1,720,992 research outputs found
Geographic data Visualisation and Map Generation
No full textThis project is one of the academic projects given to us in the Geographic Information System (GIS) Course.
Created by: Pranav Pandya (Me) and
Kartikey Hadiya
We sampled information for pollution emission in Delhi, India.
Pollution data was obtained from https://data.gov.in/resources/real-time-air-quality-index-various-locations
Pollution index data can be obtained from https://cpcb.nic.in/RealTimeAirQualityData.php
Pollution data only had address of Indian Meteorological Department, so each station was located in Google Earth and pin points were added at each station.
Then in the sidebar containing those pins on right-click, a new folder was added and all the pins were added in that new folder in google earth. Then that folder was saved as kml file.
This kml file was uploaded to Mygeodata: https://mygeodata.cloud/converter/kml-to-csv and was converted into csv.
Then the csv file was opened and coordinates were copied in the pollution data file. That file was later saved as CSV and imported in ArcGIS and xy data was displayed.
Shapefile was obtained from web search, which is attached as well. That shapefile was imported in ArcGIS and the final view was generated which is shown in the picture
Panoply.io for Database Warehousing and Post Analysis using Sequal Language (SQL)
No full textIt has never been easier to solve any database related problem using any sequel language and the following gives an opportunity for you guys to understand how I was able to figure out some of the interline relationships between databases using Panoply.io tool.
I was able to insert coronavirus dataset and create a submittable, reusable result. I hope it helps you work in Data Warehouse environment.
The following is list of SQL commands performed on dataset attached below with the final output as stored in Exports Folder
QUERY 1
SELECT "Province/State" As "Region", Deaths, Recovered, Confirmed
FROM "public"."coronavirus_updated"
WHERE Recovered>(Deaths/2)
AND Deaths>0
Description: How will we estimate where Coronavirus has infiltrated, but there is effective recovery amongst patients? We can view those places by having Recovery twice more than the Death Toll.
Query 2
SELECT country, sum(confirmed) as "Confirmed Count", sum(Recovered) as "Recovered Count", sum(Deaths) as "Death Toll"
FROM "public"."coronavirus_updated"
WHERE Recovered>(Deaths/2)
AND Confirmed>0
GROUP BY country
Description: Coronavirus Epidemic has infiltrated multiple countries, and the only way to be safe is by knowing the countries which have confirmed Coronavirus Cases. So here is a list of those countries
Query 3
SELECT country as "Countries where Coronavirus has reached"
FROM "public"."coronavirus_updated"
WHERE confirmed>0 GROUP BY country
Description: Coronavirus Epidemic has infiltrated multiple countries, and the only way to be safe is by knowing the countries which have confirmed Coronavirus Cases. So here is a list of those countries.
Query 4
SELECT country, sum(suspected) as "Suspected Cases under potential CoronaVirus outbreak"
FROM "public"."coronavirus_updated"
WHERE suspected>0
AND deaths=0
AND confirmed=0
GROUP BY country
ORDER BY sum(suspected)
DESC
Description: Coronavirus is spreading at alarming rate. In order to know which countries are newly getting the virus is important because in these countries if timely measures are taken, it could prevent any causalities. Here is a list of suspected cases with no virus resulted deaths.
Query 5
SELECT country, sum(suspected) as "Coronavirus uncontrolled spread count and human life loss",
100*sum(suspected)/(SELECT sum((suspected)) FROM "public"."coronavirus_updated") as "Global suspected Exposure of Coronavirus in percentage"
FROM "public"."coronavirus_updated"
WHERE suspected>0
AND deaths=0
GROUP BY country
ORDER BY sum(suspected)
DESC
Description: Coronavirus is getting stronger in particular countries, but how will we measure that? We can measure it by knowing the percentage of suspected patients amongst countries which still doesn’t have any Coronavirus related deaths. The following is a list
Coronavirus Panoply.io for Database Warehousing and Post Analysis using Sequal Language (SQL)
No full textIt has never been easier to solve any database related problem using any sequel language and the following gives an opportunity for you guys to understand how I was able to figure out some of the interline relationships between databases using Panoply.io tool.
I was able to insert coronavirus dataset and create a submittable, reusable result. I hope it helps you work in Data Warehouse environment.
The following is list of SQL commands performed on dataset attached below with the final output as stored in Exports Folder
QUERY 1
SELECT "Province/State" As "Region", Deaths, Recovered, Confirmed
FROM "public"."coronavirus_updated"
WHERE Recovered>(Deaths/2)
AND Deaths>0
Description: How will we estimate where Coronavirus has infiltrated, but there is effective recovery amongst patients? We can view those places by having Recovery twice more than the Death Toll.
Query 2
SELECT country, sum(confirmed) as "Confirmed Count", sum(Recovered) as "Recovered Count", sum(Deaths) as "Death Toll"
FROM "public"."coronavirus_updated"
WHERE Recovered>(Deaths/2)
AND Confirmed>0
GROUP BY country
Description: Coronavirus Epidemic has infiltrated multiple countries, and the only way to be safe is by knowing the countries which have confirmed Coronavirus Cases. So here is a list of those countries
Query 3
SELECT country as "Countries where Coronavirus has reached"
FROM "public"."coronavirus_updated"
WHERE confirmed>0 GROUP BY country
Description: Coronavirus Epidemic has infiltrated multiple countries, and the only way to be safe is by knowing the countries which have confirmed Coronavirus Cases. So here is a list of those countries.
Query 4
SELECT country, sum(suspected) as "Suspected Cases under potential CoronaVirus outbreak"
FROM "public"."coronavirus_updated"
WHERE suspected>0
AND deaths=0
AND confirmed=0
GROUP BY country
ORDER BY sum(suspected)
DESC
Description: Coronavirus is spreading at alarming rate. In order to know which countries are newly getting the virus is important because in these countries if timely measures are taken, it could prevent any causalities. Here is a list of suspected cases with no virus resulted deaths.
Query 5
SELECT country, sum(suspected) as "Coronavirus uncontrolled spread count and human life loss",
100*sum(suspected)/(SELECT sum((suspected)) FROM "public"."coronavirus_updated") as "Global suspected Exposure of Coronavirus in percentage"
FROM "public"."coronavirus_updated"
WHERE suspected>0
AND deaths=0
GROUP BY country
ORDER BY sum(suspected)
DESC
Description: Coronavirus is getting stronger in particular countries, but how will we measure that? We can measure it by knowing the percentage of suspected patients amongst countries which still doesn’t have any Coronavirus related deaths. The following is a list.
Data Provided by:
SRK, Data Scientist at H2O.ai, Chennai, Indi
Geotagging Images using Geosetter in Google Earth Environment
No full textAttachment: Methodology flow diagram & Final Outputs in kmz
Coordinates were required to be assigned to a set of 50 images which were to be taken manually by the user. So, Images had to be procured, their locations were to be known and the location was required to be inscribed in the Image Metadata.
Step 1: Obtaining Images + Location
Google Earth has Google Photos which are uploaded by Individual users. These photos were enabled in Google Earth and each image was copied and pasted in MS Paint and then saved with an appropriate name
At each image, the location pins were placed to mark the coordinates for each image
Step 2: Extracting the coordiantes
The marked pin locations in Google Earth were saved as: Save My Places -> .kml
This kml file was uploaded to https://mygeodata.cloud/converter/kml-to-csv to convert it into csv file to read the lat & long for each pinned markings
Step 3: Embedding Coordinates in Image Metadata
Geosetter Software enables us to embed coordinated in each Image file.
Images were loaded in Geosetter and the subsequent coordinates were added with the Edit Data dialogue. The location name of each image was added in Object Name.
The Images were then exported with thumbnail size=2000 and thumbnail={Object_Name
Web Based Resource Mapping of Model Colony, Pune, India
No full textResource Mapping data was collected from field survey and all points such as markets, atms, schools were located and appropriate tags were given.
Data was uploaded on Google sheets and addons of Fusion Mas and point map were installed and addons were run to form virtual maps in their own particular webpages.
Source link of those webpages are determined and were added in a iframe in src link.
In web html design a table was made and all three iframe are added in table.
The final html was added as html element in sites.google.com to create a custom website.
The website link:
www.sites.google.com/site/pranavrsmap
Webpage and Sheets are the most important data here. Other data are optional and are uploaded for your Geospatial Location researc
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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