Consequences and Sustainable solutions for Deforestation in India

Consequences and Sustainable solutions for Deforestation in India

Sushma D

Dept. of Information science and Engineering Bangalore, India

Roopa T

Dept. of Information science and Engineering Bangalore, India

Reshma N Bhosle

Dept. of Information science and Engineering Bangalore, India

R. Ashok Kumar

Department of Information Science and Engineering Bangalore, India

AbstractForest rate is depleting at a fast rate in India. Deforestation is the main reason behind this. Deforestation occur when forests are destroyed due to human needs. The analysis of deforestation is done using twitter. NodeXL Software is used to collect data. This study analyses the major reasons for deforestation in India and to find the sustainable solution to overcome it.

KeywordsDeforestation;NodeXL;Twitter;India.

1. INTRODUCTION

   Forests is an most important and very essential natural resource for mankind. Deforestation is affecting our natural habitat, biodiversity, and climatic conditions. Twitter is an most widely adopted social media platform throughout the world. This platform mostly facilitate users to tweet a message and then allow any twitter users to access those message. Twitter users also use #(hashtags), @(mentions), tweets. By considering these parameters, data can be collected using twitter import tool of NodeXL and then it is analyzed. These network analysis metric is used to examine tweet network. Purpose of these study aims to find consequences for deforestation, why and where deforestation has taken place and to find sustainable solutions for deforestation.

2. LITERATURE REVIEW

   In recent years, there is an environmental consciousness on the degradation of forest and environment resulting in depletion of ecological services and life supporting systems in the Indian state of Nagaland [1] these forest resource are very essential part of human life. As increase in population rate, the pressure on natural resources has also increased, and it leads to an vast reduction of forest thus by destroying bio diversity of the state. One of the most major causes of deforestation has also been highlighted as an demographic factor. Several human activities such as shifting, farming, urbanization, and other developing activities are endangering the states forest resources. This study examines the causes and implications of forest degradation, as well as strategies for forest and environmental conservation in that particular North-East part of India.

   Natural resource have always played very important role in the growth of any rising economy. [2] The expansion, content

   and structure of Indias forest areas from 1991 to 2009 are the subject of this research. The examination was thereafter done on a 12,50,000 scale until 1999. These research even examines the qualitative and quantitative changes in forest regions by examining the various kinds of trees. And even it gives any sustainable solution to reduce cutting of trees. These analysis has gives for what reason deforestation has occurred and cause of this deforestation. As forest is very much essential for natural habitat, due to deforestation there is an huge loss of different plant and animal species, and these are becoming extinct these days. [3] This study recommends appropriate remote sensing strategies for analyzing planet scope of 3 meter resolution satellite data and these is mainly used to recognize changes in forest areas and remote sensing method with the GUI are utilized, normalized differential vegetation index is mainly used to classify forest and these values which are obtained it used to reveal changes in forest cover areas, and even resulting in category count of forest these values even detect few changes in area of forest cover land , decreasing category mainly reveals in cutting of forest trees, fires in forest, and shedding down of leaves and many more.[4] Deforestation is represented in larger, more in many tropical and other regions of developing countries around throughout world. The aim of these is used to detect potential areas of deforestation as well as to analyse the forest cover dynamics in India, these approach are used to validate the model, which shows that it has an overall accuracy.

3. METHODOLOGY

   NodeXL is very interactive and visualization tool that is both appealing and user friendly. Finding an significant and influential nodes in these social media networks is one of the key characters. Here Vertices represents people or social structure such as workgroups, team, organization etc. These are also referred as nodes, agents. The connection between two vertices are referred as Edges. The concept of centrality refers to an idea of recognizing vertices on ranking of values. The total number of incoming vertices is referred as in degree. The number outgoing vertices, or arrow that is pointing away from the vertices is referred to as an out-degree. The person with highest out degree is referred as most influenced. The average distance between a vertex and other vertices in network is captured using closeness centrality. Another centrality metric

   which is used to explore from standpoint of an social network theory is an betweeness centrality. Because of eigen vector centrality, the connections can have a changeable value, therefore connecting to some vertices is more advantageous than connecting for other vertices. These studies mostly leverage twitter page mentions, where people engage with one other based on shared interests.

   Data Collection

   NodeXL is used to capture an random sample of Twitter data. All of these tweets acquried for study contain either mentions @, hashtag #, tweets. The data collection used here is @unredd, @letindbreathe, #deforestation.it even identifies the most influential induvidual or key participants as well as converstaion taking place on twitter.

   Data Cleaning

   When data is collected from NodeXL, there will be a same data repeating more number of times, so we can delete and merge duplicate data, within users social media network.

   Data Aanlysing

   The collected twitter hashtags are analsyed using NodeXL operations such as perparation of data, merging duplicate edges, auto filling of colums, sorting the vertices, creating subgraphs, using dynamic filters and changing the layout options are performed to get a network graph.

4. RESULTS

   1. Social Network Analysis on twitter user page

      @LetIndBreathe

      Here we have analysed username is @LetIndBreathe. The use of social network analysis is used to identify the social network consist of nodes which mainly indicates nodes who are connected to one another. These twitter user page used to identify various tweets, phrases containing deforestation in India. This is an environment communications collective Network of 28 states and 7 union territories of India. This page contains 2,384 tweets, 252 following, and 4,684 followers. This page also gives information about where deforestatin has occurred, and cause of deforestation.

      Fig 1. NodeXL graph for user page @LetIndBreathe

      The total number of vertices for this network are 1032 and the total number of edges are 1273. The graph is represented as Fig 1. After analysing graph metrics , we get following values from table 1.

      Table 1. overall metrics for @letindbreathe

      Graph Matric	Value
      connected components	1
      vertices	75
      unique_edges	75 164
      slf-loops	0
      total edges	170
      edges with duplicates	6
      reciprocated edge ratio	0.011976048
      reciprocated vertex pair ratio	0.006024096
      graph type	directed
      average geodesic distance	3.54
      maximum geodesic distance	79
      single-vertex connected	3.54
      maximum vertices in a connected component	0
      maximum edge in an connected component	170
      modularity	not applicable
      graph_density	0.02710159

      Then the insights from the graph are drawn. According to the graph, @letindbreathe is popular as it has more incoming edges with the value having 36. From the graph, we can analyze that @anerakoilash is most influential as he has more outdegree with the value of 15. From the graph we can analyze that leindbreathe has highest betweeness centrality with the value 4426.7 and closeness centrality with the value

      0.006 and eigen vector centrality of 0.010 and clustering coefficient of 0.01 given in Table 2.

      Table 2 maximum and minimum values of metrics

      Metrics	Minimum	Maximum
      In-degree	0	36
      Out-degree	0	15
      Betweeness Centrality	0	4426.750
      Closeness Centrality	0	0.006
      Eigen Vector Centrality	0	0.010
      Clustering Co-Efficient	0	0.001

   2. Social Network Analysis on twitter user page

   @Unredd.

   We have analyzed Twitter user name @unredd. The use of social network analysis is used to identify the social network consist of nodes which indicates nodes, that is connected toone another. These twitter user page used to identify various tweets, phases containing deforestation in India. This page is mainly supports nearly 65 developing countries including India in order to reduce deforestation. Our planet is better with forests, this is the main moto of these page. This page contains 9,441 tweets, 2,749 following, and 34k followers. This page also gives information about reduction of deforestation. The number of vertices used to analyses were 100 and edges are

   250. The graph is represented in as Fig 2.

   Eigen Vector Centrality	0	0.0923
   Clustering Coefficient	0	0.027

   Figure 2. NodeXL graph for user page @Unredd

   After analyzing the graph metrics we get the values from the table 3.

   Table 3 overall metrics for @unredd

   Graph Matric	Value
   vertices	41
   graph type	directed
   total Edges	56
   edges with duplicates	5
   self-loops	5
   uniqueedges	49
   reciprocated-edge-ratio	0.081
   reciprocated-vertex-pair	0.041
   maximum edges in an connected component	31
   maximum geodesic distance	5
   connected components	4
   maximum-verticesin-an connected- component	31
   single-vertex-connected component	0
   Graph-density	0.0387
   modularity	not applicable
   average-geodesic distance	2.579

   Then the insights from the graph are drawn. According to the graph, @unredd is popular as it has more incoming edges with the value having 19. From the graph, we can analyze that @mbambu4 is most influential as he has more outdegree with the value of 9. From the graph we can analyze that @unredd has highest betweeness centrality with the value 725 and closeness centrality with the value 1 and @mbambu4 having the highest values of Eigen vector centrality of 0.010 and clustering coefficient of 0.01 given in Table 4.

   Metrics	Minimum	Maximum
   In-degree	0	19
   Out-degree	0	9
   Betweeness Centrality	0	725
   Closeness Centrality	0	1

   Table 4 maximum and minimum values of metrics.

5. CONCLUSION

Twitter is one of the widely used social network platform which is used for our research to collect the data. The hashtags used to collect the data are @letindbreath and @unredd the hashtags were the result of the people or users of Twitter sharing their thoughts about the deforestation of India. By analyzing the data obtained by importing through NodeXL twitter import Tool @letindbreath has the highest betweeness centrality. Hence if the node is removed then the network will be disturbed. From which we can find that the particular node is the most influential in the network.

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