Enhancement of Effective Spatial Data Analysis using R

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1 Indian Journal of Science and Technology, Vol 9(21), DOI: /ijst/2016/v9i21/95149, June 2016 ISSN (Print) : ISSN (Online) : Enhancement of Effective Spatial Data Analysis using R S. Palaniappan 1*, T. V. Rajinikanth 2 and A. Govardhan 3 1 Department of CSE, Saveetha University, Chennai , Tamil Nadu, India; s.palani.in@gmail.com 2 Department of CSE, SNIST, Hyderabad , Andhra Pradesh, India; rajinitv@gmail.com 3 Department of SIT, JNTUH, Kukatpally, Hyderabad , Andhra Pradesh, India; govardhan_cse@yahoo.co.in Abstract Background: The availability of Spatial Data which is a part of GIS is growing day by day in an exponential manner. This high availability of data is throwing challenges to the research community to analyze and draw effective conclusions. The Present Study aims at requirement for effective analysis and to draw Conclusions. Methods/Statistical Analysis: Spatial Analysis requires logical relationships between attribute data and map features.spatial data Analysis is not a simple single task it requires complex procedures in which combinational techniques namely Hybrid techniques are required for effective analysis. Mathematics and statistics are the fundamentals to spatial data analytics. In this paper, a realistic Spatialcrime data set was considered for analysis. It involves different types of data mining Techniques like Clustering, Classification and Association rule mining techniques apart from Hybrid techniques. These hybrid Data mining Techniques were applied using R. Findings: The Hybrid Data Mining techniques with K-means Clustering and J48 Decision Tree Algorithm was developed and Applied for the enhancement of accuracy. Association Rule generation Apriori algorithm was applied on the resultant K-means clustered data set. The application of 3D visualization techniques also made for further analysis. Applications/Improvements: It is essentially required to analyze these complex spatial data sets effectively. So there is a need of hybrid Spatial Data Mining Techniquesrequirement for effective analysis and to draw Conclusions. Keywords: Complex Spatial Data Sets, Effective Spatial Data Analysis, Hybrid Data Mining Techniques, Spatial Data Analysis, 3D Visualization Techniques 1. Introduction Spatial Analysis or Spatial Statistics 1 are the techniques which are used to study properties of topological, geometrical or geographical objects. It is used to analyze the spatial data. The types of Spatial analysis includes Spatial data Analysis, Spatial Auto correlation, Interpolation, Regression, Interaction and Simulation and Modeling. Spatial analysis 2 is the process by which we turn raw data into useful information. It is a set of techniques for analyzing spatial data. It is the analysis of Geographical data sets. Shapefiles are the vector data and composed of a few required files a).shp the shape format file b).shx the shape index file c).dbf the attribute file. R language was best suitable choice for spatial data analysis. Statistical inference deals with drawing of conclusions based on data and pre assumptions. A Spatial Data Mining is nothing but application of traditional Data Mining (DM) methods to spatial data. SDM is the process of discovering required relationships and patterns that may exist implicitly in spatial databases 3. Spatial data mining 4-6 plays an important role in extraction of interesting patterns and finding relationships between spatial and non spatial data. 2. Literature Survey A Spatial Data set 7 is the geographical information that is used to represents the features and boundaries of a location on earth. It is stored in the form of co-ordinates and topology with mapping of data. GIS systems are used to access, analyze and manipulate the Spatial Data sets. The example of Spatial data 8 are the satellite images. A *Author for correspondence

2 Enhancement of Effective Spatial Data Analysis using R spatial database 7 is a database is used to optimize and store data apart from query objects data defined in a geometric space. Normally these are used to represent simple geometric objects such as points, lines and polygons. Spatial Data Mining (SDM) 8 techniques are useful to extract potentially useful knowledge. It is used to find hidden patterns, relations between spatial data and/or non-spatial data. Spatial relationships have great importance in the analysis process 9,10. The traditional Data mining Techniques are not suitable to spatial data as they do not capable to handle data belong to location. So it is required to look for new methods to address spatial relationships and spatial data. Spatial relationships measuring is a time taken process as it generates large volume of data when geometric location is encoded. To query and perform spatial analytical tasks GIS software s are useful. GIS are not much useful in this regard. So it is necessary to integrate the available methods with the spatial data mining methods 15, K-Means Cluster Algorithm K-means clustering 17,18 is a DM algorithm used to cluster the observations into groups of similar observations without having any previous knowledge about those relationships. It is also called simple K-means algorithm. It is applied to the domains like namely medical imaging, biometrics, Spatial, Spatio-temporal etc. 2.2 Working of k Means Algorithm 1. Put K points into the space denoted by the objects to be clustered. 2. These points denote initial group of Centroids. Assign every object to the closest Centroid group. 3. Recalculate the positions of the K Centroids after every object has been assigned. 4. Repeat the above two Steps 2 and 3 until there is no change in Centroids. 2.3 Decision Tree Itis used as a predictive model which maps observations ofan item to conclusions of that item s target value. There are 91 classification types. In this the class labels are represented by leaf nodesand branch nodesdenotes features that directs to these class labels. J48 algorithm is an advancedversions of C4.5 algorithms. The output of this J48 algorithm results a Decision tree. 3. Proposed Approach The Sacramento crime January 2006 file 19 contains 7,584 crime records, as made available by the Sacramento Police Department, California. This was posted by Spatial Key 20 The crime spatial data set was collected for Spatial/Non-Spatial analysis. It was initially subjected to K-means clustering techniques and after that the resultant data set was subjected to J48 Decision Tree classification which then again subjected to Association Data mining technique called Apriori. The desired rules were generated after this hybridization is allowed for further analysis. 4. Implementation of Proposed Approach The Crime Data set 20 was subjected to pre-process and lot of pre-processed techniques like data cleaning, Noise removal and inconsistency etc. were applied and then bring it in to the desired format for subjecting to various Hybrid Data Mining techniques. The Pre-processed data set is split in to Training Data set and testing data set with ratio of 70% and 30%. Initially the K-means clustering technique was applied on the pre-processed Training data set and then tested with Testing data set. Fix the number of clusters initial input as 5. The number of clusters is arrived as 5 through statistical results / observations. The spatial data set consists of 12 attributes namely FIR.No., Date, Time, Address, District, Beat, Grid, Ucr_nic_code, Crime, CN, Latitude and Longitude. The 5 Clusters are shown in the following Table.1. CLUSTER 0:The district 6 has associated with Beat 6C and Missing Person & Towed/Stored Vehicle as major Crime with Crime number 0 apart from other thefts as Petty Theft, Hit/Run. The district 6 is associated with Latitude and Longitude values as Centroids. The time is 11.00AM as its Centroid with date as 1/23/2006 and address Centroid as 1 ScrippsDr. CLUSTER 1:The district 2 has associated with Beat 2B and Petty Theft/Inside as major Crime with Crime number 7000 apart from other thefts as Auto Theft, Burglary Residence. The district 2 is associated with Latitude , Longitude values as Centroids. The time is 12.00PM as its Centroid with date as 1/23/2006 and address Centroid as 1689 Ardenway. CLUSTER 2: The district 3 has associated with Beat 3M and Grand Theft as major Crime with Crime number 487(A) PC apart from other thefts as Auto/Vehicle 2 Indian Journal of Science and Technology

3 S. Palaniappan, T. V. Rajinikanth and A. Govardhan Theft. The district 5 is associated with Latitude , Longitude values as Centroids. The time is 8.00AM as its Centroid with date as 1/24/2006 and address Centroid as 170 Shrike Cir. CLUSTER 3: The district 4 has associated with Beat 4A and Take Vehicle W/O Owner as major Crime with Crime number 10851(A) apart from other thefts as Burglary / Theft vehicle, Battery NoncohabSpous. The district 4 is associated with Latitude , Longitude values as Centroids. The time is 10.00AM as its Centroid with date as 1/23/2006 and address Centroid as 1040 Florin Rd. The Ucr_ncic_code is CLUSTER 4: The district 4 has associated with Beat 4B and Towed / Stored Vehicle as major Crime with Crime number apart from other thefts as Burglary / Theft vehicle. The district 4 is associated with Latitude , Longitude values as Centroids. The time is 7.57AM as its Centroid with date as 1/24/2006 and address Centroid as 1342 Palomar Cir. The percentage of data instances associated with Cluster0 is 18%, Cluster1 is 17%, Cluster2 is 30%, Cluster3 is 20% and Cluster 4 is 15%. After this the resultant clustered data set was subjected to hybridization using Decision Tree algorithm called J48 and the results were analyzed effectively for bringing conclusions. The resultant clustered and classified data set was subjected to Association rule mining technique namely Apriori and the final results were analyzed for effective conclusions. 5. Results and Analysis All the clusters are showing that Majority numbers of crimes are Burglary, Theft of Auto/Vehicle, Petty Thefts, Battery NoncohabSpous, Grand Theft and Hit & Run. These kinds of Thefts are associated with all the districts. The Percentage of instances associated with Clusters shows that Cluster 2 has highest percentage followed by Cluster3. The lowest percentage of instances is associated with Cluster4. From Various Decision trees it is inferred that the districts are associated with beat numbers based on the values of Latitude, Longitude and Grid values. One of the decision tree is shown in Figure 1. District 1 is associated with beat number 1A, 1B and 1C. District 2 is majorly associated with beat numbers 2A, 2B, 2C respectively. District 3 is majorly associated with beat numbers 3A, 3B, 3C and 3M. District 4 is majorly associated with beat numbers 4A, 4B and 4C. The district 5 is majorly associated with beat numbers 5A, 5B and 5C respectively. The district 6 is associated with 6A, 6B and 6C.The Figure 2 shows the graphical relationship of districts numbers with beat numbers in which Districts numbers are taken along X-axis and Beat numbers along Y-axis. More number of beats are there in district number 3 followed by 4 and the remaining districts has 3 beats equally. Figure 4 shows 3D Graph for 6 Districts with Latitude and Longitude in which Latitude was taken along X-axis, Longitude was taken along Y-axis and Districts along Z-axis. The Association Algorithm Apriori was applied and found desired rules with various Support and Confidence values. The number of rules generated is shown in Table 2. The numbers of rules are changing based on Support and Confidence values. The Majority of the rules when considered for across different values of Support and confidence are summarized and shown below. R1: ucr_ncic_code=2404 & crime= TAKE VEH W/O OWNER && CN=10851(A)VC & Date=1/23/2006 => Cluster=cluster3 R2: ucr_ncic_code=7000&date= &CN= &Crime= TOWED / STORED VEHICLE=>Cluster=cluster1& Cluster=cluster4 R3: district=6 & Date= & CN=0, Crime = MISSING PERSON/ TOWED / STORED VEHICLE=> Cluster=cluster0 R4: district=2 &district=3 & district=4 &Date= => Cluster=cluster2 One of the 3D Visualization graph of the Association rules are shown in the Figure 3 6. Figures and Tables Figure 1. The Decision tree of Crime Data set. Indian Journal of Science and Technology 3

4 Enhancement of Effective Spatial Data Analysis using R Table 1. Clustered Crime Data Set for 5 clusters Attribute Cluster# Full Data (175.0) 0 (46.0) 1 (65.0) 2 (11.0) 3 (24.0) 4(29.0) FIR No. F3756 F3759 F3756 F3767 F3763 F3757 Date 1/23/2006 1/23/2006 1/23/2006 1/24/2006 1/23/2006 1/24/2006 Time 12:00 PM 11:00AM 12:00PM 8:00AM 10:00AM 7:57 AM Address 1689 Ardenway 1 ScrippsDr 1689 Ardenway 170 Shrike Cir 1040 Florin Rd 1342 Palomar Cir District Beat 6C 6C 2B 3M 4A 4B Grid Ucr_ncic_code Crime TSV MP / TSV PTI GT TVWO TSV CN PC 487(A) PC 10851(A) Latitude Longitude Table 2. Number of Rules generation with different Support and Confidence S.No. Support Confidence Number of Rules Figure 4. 3D Visualization of Association Rules. 7. Conclusion Figure 2. Graph Showing Districts numbers on X-axis and Beat Numbers on Y-axis. Figure 3. 3D Graph for 6 Districts with Latitude and Longitude. The Hybrid Data Mining techniques with K-means Clustering and J48 Decision Tree Algorithm was developed and Applied for the enhancement of accuracy. Association Rule generation Apriori algorithm was applied on the resultant K-means clustered data set. Beat number is associated with type of crime and where as district number is in turn associated with Beat number and Ucr_ncic_code7000. The Decision Tree is also showing that the beat numbers are associated with District numbers based on Latitude, Longitude and Grid attribute values. All the clusters are showing that Majority numbers of crimes are Burglary, Theft of Auto/Vehicle, Petty Thefts, BatteryNoncohabSpous, Grand Theft and Hit & Run. These kinds of Thefts are associated with all the districts. The same is confirmed by the Association rules thus generated. The Cluster3 is associated with 4 Indian Journal of Science and Technology

5 S. Palaniappan, T. V. Rajinikanth and A. Govardhan District 4, beat number 4A and the associated majority crime is Vehicle Theft which in turn associated with crime Number 10851(A)VC dated 1/23/2006 and ucr_ ncic_code=2404. The Cluster 1 & 4 are associated with Districts 2 & 4 with Beat numbers 2B & 4B and the associated major crime is TOWED / STORED VEHICLE which in turn with Crime number dated 1/23/2006 and ucr_ncic_code=7000. Cluster 0 is associated with District 6, beat number 6C and the associated majority crime is MISSING PERSON & TOWED / STORED VEHICLE which in turn associated with Crime Number 0 dated 1/23/2006 and ucr_ncic_code=7000. Districts 2,3 and 4 associated with date 1/23/2006 for the Cluster2. 8. Acknowledgement Thanks to the Spatial Key ( com/) organization in providing the Spatial Crime Data Set as open to researchers to do explorative analysis. 9. References 1. Spatial analysis. Available from: wiki/spatial_analysis. Date accessed: 05/10/ Spatial Data Analysis. Available from: edu/gis/chapter14_notes.pdf. Date accessed: 05/10/ Hemalatha M, Naga Saranya N. A Recent Survey on Knowledge Discovery in Spatial Data Mining. IJCI International Journal of Computer Science May; 8(3): Rao T, Rajasekhar N, Rajinikanth TV. An efficient approach for Weather forecasting using Support Vector Machines International Conference on Intelligent Network and Computing. 2012; 47. p RajiniKanth TV, Anuradha K, Premchand P, Murali Krishna IV. Weather Data Analysis of Rajasthan State using Data Mining Techniques. Journal of Advanced. 2011Apr; 3(2): Data mining. Available from: wiki/data_mining. Date accessed: 07/10/ Spatial Data base. Available from: wiki/spatial_database. Date accessed: 07/10/ Introduction to Spatial Databases. Available from: Date accessed: 07/10/ Rajanikanth J, Rajinikanth TV, Prasad TVKP, Radha Krishna B. Analysis on Spatial Data Clustering Methods - A Case Study Oct- Dec; 3(4): Fayyad UMJ, Piatetsky-Shapiro G, Smyth P. From Data Mining to Knowledge Discovery: An Overview. Advances in Knowledge Discovery and Data Mining, AAAI Press, Menlo Park. 1996; Shekhar S, Zhang P, Huang Y, Vatsavai R. Trend in Spatail Data Mining, as a chapter to appear in Data Mining: Next Generation Challenges and Future Directions Apr- Jun; 4(2): Vijay Kumar A, RajiniKanth TV. Estimation of the Influence of Fertilizer Nutrients Consumption on the Wheat Crop yield in India- a Data mining Approach Dec; 3(2): Vijay Kumar A, RajiniKanth TV. A Data Mining Approach for the Estimation of Climate Change on the Jowar Crop Yield in India Dec; 2(2): Vijay Kumar A, RajiniKanth TV. Estimation of the Influential Factors of rice yield in India. 2nd International Conference on Advanced Computing methodologies Aug; Gueting RH. An Introduction to Spatial Database Systems, Special Issue on Spatial Database Systems of the VLDB Journal Oct; 3(4): Koperski K, Adhikary J, Han J. Knowledge Discovery in Spatial Databases: Progress and Challenges. Proceedings of SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, Technical Report 96-08, University of British Columbia, Vancouver, Canada, Zhang P, Steinbach M, Kumar V, Shekhar S, Tan P, Klooster S, Potter C. Discovery of Patterns of Earth Science Data using Data Mining, as a chapter to appear in Next Generation of Data Mining Applications, IEEE Press. 2004; Parimala M, Lopez D, Kaspar S. K-Neighbourhood Structural Similarity Approach for Spatial Clustering. Indian Journal of Science and Technology Sep; 8(23): Rajinikanth TV, Balaram VVSSS, Rajasekhar N. Analysis of Indian temperature data using Data mining Techniques. International Conference Advances in Computing and Information Technology May. p Spatial Key Support. Available from: Date accessed: 09/10/2015. Indian Journal of Science and Technology 5