Analyzing the Factors of Deforestation using Association Rule Mining

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1 Analyzing the Factors of Deforestation using Association Rule Mining Mrs. K.R.Manjula Associate Professor, Dept. of MCA, SIET, Puttur Mr.S.Anand Kumar Varma Associate Professor, Dept. of Civil Engineering, SIET, Puttur Prof. S. Jyothi Head (i/c) CSE & IT BOS Chairperson Sri Padmavati Women s University Tirupati, India jyothi.spmvv@gmail.com

2 Abstract The socio-demographic factors and the land use patterns are identified and derived from the remote Sensing images using GIS. Given these elements, the main objective of this paper is to analyze the role of different driving factors for deforestation and the relationship among these factors in the study area. For that an association analysis on deforestation factors is done.

3 The widespread use of spatial database and spatial data mining technique can be used to understand inter relational nature of spatial data. The spatial association extraction algorithm discussed by Koperski and Han [15] is used in our study. The algorithm searches for associations between spatial objects or non-spatial objects and attributes.

4 In this study, rules are expressed by spatial and non-spatial predicates. Typically, the analysis reveals the positive association of each of the above specified factors for deforestation. The final outcome of this paper can be used as the suggestions for policy issues and technological developments to the decision makers.

5 Introduction Factors of the study are demographic, topography, road infra structure and mining units. the study area is 26 mandals of Chittoor and Kadapa districts.

6 The strong association rules can derive that population growth, urban development, road infrastructure, change in land use patterns, industrial development

7 To control and decrease the forest degradation the government should know where, when, why and how such deforestation occurs and what measures can be taken to address the problem. The science and technologies of GIS, Remote Sensing and Data mining technique could be a perfect tool for answering the above questions.

8 Data Pre Processing By classifying the forest and nonforest areas taking 1991, 2001 and 2011 satellite images overlaying them and then the changes are identified. Information of these maps and other ancillary data are entered in the database [18][23].

9 Spatial data mining Mining knowledge from large amounts of spatial data, collected in various applications, ranging from Remote sensing to Geographical information systems (GIS), Computer cartography, Environmental assessment and Planning.

10 Spatial Association Rule Mining A spatial association rule is a rule which describes the implication of one or a set of features by another set of features in spatial databases. A spatial association rule is a rule of the form A B, where A and B are set of spatial predicates.

11 Mining spatial association rules can be decomposed in three main steps: Extract spatial predicates: This is usually performed as a data preprocessing method. Find all frequent patterns/predicates: A set of predicates is a frequent pattern if its support is at least equal to a certain threshold, called minsup. Generate strong rules: A rule is strong if it frequent and the confidence is at least equal to a certain threshold, called minconf.

12 Examples of spatial association rules. Non-spatial consequent with spatial antecedent(s). is_a( x, house)^ close_to( x, beach) => is_expensive( x) (90%) Spatial consequent with non spatial/spatial antecedent(s). is_a( x, gas_station)=>close_to( x, highway) (75%).

13 METHODOLOGY Data on limited and unlimited access highways are acquired from the Environmental Systems Research Institute (ESRI) Streets Data-base. Land cover data for the 1991, 2001 are acquired from the U.S. Geological Survey s (USGS) and 2011 from National Remote Sensing Centre (NRSC) and Topographic maps from Survey of India with 1,25,000 scale during the year 1970s. From these, vector polygon data are generated from historic aerial photography [11][12][13].

14 The following variables are then calculated for each polygon Land Cover Change Urban Development Distance to Highway Population Density

15 The database relations for organizing and representing spatial objects are: Mandal (Name, Area, Type, District, Geo, ) Road (Name, Length, Type, Geo,.) Mine (Name, Area, Type, Geo, ) Urban/Builtup (Name, Area, Type, Geo, ) Population (MandalName, PopulationSize, density )

16 Mining spatial association rules involve data mining query which extracts the above related data set from the database covering mandals, roads, builtup land, degraded areas and forest areas. Then the adjacent_to and with_in relationships are computed along with support count.

17 Relational Table with Population information

18 Table showing classified data with Feature class

19 Spatial data with Spatial Predicates Name and Count

20 Spatial data with Spatial Predicates and Count

21 Map showing the change in builtup(urban) land

22 Map showing the Buffering of Road

23 Map showing extent of Mining area

24 Algorithm: Algorithm for Mining Spatial Association Rules Mining the spatial association rules in a large spatial database. Input: The input consists of a spatial database, a mining query, a set of thresholds minimum support and minimum confidence Output: Strong spatial association rules for the relevant sets of objects and relations. Method: Mining spatial association rules proceeds as follows:

25 Step 1: Taskrelevant DB := extract task relevant objects (Spatial and Non spatial) Step 2: Predicate_DB := spatial computation (Task relevant DB) Step 3: LargePredicate_DB :=filtering with minimum support (predicate DB) Step 4: Fine predicate DB: = refined spatial computation (Large predicate DB) Step 5: Find large predicates and mine rules (Fine predicate DB)

26 A few of the Interesting Rules Is_a(X, Mine ) =>Adjacent_to(X, Road ) : (73%) Is_a(X, Builtup )=>Adjacent_to(X, Road ) : (80%) Is_a(X, Mine )=>Adjacent_to(X, Degraded ) :(52%) Is_a(X, Urban ) => Population(X, High ) : (80%) Is_a(X, Rural ) => Population(X, Low ) : (72%) Is_a(X, Mine ) ^ Within(X, Forest )=>Adjacent_to(X, Degraded ) : (80%) Is_a(X, Builtup ) ^ Adjacent_to(X, Degraded ) => Adjacent_to(X, Forest ) : (78%)

27 RESULT ANALYSIS AND DISCUSSION Demographic factors (population size, density and growth) Population growth and high density of population in large or urban areas which results in land scarcity and leads to deforestation from the rules if it is adjacent to forest. Is_a(X, Urban ) => Population(X, High ): 80%) Is_a(X, Rural ) => Population(X, Low ): (72%)

28 Population Density and Urban growth High density of population and other developments converts and merges village to town and town to urban areas which results in deforestation from the rules if it is adjacent to forest. Is_a(X, Urban ) =>Population(X, High ): (80%) Is_a(X, Rural ) => Population(X, Low ): (72%) Is_a(X, Urban ) ^ Adjacent_to(x, Road ) ^ Adjacent_to(X, Degraded ) => Population(X, High ): (80%)

29 Road infrastructure and access to urban towns Logging and mining concessions that entail road building which to deforestation from the rules if it is within to forest. Is_a(X, Mine ) ^ Within(X, Forest ) ^ Adjacent_to(X, Road ) =>Aadjacent_to(X, Degraded ) : (87%)

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