Predictive Policing- The Future of Law Enforcement in the Trinidad and Tobago Police Service

Transcription

1 Predictive Plicing- The Future f Law Enfrcement in the Trinidad and Tbag Plice Service Andre Nrtn T & T Plice Service Crner Edward & Sackville Streets POS, Trinidad WI ABSTRACT The Trinidad and Tbag Plice Service (TTPS) is currently faced with large vlumes f criminal data that cntinues t grw daily and which are required t be prcessed and transfrmed int useful infrmatin and where data mining can greatly imprve crime analysis and aid in preventing and reducing crime. Currently, crime analysts attached t the analytical department f the TTPS are required t unravel the cmplexities in data t assist peratinal persnnel in arresting ffenders and als in directing crime preventin strategies. With the current vlume f crime being cmmitted and the awareness f mdern criminals, this is becming a very daunting task. The ability t analyze huge vlumes f data with its inherent cmplexities withut the use f cmputatinal supprt puts a strain n human resurces. Because f the speed and advances in the field f data mining within recent years, independent studies n its impact n plicing are nly nw getting n the way. It is particularly imprtant in this respect t examine the benefits which the TTPS can derive thrugh a careful implementatin f this technlgy. The infamus events f July, 1990 in Trinidad and Tbag heralded the need fr predictive plicing and exacerbated cncerns abut natinal security by the lcal law enfrcement agency. Accurately and efficiently analyzing the rganizatin s ever grwing vlume f crime data is a majr challenge facing the TTPS. This paper presents a case fr implementing data mining (knwledge discvery in databases -KDD) within the TTPS as a tl fr predictive analytics f crime data. It is hped that this technlgy will prvide decisin makers with intelligence frm the crime data t infrm their strategic planning. It discusses the challenges f implementing data mining with special discussin f key issues relating t data integrity and the infrmatin technlgy (IT) infrastructure required t supprt data mining. It cncludes by suggesting the internal infrmatin technlgy (IT) infrastructural changes needed t facilitate its implementatin in the TTPS. General Terms Crime analysis, technlgical capabilities, law enfrcement, fraud patterns, mney laundering. Keywrds Predictive plicing, Data Mining, Artificial Intelligence, Unifrm Crime Recrding System (UCR) 1. INTRODUCTION In Trinidad and Tbag, cncerns abut natinal security have increased significantly since the lcally attempted terrrist acts f July 27 th Mst agencies charged with the respnsibility fr intelligence gathering have been actively cllecting dmestic intelligence t prevent any future attempts at insurrectin. These effrts have significantly mtivated the primary lcal law enfrcement agency (the Trinidad and Tbag Plice Service TTPS) t mre clsely mnitr criminal activities in all f its jurisdictins. Despite having adpted a cmputerized recrds management system and gegraphic infrmatin systems, the rganizatin s ability t rganize and assemble data abut crime and disrder have nt sared. In sme aspects ur technlgical capabilities have grwn faster than ur capacity t understand. The rganizatin s ability t accurately and efficiently analyze the grwing vlumes f crime data pses a great challenge. Trinidad and Tbag is a twin-island republic with an estimated ppulatin size f 1.3 millin peple [1] with a plice agency (TTPS) cmprising apprximately 6,400 members [2]. In spite f in-huse crime analysts, the rganizatin is yet t explre data mining as a viable ptin fr acquiring predictive capabilities frm its vast strage f crime data. Research has shwn that data mining can greatly imprve crime analysis and aid in reducing and preventing crime. As a matter f fact, n field is in greater need f data mining technlgy than law enfrcement. One ptential area f applicatin is spatial data mining tls which prvides law enfrcement agencies with significant capabilities t learn crime trends n where, hw and why crimes are cmmitted. An early applicatin f data mining in law enfrcement was by the FBI in the investigatin f the Oklahma City bmbing case [3]. They adpted data mining techniques t scrutinize large vlume f data gathered frm varius surces t track dwn criminals. Similarly, the Treasury Department f the United States adpted data mining technlgy t extract suspicius mney laundering r fraud patterns [4]. This is particularly aiming at detecting criminals invlved in mney laundering r fraud. The safety and security f citizens remains a majr cncern fr the TTPS, hwever rather than fcusing n enfrcement and incarceratin, the rganizatin can deter crime thrugh the knwledge benefits that derive frm infrmatin and its assciated technlgies. By emplying the crrect technlgies the rganizatin can turn its fficers int effective prblem slvers by leveraging their intellectual capital t pre-empt crime. One such technlgy is data mining. 2. KEY DEFINITIONS 2.1 What is Predictive Plicing Predictive plicing is a cncept that is built n the premise that it is pssible t predict when and where crimes will ccur 32

2 again in the future by using sphisticated cmputer analysis f infrmatin abut previusly cmmitted crimes [5]. 2.2 What is Data Mining? In its simplest definitin, data mining is the prcess f extracting patterns frm data. It is the prcess f discvering interesting knwledge frm large amunts f data stred either in databases, data warehuses r ther infrmatin repsitries. Crime data mining can be sub-divided int the fllwing techniques: Clustering Classificatin Deviatin Detectin Scial Netwrk Analysis Entity Extractin Assciatin Rule Mining String Cmparatr Sequential Pattern Mining A single data mining tl r technique is nt equally applicable t all the abve-mentined tasks. Based n the nature f the prblem under cnsideratin, and its prximity t the main divisins f the data mining techniques, chice f the apprpriate technique is essential [6]. 3. CRIME RECORDING PROCESS In the Trinidad and Tbag Plice Service crime statistics is cmpiled using the Federal Bureau f Investigatins (FBI) Unifrm Recrding System (UCR). UCR s successr, the Natinal Incident Based Recrding System (NIBRS) is yet t be implemented lcally. There exist varius specialist sectins which investigate different crime types. Fr example all fraud matters are investigated by a specialized fraud unit; likewise stlen vehicles have a specialized investigative unit. The ubiquitus nature f cmputing and the Internet s pervasiveness likewise makes ffences such as identity theft, netwrk intrusin, cyber piracy, and ther illicit cmputermediated activities a new challenge fr the TTPS. Crime reprts are dcumented n rganizatinal specific general ccurrence incident reprt (GOIR) frms which cnsists f structured fields (tick bxes) as well as an unstructured free frm text area. Sme f the key variables fund n GOIR frms are day, date and time f crime (tempral dimensins), lcatin f crime (spatial dimensins), ffence type, victim and suspect data as well as mdus perandi (MO) which identifies hw the crime was cmmitted. MO is dcumented in the unstructured free frm text area f the crime frm. There are apprximately 400 unifrm UCR cdes. The cntents f each frm are manually entered int a centralized recrds management system (RMS) by data entry clerks. 4. CHALLENGES TO DATA MINING IN TTPS At present, the greatest impediment t implementing data mining as a predictive plicing tl within the TTPS is because f the fllwing reasns: A large percentage f the data in the database is incmplete and invalid The data cntain fields that requires parsing (elementizing) by being split int smaller parts befre being able t use data mining tls n the database The data cntain several abbreviatins which shuld be changed t ensure cnsistency thrughut the database (data standardizatin is needed) A significant prtin f the data is incrrect There exists redundancy in the data (duplicate data stred at different lcatins in the database) The database have huge amunts f free frm text that needs t be indexed and classified t be useful fr data mining The data des nt suitably enugh match the business rules f the rganizatin and there exist missing values, incnsistent values and invalid relatinships There are several different frms f data that need t be cnverted t a single frm f cnsistency in the database In additin t these shrtcmings, the database which supplies the raw data is dynamic (subject t update, append, delete etc.), which by default pses prblems. Other unique prblems arise as a cnsequence f these inadequacies and irrelevance f sme f the stred infrmatin. This existing database like mst databases was nt designed t supprt data mining and as such, thse essential attributes fr knwledge discvery f the applicatin dmain are absent frm the data, making it very difficult t discver significant knwledge abut the given dmain. The quality f the data set is dependent upn a number f issues, but the surce f the data is the crucial factr. Data entry and acquisitin is inherently prne t errrs bth simple and cmplex. Much effrt can be given t this frnt-end prcess, with respect t reductin in entry errr, but the fact ften remains that errrs in a large data set are cmmn [7]. Unless the rganizatin takes extreme measures in an effrt t avid data errrs the field errrs rates will typically cntinue. The current system fr cleaning the data is mre f a manual prcess which is labrius, time cnsuming and itself prne t errrs. Analyzing pr quality data prduces very little intelligence fr the rganizatin when designing its peratinal strategies fr manpwer deplyment. One lgical slutin fr imprving data integrity is by incrprating it as part f the rganizatin s business practices thereby reducing the need t cnsistently clean nisy data [8]. Hwever, this pre-prcessing f data as a rutine task usually cnsumes much f the effrts exerted in the entire data mining prcess. Therefre the ptential t increase the usefulness f data by cmbining it with ther data surces is great, but if the underlying data is nt accurate, any relatinships fund in the data may be misleading. A significant amunt f disparately held databases exist amng individual departments which require cnslidatin. Only the peratinal capabilities f gegraphical infrmatin systems (GIS) are being utilized. Hwever where the administrative and strategic capabilities f the sftware is in greatest demand, the necessary skill set required fr such utilizatin is in shrtage acrss the wider rganizatin. 5. DATA MINING TECHNIQUES FOR CRIME PREDICTION Newer data mining techniques when applied t law enfrcement identifies patterns frm bth structured and unstructured data. Similar t ther frms f data mining that exist in the fr prfit wrld, there are privacy issues 33

3 assciated with crime data mining. Hwever researchers and develpers have created several autmated data mining techniques specifically suited t plice agencies [9]. The fllwing are cmmn crime data mining techniques designed fr law enfrcement: Clustering des nt have a set f pre-defined classes fr assigning items. They grup data int classes with similar characteristics in rder t maximize r minimize intra-class similarity. This culd invlve identifying suspects wh cnduct crimes in similar ways r distinguish amng grups belnging t different gangs. Smetimes a statistic based cncept space algrithm is used which autmatically assciate different bjects e.g. persns, rganizatins and vehicles in crime recrds. Clustering crime incidents has a high cmputatinal intensity but autmates a majr part f crime analysis. Classificatin has been used t identify surces f spamming based upn the sender s structural features and linguistic patterns. Classificatin lks fr cmmn features between crime entities and rganizes them int predefined classes. It is ften used t predict crime trends and reduce the time required t identify crime entities but requires a predefined classificatin scheme. It als requires reasnably cmplete training and testing data because a high degree f missing data wuld limit predictin accuracy. Deviatin detectin is als referred t as utlier detectin, uses specific measures t study data that differs markedly frm the rest f the data. This technique is cmmnly applied t fraud detectin, netwrk intrusin detectin, and ther crime analyses, thugh in sme instances these anmalies may be nrmal and thereby adding a further layer f cmplexity making it difficult t identify utliers. Scial netwrk analysis describes the rles f and interactins amng ndes in a cnceptual netwrk. This technique can be used by investigatrs t build a netwrk that illustrates criminals rles, the flw f intangible and tangible infrmatin and entity assciatins. In spite f this technique enables investigatrs t visualize criminal netwrks, the discvery f the netwrk s true leaders may be undiscverable especially if such leaders are lw prfiled. Entity extractin identifies particular patterns frm data such as text, images, r audi materials. It has been used t autmatically identify persns, addresses, vehicles, and persnal characteristics frm plice narrative reprts. Entity extractin prvides basic infrmatin fr crime analysis, but its perfrmance depends greatly n the availability f extensive amunts f clean input data. Assciatin rule mining discvers frequently ccurring item sets in a database and presents the patterns as rules. This technique has been applied in netwrk intrusin detectin t derive assciatin rules frm users interactin histry. Investigatrs als can apply this technique t netwrk intruders prfiles t help detect ptential future netwrk attacks. String cmparatr techniques perfrm a cmparisn between textual fields in pairs f database recrds and cmpute the similarity between the recrds. These techniques can detect deceptive infrmatin such as name, address, and Identificatin Number in criminal recrds. Investigatrs can use string cmparatrs t analyze textual data, but the techniques ften require intensive cmputatin. Sequential pattern mining is similar t assciatin rule mining and this technique finds frequently ccurring sequences f items ver a set f transactins that ccurred at different times. This apprach identifies intrusin patterns in netwrk intrusin detectin amng time-stamped data. Shwing hidden patterns benefits crime analysis, but t btain meaningful results requires rich and highly structured data. 5.1 Phases f Data Mining Data preparatin phase- In this phase, the main data sets t be used by the data mining peratin are identified and cleansed frm any data impurities (data cleaning). If the data is in a data warehuse it will be already integrated and filtered, if nt then it must be extracted frm the peratinal database and cleaned befre utilizing the data mining tls n the newly cleaned data. The data analysis and classificatin phase- The bjective f this phase is t study the data in rder t identify cmmn data characteristics r patterns. The data mining tls applies specific algrithms t find the fllwing: Data grupings, classificatins, clusters, r sequences. Data dependencies, links, r relatinships. Data patterns, trends, and deviatins. The knwledge acquisitin phase- This phase uses the results f the data analysis and classificatin phase. End user interventin selects the apprpriate mdeling r knwledge acquisitin algrithms. Typically the algrithms used in data mining are based n neural netwrks, decisin trees, rules inductin, genetic algrithms, classificatin and regressin trees, memry-based reasning, r nearest neighbr and data visualizatin. Usually a data mining tl may use multiple algrithms in any cmbinatin t generate a cmputer mdel that reflects the behavir f the target data set. The prgnsis phase- In this phase, the data mining findings are used t predict future behavir and frecast ptential rganizatinal utcmes. Examples f data mining findings can be as fllws: 80% f Burglaries are cmmitted by individuals wh jurneyed t cmmit the 34

4 crime at a lcatin distant frm their hmes. 55% f street Rbberies was cmmitted by persns f mixed descent between the ages f yrs. frm single parent husehlds. 75% f stlen vehicles recvered were intact. The cmplete set f findings can be represented in a decisin tree, a neural netwrk, a frecasting mdel r a visual presentatin interface which is then used t prject future events r results. Fr example the prgnsis phase may prject the likely utcme f a new crime plan rll-ut. By uncvering critical patterns and variables that can be indicatrs f future crime activity, the TTPS can practively recgnize and react t threats t public safety befre they take place [10]. 6. A CASE FOR DATA MINING Data mining is ne f the fastest grwing fields in the cmputer industry. Once a small interest area within cmputer science and statistics, it has quickly expanded int a field f its wn. At present, mst f the decisin making prcess fr peratinal deplyment t crime prne areas is nt supprted by superir tls and techniques which can prvide actinable intelligence frm the stred crime data. Cnsequently, resurce deplyment, crime preventin and investigatin strategies are being pursued mre n the basis f crime incidents rather than crime patterns and trends. This is a clear indicatin that the current apprach tward crime preventin takes mre a reactive than practive apprach. Data Mining will facilitate much mre granularity fr analyzing trends and pattern, which when cmbined with criminlgical theries can ptentially prvide the desired predictive capabilities t the rganizatin. The advanced algrithms used by data mining technlgies can allw the TTPS t better predict lcatins where crimes are likely t ccur and direct apprpriate resurces t thse areas. This effectively stps crimes befre they ccur. Despite the extensive use f the TTPS criminal database (RMS) by the agency s crime analysts t prvide analytical prducts fr decisin making, the absence f an infrmatin technlgy infrastructure which supprts analytical prcessing has thus far shrt-changed the rganizatin in realizing its predictive plicing capability (a practive plicing methdlgy) which is much need. Currently, much emphasis is placed n cunting crime and incidents. The crime predictin capabilities achievable frm data mining presents the rganizatin with pprtunities t mve frm merely cunting crimes mre tward anticipating, preventing and perhaps respnding mre effectively t their ccurrence. This fcuses the rganizatin mre n effective use f its stred data t develp deplyment strategies because criminal behavir tends t be relatively predictable which can be determined thrugh analyzing histrical data. Greater predictive capability is achievable utilizing data mining tls t explit existing data sets in rder t prvide mre actinable intelligence frm the stred crime data. 7. CONCLUSION Predictive plicing is slwing making way t the frefrnt f strategic law enfrcement and many plicing experts believe that it may be a prminent directin in the future. It invlves anticipating questins which leads t a search fr evidence, evidence helps fficers establish facts and facts in turn supprt and substantiate actin [11]. Many experts believe that predictive plicing is the future f law enfrcement. Predictive plicing techniques when supprted by the right tls can empwer the TTPS t practively fight crime by facilitating better frecasts f future incidents and events thrugh the fast, accurate detentin f patterns and trends frm its large vlumes f histrical data residing in the RMS [12]. With the advanced predictive mdeling slutins fund in data mining, the rganizatin can have the tls required t be in frnt f crime and stp criminals in their tracks, perate mre cst effectively and bts public trust and cnfidence in the rganizatin [13]. A cmbining f data mining techniques with dashbards, screcards, data visualizatin, interactive mapping, gespatial analysis and enterprise search, the TTPS can vercme budgetary challenges and cmbat crime mre successfully than befre. In rder t achieve the afrementined benefits it is imperative fr the TTPS re-engineer the fllwing: Data Cllectin- There is need t imprve the current data cllectin methdlgy where data integrity becmes built-in t the internal business prcesses f the rganizatin; IT Infrastructure upgrade- TTPS needs t upgrade the infrmatin technlgy infrastructure t facilitate predictive analytics technlgies; Data Structure- All transactinal criminal must be extracted frm its peratinal envirnment, transfrmed int a data structure that facilitates analytical prcessing and stred electrnically in a preferably nn-vlatile strage envirnment (e.g. a data warehuse); Data Integratin- All disparate databases husing criminal and intelligence data must be integrated int this cmmn data structure [14]. Other essential attributes frm Curt and Prcess data, CID/CRO data, prisn release data and GIS data are required t cmplete this cmmn structure. It is imprtant fr plice fficers and crime analysts within TTPS t mve the science frward, making predictins and acting n the results [15]. The gal f the rganizatin shuld always be t reduce crime and imprve service. Implementing data mining technlgies as a predictive plicing tl makes TTPS the pineer ( first mver ) plice agency amng Caricm member states t adpt the technique, thereby setting the stage fr thers t fllw. 8. ACKNOWLEDGMENTS I wuld like t pay special tribute t Dr. Christpher Ward, my supervisr at the University f the West Indies St. 35

5 Augustine Campus Trinidad WI, wh inspired me t write this paper. 9. REFERENCES [1] Central statistical ffice Ministry f Planning and Develpment, Gvernment f the Republic f Trinidad and Tbag. [2] Human Resurce Branch f the Trinidad and Tbag Plice Service (HRB-TTPS) [3] The Federal Bureau f Investigatin, Terrr Hits Hme: The Oklahma City Bmbing [4] Federal Agency Data Mining Reprt 2010, Department f Treasury January [5] Uchida, C Natinal Institute f Justice: A Natinal Discussin n Predictive Plicing: Defining ur Terms and Mapping Successful Implementatin Strategies NJC [6] Adderley, R & P.B. Musgrve Data Mining Case Study: Mdeling the Behavir f Offenders Wh Cmmit Serius Sexual Assaults, Prceedings f the seventh ACM SIGKDD internatinal cnference n Knwledge discvery and data mining. [7] Brwn, D., 2003.The Reginal Crime Analysis Prgram (RECAP): A Framewrk fr Mining Data t Catch Criminals. [8] Butler, A Plice Management 2 nd Editin, England: Dartmuth Publishing Cmpany Limited. [9] Franklin, D Data Miners: New sftware instantly cnnects key bits f data that nce eluded teams f researchers. Time, December 23. [10] Helberg, C Data mining with cnfidence, 2 nd Editin, SPSS, Inc., Chicag, IL. [11] Mena, J Investigative Data Mining fr Security and Criminal Detectin, Elsevier Science (USA). [12] Mc Cue, C. et al, Data Mining and value-added analysis, FBI Law Enfrcement Bulletin, [13] Mc Cue, C Data Mining and Predictive Analysis: Intelligence gathering and Crime Analysis, Butterwrth- Heinemann. [14] Perez, B Data Mining Technlgy Use Grws; pdf [15] Tabussum, Z CIA turns t data mining; rns_t.html 36