250.CRIMINAL ACTIVITY ANALYSIS OF DIFFERENT TYPES OF THIEVES IN INDIA (1950–2025) USING PROC PRINT | PROC SORT | PROC MEANS | PROC FREQ | PROC SQL | MACROS IN SAS

CRIMINAL ACTIVITY ANALYSIS OF DIFFERENT TYPES OF THIEVES IN INDIA (1950–2025) USING PROC PRINT | PROC SORT | PROC MEANS | PROC FREQ | PROC SQL | MACROS IN SAS

/*Creating a different types of thieves reported across India between 1950 and 2025*/

Step 1: Creating the Dataset

data thieves_india;

    infile datalines dsd dlm=' ' truncover;

    length Thief_Type $20 Region $15 Method_Used $25 Arrested_By $20 

           Items_Stolen $30 Crime_Level $10;

    input Thief_ID 

          Thief_Type :$20. 

          Region :$15. 

          Year_Caught 

          Method_Used :$25. 

          Arrested_By :$20. 

          Items_Stolen :$30. 

          Estimated_Loss 

          Crime_Level :$10.;

datalines;

1 Pickpocket South 1952 "Sneaky Hand" Police "Wallets, Watches" 2000 Minor

2 Highway_Robber North 1963 "Hold at Gunpoint" Army "Cash, Jewelry" 15000 Major

3 Cyber_Thief West 2022 "Phishing Email" Cyber_Crime "Bank Info" 500000 Major

4 Cat_Burglar East 1975 "Rope Climb" Police "Jewels" 12000 Medium

5 Con_Artist Central 1985 "Fake Identity" CID "Cash, Docs" 30000 Medium

6 Temple_Thief South 1993 "Night Break-in" Police "Idols, Donation Box" 70000 Major

7 Political_Thief North 2001 "Fund Misuse" CBI "Public Money" 5000000 Major

8 Corporate_Thief West 2018 "Data Theft" IT_Cell "Trade Secrets" 2000000 Major

9 Bank_Robber East 1970 "Gunpoint Entry" Police "Cash" 250000 Major

10 Smuggler South 1960 "Border Transport" BSF "Gold, Silver" 400000 Major

11 Art_Thief North 1999 "Forgery & Theft" Police "Paintings" 1000000 Major

12 Shoplifter West 2005 "Disguise Method" Local_Police "Electronics" 5000 Minor

13 Gang_Leader East 2011 "Armed Loot" Crime_Branch "Weapons" 800000 Major

14 Serial_Thief Central 1990 "Same Method" Police "Cash, Gold" 45000 Medium

15 Vehicle_Thief South 1987 "Lock Pick" Police "Cars, Bikes" 200000 Medium

16 Train_Thief North 1958 "Moving Robbery" RPF "Luggage, Cash" 10000 Minor

17 Kidnap_Thief West 1973 "Ransom-Based" ATS "Children" 3000000 Major

18 Fraud_Investor East 2023 "Ponzi Scheme" SEBI "Investor Money" 10000000 Major

19 Insider_Thief Central 2015 "Employee Leak" Vigilance "Company Assets" 750000 Major

20 ATM_Hacker South 2017 "Skimming Devices" Cyber_Crime "ATM Cards" 850000 Major

21 Social_Engineer North 2021 "Fake Calls" Cyber_Crime "OTP, Access" 300000 Medium

22 Relic_Thief West 1965 "Antique Loot" Police "Sculptures" 150000 Medium

23 Drug_Courier East 2008 "Hidden Packets" Narcotics "Drugs" 1000000 Major

24 Identity_Thief Central 2019 "Fake PAN/Aadhaar" Cyber_Crime "Identity Docs" 250000 Medium

25 Fake_Saint South 1995 "Blind Belief Scam" CID "Donations, Land" 600000 Major

26 Petrol_Thief North 2010 "Pump Tampering" Local_Police "Fuel" 120000 Medium

27 Forest_Looter East 1982 "Illegal Logging" Forest_Dept "Timber" 350000 Medium

28 Wildlife_Smuggler Central 2003 "Hidden Crates" Forest_Dept "Animals, Skins" 900000 Major

29 Foodgrain_Thief West 1979 "Storage Fraud" Local_Agents "Ration" 100000 Medium

30 Passport_Scammer North 2024 "Fake Visas" Immigration "Passports" 400000 Major

;

run;

proc print data=thieves_india;

run;

Output:

Obs	Thief_Type	Region	Method_Used	Arrested_By	Items_Stolen	Crime_Level	Thief_ID	Year_Caught	Estimated_Loss
1	Pickpocket	South	Sneaky Hand	Police	Wallets, Watches	Minor	1	1952	2000
2	Highway_Robber	North	Hold at Gunpoint	Army	Cash, Jewelry	Major	2	1963	15000
3	Cyber_Thief	West	Phishing Email	Cyber_Crime	Bank Info	Major	3	2022	500000
4	Cat_Burglar	East	Rope Climb	Police	Jewels	Medium	4	1975	12000
5	Con_Artist	Central	Fake Identity	CID	Cash, Docs	Medium	5	1985	30000
6	Temple_Thief	South	Night Break-in	Police	Idols, Donation Box	Major	6	1993	70000
7	Political_Thief	North	Fund Misuse	CBI	Public Money	Major	7	2001	5000000
8	Corporate_Thief	West	Data Theft	IT_Cell	Trade Secrets	Major	8	2018	2000000
9	Bank_Robber	East	Gunpoint Entry	Police	Cash	Major	9	1970	250000
10	Smuggler	South	Border Transport	BSF	Gold, Silver	Major	10	1960	400000
11	Art_Thief	North	Forgery & Theft	Police	Paintings	Major	11	1999	1000000
12	Shoplifter	West	Disguise Method	Local_Police	Electronics	Minor	12	2005	5000
13	Gang_Leader	East	Armed Loot	Crime_Branch	Weapons	Major	13	2011	800000
14	Serial_Thief	Central	Same Method	Police	Cash, Gold	Medium	14	1990	45000
15	Vehicle_Thief	South	Lock Pick	Police	Cars, Bikes	Medium	15	1987	200000
16	Train_Thief	North	Moving Robbery	RPF	Luggage, Cash	Minor	16	1958	10000
17	Kidnap_Thief	West	Ransom-Based	ATS	Children	Major	17	1973	3000000
18	Fraud_Investor	East	Ponzi Scheme	SEBI	Investor Money	Major	18	2023	10000000
19	Insider_Thief	Central	Employee Leak	Vigilance	Company Assets	Major	19	2015	750000
20	ATM_Hacker	South	Skimming Devices	Cyber_Crime	ATM Cards	Major	20	2017	850000
21	Social_Engineer	North	Fake Calls	Cyber_Crime	OTP, Access	Medium	21	2021	300000
22	Relic_Thief	West	Antique Loot	Police	Sculptures	Medium	22	1965	150000
23	Drug_Courier	East	Hidden Packets	Narcotics	Drugs	Major	23	2008	1000000
24	Identity_Thief	Central	Fake PAN/Aadhaar	Cyber_Crime	Identity Docs	Medium	24	2019	250000
25	Fake_Saint	South	Blind Belief Scam	CID	Donations, Land	Major	25	1995	600000
26	Petrol_Thief	North	Pump Tampering	Local_Police	Fuel	Medium	26	2010	120000
27	Forest_Looter	East	Illegal Logging	Forest_Dept	Timber	Medium	27	1982	350000
28	Wildlife_Smuggler	Central	Hidden Crates	Forest_Dept	Animals, Skins	Major	28	2003	900000
29	Foodgrain_Thief	West	Storage Fraud	Local_Agents	Ration	Medium	29	1979	100000
30	Passport_Scammer	North	Fake Visas	Immigration	Passports	Major	30	2024	400000

Step 2: PROC PRINT — View the Dataset

proc print data=thieves_india;

    title "Complete Thief Dataset from 1950 to 2025";

run;

Output:

Complete Thief Dataset from 1950 to 2025

Obs	Thief_Type	Region	Method_Used	Arrested_By	Items_Stolen	Crime_Level	Thief_ID	Year_Caught	Estimated_Loss
1	Pickpocket	South	Sneaky Hand	Police	Wallets, Watches	Minor	1	1952	2000
2	Highway_Robber	North	Hold at Gunpoint	Army	Cash, Jewelry	Major	2	1963	15000
3	Cyber_Thief	West	Phishing Email	Cyber_Crime	Bank Info	Major	3	2022	500000
4	Cat_Burglar	East	Rope Climb	Police	Jewels	Medium	4	1975	12000
5	Con_Artist	Central	Fake Identity	CID	Cash, Docs	Medium	5	1985	30000
6	Temple_Thief	South	Night Break-in	Police	Idols, Donation Box	Major	6	1993	70000
7	Political_Thief	North	Fund Misuse	CBI	Public Money	Major	7	2001	5000000
8	Corporate_Thief	West	Data Theft	IT_Cell	Trade Secrets	Major	8	2018	2000000
9	Bank_Robber	East	Gunpoint Entry	Police	Cash	Major	9	1970	250000
10	Smuggler	South	Border Transport	BSF	Gold, Silver	Major	10	1960	400000
11	Art_Thief	North	Forgery & Theft	Police	Paintings	Major	11	1999	1000000
12	Shoplifter	West	Disguise Method	Local_Police	Electronics	Minor	12	2005	5000
13	Gang_Leader	East	Armed Loot	Crime_Branch	Weapons	Major	13	2011	800000
14	Serial_Thief	Central	Same Method	Police	Cash, Gold	Medium	14	1990	45000
15	Vehicle_Thief	South	Lock Pick	Police	Cars, Bikes	Medium	15	1987	200000
16	Train_Thief	North	Moving Robbery	RPF	Luggage, Cash	Minor	16	1958	10000
17	Kidnap_Thief	West	Ransom-Based	ATS	Children	Major	17	1973	3000000
18	Fraud_Investor	East	Ponzi Scheme	SEBI	Investor Money	Major	18	2023	10000000
19	Insider_Thief	Central	Employee Leak	Vigilance	Company Assets	Major	19	2015	750000
20	ATM_Hacker	South	Skimming Devices	Cyber_Crime	ATM Cards	Major	20	2017	850000
21	Social_Engineer	North	Fake Calls	Cyber_Crime	OTP, Access	Medium	21	2021	300000
22	Relic_Thief	West	Antique Loot	Police	Sculptures	Medium	22	1965	150000
23	Drug_Courier	East	Hidden Packets	Narcotics	Drugs	Major	23	2008	1000000
24	Identity_Thief	Central	Fake PAN/Aadhaar	Cyber_Crime	Identity Docs	Medium	24	2019	250000
25	Fake_Saint	South	Blind Belief Scam	CID	Donations, Land	Major	25	1995	600000
26	Petrol_Thief	North	Pump Tampering	Local_Police	Fuel	Medium	26	2010	120000
27	Forest_Looter	East	Illegal Logging	Forest_Dept	Timber	Medium	27	1982	350000
28	Wildlife_Smuggler	Central	Hidden Crates	Forest_Dept	Animals, Skins	Major	28	2003	900000
29	Foodgrain_Thief	West	Storage Fraud	Local_Agents	Ration	Medium	29	1979	100000
30	Passport_Scammer	North	Fake Visas	Immigration	Passports	Major	30	2024	400000

Step 3: PROC SORT — Organizing the Data

proc sort data=thieves_india out=sorted_thieves;

    by Region Year_Caught;

run;

proc print data=sorted_thieves;

    title "Sorted Thieves by Region and Year Caught";

run;

Output:

Sorted Thieves by Region and Year Caught

Obs	Thief_Type	Region	Method_Used	Arrested_By	Items_Stolen	Crime_Level	Thief_ID	Year_Caught	Estimated_Loss
1	Con_Artist	Central	Fake Identity	CID	Cash, Docs	Medium	5	1985	30000
2	Serial_Thief	Central	Same Method	Police	Cash, Gold	Medium	14	1990	45000
3	Wildlife_Smuggler	Central	Hidden Crates	Forest_Dept	Animals, Skins	Major	28	2003	900000
4	Insider_Thief	Central	Employee Leak	Vigilance	Company Assets	Major	19	2015	750000
5	Identity_Thief	Central	Fake PAN/Aadhaar	Cyber_Crime	Identity Docs	Medium	24	2019	250000
6	Bank_Robber	East	Gunpoint Entry	Police	Cash	Major	9	1970	250000
7	Cat_Burglar	East	Rope Climb	Police	Jewels	Medium	4	1975	12000
8	Forest_Looter	East	Illegal Logging	Forest_Dept	Timber	Medium	27	1982	350000
9	Drug_Courier	East	Hidden Packets	Narcotics	Drugs	Major	23	2008	1000000
10	Gang_Leader	East	Armed Loot	Crime_Branch	Weapons	Major	13	2011	800000
11	Fraud_Investor	East	Ponzi Scheme	SEBI	Investor Money	Major	18	2023	10000000
12	Train_Thief	North	Moving Robbery	RPF	Luggage, Cash	Minor	16	1958	10000
13	Highway_Robber	North	Hold at Gunpoint	Army	Cash, Jewelry	Major	2	1963	15000
14	Art_Thief	North	Forgery & Theft	Police	Paintings	Major	11	1999	1000000
15	Political_Thief	North	Fund Misuse	CBI	Public Money	Major	7	2001	5000000
16	Petrol_Thief	North	Pump Tampering	Local_Police	Fuel	Medium	26	2010	120000
17	Social_Engineer	North	Fake Calls	Cyber_Crime	OTP, Access	Medium	21	2021	300000
18	Passport_Scammer	North	Fake Visas	Immigration	Passports	Major	30	2024	400000
19	Pickpocket	South	Sneaky Hand	Police	Wallets, Watches	Minor	1	1952	2000
20	Smuggler	South	Border Transport	BSF	Gold, Silver	Major	10	1960	400000
21	Vehicle_Thief	South	Lock Pick	Police	Cars, Bikes	Medium	15	1987	200000
22	Temple_Thief	South	Night Break-in	Police	Idols, Donation Box	Major	6	1993	70000
23	Fake_Saint	South	Blind Belief Scam	CID	Donations, Land	Major	25	1995	600000
24	ATM_Hacker	South	Skimming Devices	Cyber_Crime	ATM Cards	Major	20	2017	850000
25	Relic_Thief	West	Antique Loot	Police	Sculptures	Medium	22	1965	150000
26	Kidnap_Thief	West	Ransom-Based	ATS	Children	Major	17	1973	3000000
27	Foodgrain_Thief	West	Storage Fraud	Local_Agents	Ration	Medium	29	1979	100000
28	Shoplifter	West	Disguise Method	Local_Police	Electronics	Minor	12	2005	5000
29	Corporate_Thief	West	Data Theft	IT_Cell	Trade Secrets	Major	8	2018	2000000
30	Cyber_Thief	West	Phishing Email	Cyber_Crime	Bank Info	Major	3	2022	500000

Step 4: PROC FREQ — Frequency Analysis

Frequency of Thief Types:

proc freq data=thieves_india;

    tables Thief_Type;

    title "Frequency of Different Thief Types";

run;

Output:

Frequency of Different Thief Types

The FREQ Procedure

Thief_Type	Frequency	Percent	Cumulative Frequency	Cumulative Percent
ATM_Hacker	1	3.33	1	3.33
Art_Thief	1	3.33	2	6.67
Bank_Robber	1	3.33	3	10.00
Cat_Burglar	1	3.33	4	13.33
Con_Artist	1	3.33	5	16.67
Corporate_Thief	1	3.33	6	20.00
Cyber_Thief	1	3.33	7	23.33
Drug_Courier	1	3.33	8	26.67
Fake_Saint	1	3.33	9	30.00
Foodgrain_Thief	1	3.33	10	33.33
Forest_Looter	1	3.33	11	36.67
Fraud_Investor	1	3.33	12	40.00
Gang_Leader	1	3.33	13	43.33
Highway_Robber	1	3.33	14	46.67
Identity_Thief	1	3.33	15	50.00
Insider_Thief	1	3.33	16	53.33
Kidnap_Thief	1	3.33	17	56.67
Passport_Scammer	1	3.33	18	60.00
Petrol_Thief	1	3.33	19	63.33
Pickpocket	1	3.33	20	66.67
Political_Thief	1	3.33	21	70.00
Relic_Thief	1	3.33	22	73.33
Serial_Thief	1	3.33	23	76.67
Shoplifter	1	3.33	24	80.00
Smuggler	1	3.33	25	83.33
Social_Engineer	1	3.33	26	86.67
Temple_Thief	1	3.33	27	90.00
Train_Thief	1	3.33	28	93.33
Vehicle_Thief	1	3.33	29	96.67
Wildlife_Smuggler	1	3.33	30	100.00

Frequency by Region:

proc freq data=thieves_india;

    tables Region;

    title "Thief Cases by Region";

run;

Output:

Thief Cases by Region

The FREQ Procedure

Region	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Central	5	16.67	5	16.67
East	6	20.00	11	36.67
North	7	23.33	18	60.00
South	6	20.00	24	80.00
West	6	20.00	30	100.00

Crime Level Distribution:

proc freq data=thieves_india;

    tables Crime_Level / nocum;

    title "Distribution of Crime Severity";

run;

Output:

Distribution of Crime Severity

The FREQ Procedure

Crime_Level	Frequency	Percent
Major	17	56.67
Medium	10	33.33
Minor	3	10.00

Step 5: PROC MEANS — Estimated Loss Analysis

proc means data=thieves_india mean sum maxdec=0;

    var Estimated_Loss;

    class Crime_Level;

    title "Summary of Losses by Crime Level";

run;

Output:

Summary of Losses by Crime Level

The MEANS Procedure

Analysis Variable : Estimated_Loss
Crime_Level	N Obs	Mean	Sum
Major	17	1619706	27535000
Medium	10	155700	1557000
Minor	3	5667	17000

Step 6: PROC SQL — Advanced Data Analysis

Top 5 Highest Loss Thefts:

proc sql outobs=5;

    title "Top 5 High Loss Thefts";

    select Thief_Type, Region, Estimated_Loss 

    from thieves_india 

    order by Estimated_Loss desc;

quit;

Output:

Top 5 High Loss Thefts

Thief_Type	Region	Estimated_Loss
Fraud_Investor	East	10000000
Political_Thief	North	5000000
Kidnap_Thief	West	3000000
Corporate_Thief	West	2000000
Art_Thief	North	1000000

Average Loss by Region:

proc sql;

    title "Average Loss by Region";

    select Region, avg(Estimated_Loss) as Avg_Loss format=comma12. 

    from thieves_india 

    group by Region;

quit;

Output:

Average Loss by Region

Region	Avg_Loss
Central	395,000
East	2,068,667
North	977,857
South	353,667
West	959,167

Thieves Caught After 2000:

proc sql;

    title "Thieves Caught After 2000";

    select Thief_Type, Year_Caught, Crime_Level 

    from thieves_india 

    where Year_Caught > 2000;

quit;

Output:

Thieves Caught After 2000

Thief_Type	Year_Caught	Crime_Level
Cyber_Thief	2022	Major
Political_Thief	2001	Major
Corporate_Thief	2018	Major
Shoplifter	2005	Minor
Gang_Leader	2011	Major
Fraud_Investor	2023	Major
Insider_Thief	2015	Major
ATM_Hacker	2017	Major
Social_Engineer	2021	Medium
Drug_Courier	2008	Major
Identity_Thief	2019	Medium
Petrol_Thief	2010	Medium
Wildlife_Smuggler	2003	Major
Passport_Scammer	2024	Major

Step 7: Creating a Macro — Crime Summary by Region

%macro crime_by_region(region_name);

    proc sql;

        title "Crimes in &region_name Region";

        select Thief_Type, Year_Caught, Method_Used, Estimated_Loss, Crime_Level

        from thieves_india

        where Region = "&region_name";

    quit;

%mend;

%crime_by_region(North);

Output:

Crimes in North Region

Thief_Type	Year_Caught	Method_Used	Estimated_Loss	Crime_Level
Highway_Robber	1963	Hold at Gunpoint	15000	Major
Political_Thief	2001	Fund Misuse	5000000	Major
Art_Thief	1999	Forgery & Theft	1000000	Major
Train_Thief	1958	Moving Robbery	10000	Minor
Social_Engineer	2021	Fake Calls	300000	Medium
Petrol_Thief	2010	Pump Tampering	120000	Medium
Passport_Scammer	2024	Fake Visas	400000	Major

%crime_by_region(South);

Output:

Crimes in South Region

Thief_Type	Year_Caught	Method_Used	Estimated_Loss	Crime_Level
Pickpocket	1952	Sneaky Hand	2000	Minor
Temple_Thief	1993	Night Break-in	70000	Major
Smuggler	1960	Border Transport	400000	Major
Vehicle_Thief	1987	Lock Pick	200000	Medium
ATM_Hacker	2017	Skimming Devices	850000	Major
Fake_Saint	1995	Blind Belief Scam	600000	Major

%crime_by_region(West);

Output:

Crimes in West Region

Thief_Type	Year_Caught	Method_Used	Estimated_Loss	Crime_Level
Cyber_Thief	2022	Phishing Email	500000	Major
Corporate_Thief	2018	Data Theft	2000000	Major
Shoplifter	2005	Disguise Method	5000	Minor
Kidnap_Thief	1973	Ransom-Based	3000000	Major
Relic_Thief	1965	Antique Loot	150000	Medium
Foodgrain_Thief	1979	Storage Fraud	100000	Medium

%crime_by_region(East);

Output:

Crimes in East Region

Thief_Type	Year_Caught	Method_Used	Estimated_Loss	Crime_Level
Cat_Burglar	1975	Rope Climb	12000	Medium
Bank_Robber	1970	Gunpoint Entry	250000	Major
Gang_Leader	2011	Armed Loot	800000	Major
Fraud_Investor	2023	Ponzi Scheme	10000000	Major
Drug_Courier	2008	Hidden Packets	1000000	Major
Forest_Looter	1982	Illegal Logging	350000	Medium

%crime_by_region(Central);

Output:

Crimes in Central Region

Thief_Type	Year_Caught	Method_Used	Estimated_Loss	Crime_Level
Con_Artist	1985	Fake Identity	30000	Medium
Serial_Thief	1990	Same Method	45000	Medium
Insider_Thief	2015	Employee Leak	750000	Major
Identity_Thief	2019	Fake PAN/Aadhaar	250000	Medium
Wildlife_Smuggler	2003	Hidden Crates	900000	Major

Step 8: Additional Macro — Count by Crime Level

%macro count_by_level(level);

    proc sql;

        title "Total Thieves by &level Crime Level";

        select count(*) as Total_Thieves, sum(Estimated_Loss) as Total_Loss format=comma12.

        from thieves_india

        where Crime_Level = "&level";

    quit;

%mend;

%count_by_level(Minor);

Output:

Total Thieves by Minor Crime Level

Total_Thieves	Total_Loss
3	17,000

%count_by_level(Medium);

Output:

Total Thieves by Medium Crime Level

Total_Thieves	Total_Loss
10	1,557,000

%count_by_level(Major);

Output:

Total Thieves by Major Crime Level

Total_Thieves	Total_Loss
17	27,535,000

Step 9: Custom Report — Loss Over Time

proc sql;

    title "Total Estimated Loss by Decade";

    select floor(Year_Caught/10)*10 as Decade,

           sum(Estimated_Loss) as Total_Loss format=comma12.

    from thieves_india

    group by calculated Decade

    order by Decade;

quit;

Output:

Total Estimated Loss by Decade

Decade	Total_Loss
1950	12,000
1960	565,000
1970	3,362,000
1980	580,000
1990	1,715,000
2000	6,905,000
2010	4,770,000
2020	11,200,000

To Visit My Previous E-Commerce Dataset:Click Here

To Visit My Previous Length,Input,Retain Statements:Click Here

To Visit My Previous Urban Traffic Dataset:Click Here

To Visit My Previous Home Energy Consumption Dataset:Click Here

--->PLEASE FOLLOW OUR BLOG FOR MORE INFORMATION.

--->PLEASE DO COMMENTS AND SHARE OUR BLOG.

PLEASE FOLLOW OUR TELEGRAM CHANNEL CLICK HERE

PLEASE FOLLOW OUR FACEBOOK PAGE  CLICK HERE

PLEASE FOLLOW OUR INSTAGRAM PAGE CLICK HERE