A telecom provider will need to understand if the network condition over a region is good. If not, then they will work on delivering optimized solutions.

#data science #software developent #data storage #maps #gis #data transformation #gis mapping software #data acquisition #data representation

Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Arvel  Parker

Arvel Parker

1593156510

Basic Data Types in Python | Python Web Development For Beginners

At the end of 2019, Python is one of the fastest-growing programming languages. More than 10% of developers have opted for Python development.

In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.

Table of Contents  hide

I Mutable objects

II Immutable objects

III Built-in data types in Python

Mutable objects

The Size and declared value and its sequence of the object can able to be modified called mutable objects.

Mutable Data Types are list, dict, set, byte array

Immutable objects

The Size and declared value and its sequence of the object can able to be modified.

Immutable data types are int, float, complex, String, tuples, bytes, and frozen sets.

id() and type() is used to know the Identity and data type of the object

a**=25+**85j

type**(a)**

output**:<class’complex’>**

b**={1:10,2:“Pinky”****}**

id**(b)**

output**:**238989244168

Built-in data types in Python

a**=str(“Hello python world”)****#str**

b**=int(18)****#int**

c**=float(20482.5)****#float**

d**=complex(5+85j)****#complex**

e**=list((“python”,“fast”,“growing”,“in”,2018))****#list**

f**=tuple((“python”,“easy”,“learning”))****#tuple**

g**=range(10)****#range**

h**=dict(name=“Vidu”,age=36)****#dict**

i**=set((“python”,“fast”,“growing”,“in”,2018))****#set**

j**=frozenset((“python”,“fast”,“growing”,“in”,2018))****#frozenset**

k**=bool(18)****#bool**

l**=bytes(8)****#bytes**

m**=bytearray(8)****#bytearray**

n**=memoryview(bytes(18))****#memoryview**

Numbers (int,Float,Complex)

Numbers are stored in numeric Types. when a number is assigned to a variable, Python creates Number objects.

#signed interger

age**=**18

print**(age)**

Output**:**18

Python supports 3 types of numeric data.

int (signed integers like 20, 2, 225, etc.)

float (float is used to store floating-point numbers like 9.8, 3.1444, 89.52, etc.)

complex (complex numbers like 8.94j, 4.0 + 7.3j, etc.)

A complex number contains an ordered pair, i.e., a + ib where a and b denote the real and imaginary parts respectively).

String

The string can be represented as the sequence of characters in the quotation marks. In python, to define strings we can use single, double, or triple quotes.

# String Handling

‘Hello Python’

#single (') Quoted String

“Hello Python”

# Double (") Quoted String

“”“Hello Python”“”

‘’‘Hello Python’‘’

# triple (‘’') (“”") Quoted String

In python, string handling is a straightforward task, and python provides various built-in functions and operators for representing strings.

The operator “+” is used to concatenate strings and “*” is used to repeat the string.

“Hello”+“python”

output**:****‘Hello python’**

"python "*****2

'Output : Python python ’

#python web development #data types in python #list of all python data types #python data types #python datatypes #python types #python variable type

Cyrus  Kreiger

Cyrus Kreiger

1618039260

How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt