Using Census Data, District Data, with GIS, SPSS, and Answer Tree to Identify possible populations to market to, and increase enrollments

Using Census Data, District Data, with GIS, SPSS, and Answer Tree to Identify possible populations to market to, and increase enrollments Presented by Keith Wurtz Senior Research Analyst Chaffey Community College Keith.wurtz@chaffey.edu

Introduction How to Create a District Map? How to Merge Census Data into a GIS Map? Using Census Data and District Data to Identify possible populations to market to, and increase enrollments

Chaffey College District San Antonio Heights Upland Rancho Cucamonga Fontana Montclair Ontario Chino Chino Hills Prepared by Keith Wurtz Date: 20060406 0 1.5 3 6 Miles

How do I create a map of my District in GIS? Open ArcMap Click on the + sign to add data The data that you want to first add is the form of shape files Shape files are the type of files the GIS uses to create maps Since Chaffey s District is in San Bernardino County (#71) I am going to start with shape files from that county Shape File Types BLK Data by Census Blocks GRP Data by Block Groups TRT Census Tract ZCTA Zip Code Place Cities CTY County LKA - Streets To create the map on the previous slide I am going to choose the Place file or data by City (i.e. tgr06071place00.shp) and the ZCTA file or zip code data (i.e. tgr06071zcta5cu.shp)

Creating District Map (Continued) Once the zip code and city shape files have been inserted you can see where the zip codes and the cities are in your county Next double click on the shaded rectangles under layers and choose Hollow and OK for zip codes and city I am interested in the southwest portion of the county where our District is located. Note. You can highlight the layer by checking or un-checking the boxes To highlight this click on the magnifying glass and highlight this portion of the county Double click on the place shape file and choose labels Check Label features in this layer and choose OK Now you can see each city in the county as well as the cities in Chaffey s District

Creating District Map (Continued) Select only the cities in Chaffey s District Click on the black arrow (i.e. Select Features icon Click on each city and hold the control key down Right mouse click on the place shape file and choose Selection and Create Layer from Selected Features Un-check the place shape file Turn on the label features on the District Layer that you just created and make it Hollow Double Click on the Chaffey District Layer that you created and change the font color of the city names Double Click on the Chaffey District rectangle under Layers and change the line color

Creating District Map (Continued) Click on View and then Layout View Go to View and then Data Frame Properties Choose Frame Click on Color and choose No Color Insert a Title by clicking Insert and Title Insert Text by clicking Insert and Text Insert Scale Bar by clicking Insert and Scale Bar Notice that scale is in decimal degrees To change this, double click on it and under the Scale and Units Tab choose Division Units and then choose Miles

2000 US Census Population Data in the Chaffey College District San Antonio Heights Upland Rancho Cucamonga Fontana Montclair Chino Hills Chino Ontario Legend 2000 US Census Population Data Census2-SF1.TOTPOP 25-496 497-817 818-1088 1089-1371 1372-1713 1714-2259 2260-3025 3026-4552 4553-7658 7659-11889 Prepared by Keith Wurtz Date: 20060406 0 1.5 3 6 Miles

Inserting and Using Census Data into District Map Obtaining US Census Data Go to the following: http://www.census.gov/ Click on American Fact Finder Go to Data Sets and click on Decennial Census (Note: American Community Survey) Click on Detailed Tables under SF 1 Click on geo within geo Under Show me all click on Block Groups Under Within click on County Under Select a State click on CA Under Select a County click on San Bernardino Under Select one or more click on All Block Groups and click on Add Click Next Under Select one or more click on P1. Total Population and click on Add (Note. You can choose more than one and it will still work) Click on Show Result

File Downloaded from the Census Bureau Click on Print/Download and click on Download Choose Excel, hold down the control key, and click OK Click on Open and then open the Excel file with data in the name. It is usually the largest file, but not always. Change the GEOGRAPHY_ field name to JOINID. This is the field that is going to match with the STFID Save this file as a dbf file.

Merging or Joining in GIS Need to insert the block groups data for San Bernardino County Click on the + sign and insert the tgr06071grp00.shp file Right Click on Block Groups in GIS because this is at the level in which I downloaded the Census data Go to Join and Relates and Click on Join Make sure that the layer joining from is an attributes table In Number 1 Choose STFID In Number 2 Click on Folder and find the dbf file that you created: RPPop.dbf In Number 3 choose the Join ID field, click OK, and then Click Yes Check to see that the Join worked by right clicking on block groups, clicking on Open Attribute Table, and scrolling to the right to see if the total population field is there

Snap of Block Group Data that includes Total Population In this case there is a Null field in the top row. This indicates that the join did not work for this block group San Bernardino County is one of the few counties in the Country that has an error in one of its block groups

Incorporating US Census Data (Continued) Selecting Block Groups in Chaffey s District Right mouse click on the block group and open the attribute table Click on the Select Features icon: Make sure that the block groups rectangle is checked so that you can see the block groups Click to the left of the map and select the district Once you have selected most of the district it is best to open the attributes table to make sure that all of the block groups have been selected Right mouse click on the block group and open the attributes table Hold the control key down and select the block groups by clicking on the row Right mouse click on the block group and choose Selection and choose Create layer from Selected Features Uncheck the initial block group

Incorporating US Census Data (Continued) Displaying the US Population Data Double click on the Layer that we just created Click on Symbology Click on Quantities In the Value Field choose the total population field and change classes to 10 instead of 5 Click OK

Using Census Data and District Data to Identify possible populations to market to and increase enrollments

Participation Rates of 2000 2001 Chaffey Students by Age Age # N % 18 19 Years 4,018 21,968 18.3 20 24 Years 6,066 50,091 12.1 25 29 Years 2,395 50,686 4.7 30 34 Years 1,752 57,004 3.1 35 39 Years 1,605 64,342 2.5 40 49 Years 2,304 107,749 2.1 50 65 Years 1,071 78,606 1.4 Total 19,211 430,446 4.5 Note. # refers to the number of students attending Chaffey in the 2000 2001 academic year. N refers to the population living in the Chaffey College District taken from the 2000 US Census.

Marketing to 40 49 Year Olds US Census Data allows us to identify the number of 40 49 year olds living in each block group We can use the mapping software to identify where 40 49 year olds live Once we know where they live, we can use segmentation modeling (i.e. answer tree or classification tree) to identify enrollment characteristics of these students and then market to them

Segmentation Modeling According to Borges and Cherpitel (2001), segmentation modeling (i.e. classification tree models) are based on the principle of binary recursive partitioning. Binary recursive partitioning is where the values of the dependent variable (i.e. success and non-success) are examined for all possible splits of the data at each step of the tree-building process to find the split that most effectively separates the dependent variable into homogeneous groups until it is not possible to continue (Borges and Cherpitel, 2001). The model attempts to maximize the number of students who are correctly classified as successes and those who are correctly classified as non-successes.

Enrollment Variables used in Segmentation Model Used MIS to identify enrollment characteristics Transfer course enrollment Basic skills course enrollment Occupational course enrollment Credit course enrollment School Location of course Term Created field for each one that generated number of enrollments aggregated by student

Age Dichotomous - 40-49 year olds and other Node 0 Category % n All other ages 88.52 23477 40-49 year olds 11.48 3044 Total (100.00) 26521 Number of Enrollments in PE Courses Adj. P-value=0.0000, Chi-square=633.4093, df=1 <=Did Not Enroll Node 1 Category % n All other ages 84.06 12245 40-49 year olds 15.94 2322 Total (54.93) 14567 Number of Enrollments in LIB Courses Adj. P-value=0.0000, Chi-square=285.0683, df=1 >Did Not Enroll Node 2 Category % n All other ages 93.96 11232 40-49 year olds 6.04 722 Total (45.07) 11954 Number of Enrollments in HS Courses Adj. P-value=0.0000, Chi-square=90.0277, df=1 <=Did Not Enroll >Did Not Enroll <=Did Not Enroll >Did Not Enroll Node 3 Category % n All other ages 80.37 7711 40-49 year olds 19.63 1883 Total (36.18) 9594 Node 4 Category % n All other ages 91.17 4534 40-49 year olds 8.83 439 Total (18.75) 4973 Node 5 Category % n All other ages 92.18 6331 40-49 year olds 7.82 537 Total (25.90) 6868 Node 6 Category % n All other ages 96.36 4901 40-49 year olds 3.64 185 Total (19.18) 5086 Number of Enrollments in Credit Courses Adj. P-value=0.0000, Chi-square=94.2465, df=1 Number of Enrollments in SU00 Adj. P-value=0.0000, Chi-square=52.3692, df=1 Number of Enrollments in SSS Courses Adj. P-value=0.0000, Chi-square=32.2187, df=1 Number of Enrollments at CCFC Adj. P-value=0.0000, Chi-square=17.5269, df=1 <=Did Not Enroll >Did Not Enroll <=Did Not Enroll >Did Not Enroll <=Did Not Enroll >Did Not Enroll <=Did Not Enroll >Did Not Enroll Node 7 Category % n All other ages 84.41 3943 40-49 year olds 15.59 728 Total (17.61) 4671 Node 8 Category % n All other ages 76.54 3768 40-49 year olds 23.46 1155 Total (18.56) 4923 Node 9 Category % n All other ages 94.49 2042 40-49 year olds 5.51 119 Total (8.15) 2161 Node 10 Category % n All other ages 88.62 2492 40-49 year olds 11.38 320 Total (10.60) 2812 Node 11 Category % n All other ages 91.03 4497 40-49 year olds 8.97 443 Total (18.63) 4940 Node 12 Category % n All other ages 95.12 1834 40-49 year olds 4.88 94 Total (7.27) 1928 Node 13 Category % n All other ages 91.50 226 40-49 year olds 8.50 21 Total (0.93) 247 Node 14 Category % n All other ages 96.61 4675 40-49 year olds 3.39 164 Total (18.25) 4839

Segmentation Modeling Results Nodes n % Gain: n Gain (%) Resp: % Index (%) 8 MORE likely to not enroll in a PE Course, MORE likely to not enroll in a library course, MORE likely to enroll in credit course 4,923 18.6 1,155 37.9 23.5 204.4 7 MORE likely to not enroll in a PE Course, MORE likely to not enroll in a library course, LESS likely to enroll in credit course 4,671 17.6 728 23.9 15.6 135.8 10 MORE likely to not enroll in a PE Course, LESS likely to enroll in a library course, MORE likely to enroll in Summer 2,812 10.6 320 10.5 11.4 99.1 11 - LESS likely to enroll in a PE Course, MORE likely to enroll in a HS course, Less likely to not enroll in SSS course 4,940 18.6 443 14.6 9.0 78.1 13 LESS likely to enroll in PE Course, LESS likely to enroll in HS course, MORE likely to not enroll at Fontana 247 0.9 21 0.7 8.5 74.1 9 - MORE likely to not enroll in a PE Course, LESS likely to enroll in a library course, LESS likely to not enroll in Summer 2,161 8.1 119 3.9 5.5 48.0 12 - LESS likely to enroll in a PE Course, MORE likely to enroll in a HS course, Less likely to enroll in SSS course 1,928 7.3 94 3.1 4.9 42.5 14 - LESS likely to enroll in PE Course, LESS likely to enroll in HS course, MORE likely to enroll at Fontana 4,839 18.2 164 5.4 3.4 29.5 Note. N is the number of all cases in the node. % is the percent of all cases in the node. Gain:n is the number of all cases with the target response (i.e. 40-49 year olds). Gain:% is the percent of all cases (e.g.: 1,155/3,044=37.9) with the target response. Resp:% represents the proportion of cases in the node that have the target response (e.g.:1,155/4,923=23.5%). Index(%) gives a measure of how the number of target responses in the node compares to that for the entire sample (e.g.: 37.9%/18.6%=204.4%).

Using Information to Develop Marketing Plan Now that we know that 40 49 year olds prefer the following types of courses MORE likely to not enroll in a PE Course, MORE likely to not enroll in a library course, MORE likely to enroll in credit course MORE likely to not enroll in a PE Course, MORE likely to not enroll in a library course, LESS likely to enroll in credit course We can back to SPSS Base and identify which courses that meet this criteria

Courses Preferred by 40 49 Year Olds Of the 8,849 enrollments that met the previously stated criteria 13% or 1,127 of these enrollments were in Computer Information Systems courses 310 of these enrolments were in CIS-1 (Introduction to Computer Information) 116 were in CIS-68I (Using the Internet) 91 were in CIS-404 (Fundamentals of Microsoft Windows) 11% or 937 of these enrollments were in Disabilities Programs and Services courses Most of these enrollments were in the independent living courses 8% or 708 of these enrollments were in Business and Office Technologies courses 120 of these were in BUSOT-40A (Beginning Computer Keyboarding) 99 were in BUSOT-46A (Beginning Microsoft Word) 7% or 620 of these enrollments were math courses 190 of these were in MATH-410 (Elementary Algebra) 99 were in MATH-420 (Intermediate Algebra) 83 were in MATH-25 (College Algebra) 72 were in MATH-520 (Arithmetic and Preparation for Algebra) 64 were in MATH-510 (Arithmetic) 4% or 347 of these enrollments were in Child Development Education courses 39 of these were in CDE-4 (Child, Family, and Community) These enrollments were very spread out in mostly transferable courses 3% or 288 of these enrollments were in ESL courses

HAVEN AVE 2000 US Census Population Data in the Chaffey College District San Antonio Heights STATE HWY 30 I 15 Upland STATE HWY 83 Rancho Cucamonga HAVEN AVE BASE LINE ST STATE HWY 66 W BASE LINE RD Fontana Montclair I 10 STATE HWY 60 Ontario Chino Hills Chino STATE HWY 71 Number of 40-49 Year Olds tgr06071grp00.all40t49 3-69 70-121 122-169 170-215 216-275 276-344 345-439 440-598 599-1007 1008-1653 Prepared by Keith Wurtz Date: 20060406 0 1.25 2.5 5 Miles

HAVEN AVE 2000 US Census Population Data and Chaffey Students who are 40-49 Years Old San Antonio Heights [` Main Campus STATE HWY 30 I 15 Montclair Upland STATE HWY 83 [` Ontario Center Rancho Cucamonga HAVEN AVE BASE LINE ST STATE HWY 66 I 10 W BASE LINE RD Fontana Fontana Center [` Chino Hills Prepared by Keith Wurtz Date: 20060408 [` Chino STATE HWY 60 Chino Center [_ STATE HWY 71 Chino Campus Ontario Number of 40-49 Year Olds [` 3-69 70-121 122-169 170-215 216-275 276-344 345-439 440-598 599-1007 1008-1653 2000-2001 40-49 Year Olds Chaffey Locations [_ Chino Campus 0 1 2 4 Miles

HAVEN AVE Areas in Chaffey's District with High Concentration of 40-49 Year Olds San Antonio Heights [` Main Campus STATE HWY 30 I 15 Upland Rancho Cucamonga HAVEN AVE BASE LINE ST STATE HWY 66 W BASE LINE RD [` Fontana Fontana Center STATE HWY 83 Montclair [` Ontario Center I 10 Chino Hills [` Chino STATE HWY 60 Chino Center [_ STATE HWY 71 Chino Campus Ontario Number of 40-49 Year Olds [` [_ 3-69 70-121 122-169 170-215 216-275 276-344 345-439 440-598 599-1007 1008-1653 2000-2001 40-49 Year Olds Chaffey Locations Chino Campus High Concentration of 40-49 Year Olds Prepared by Keith Wurtz Date: 20060509 00.51 2 Miles

Using GIS to Generate Address Labels in these Block Groups 1. Open the attribute table in the Streets file and in the High Concentration of 40 49 Year Olds 2. In the 40 49 year old attribute table locate the tgr06071grp.00.tract field and in Streets locate the TRACTL field and sort this field in ascending order 3. Click on the Selection Icon and in the streets file select the rows where the TRACT field in the 40-49 year olds table and the TRACTL field in the Streets table match

Legend Spring 2006 Alta Loma Students Spring 2006 Etiwanda Students Spring 2006 Rancho Students Spring 2006 Upland Students Spring 2006 Fontana Students Spring 2006 Ontario Students Spring 2006 Montclair Students Spring 2006 Chino Students Spring 2006 Chino Hills Students Montclair San Antonio Heights Upland Rancho Cucamonga Fontana Ontario Chino Hills Chino Note. Participation rates are misleading because the N includes 2005 estimates from the California Department of Finance of every person in the city. For example, all those under 18 and over 65 are included. Area Spring 2006 # N % Alta Loma 2,300 Etiwanda 780 Rancho 1,715 Rancho Cucamonga Total 4,795 161,830 3.0% Upland 1,490 73,697 2.0% Fontana 3,814 160,015 2.4% Ontario 2,259 170,373 1.3% Montclair 380 35,530 1.1% Chino 869 76,070 1.1% Chino Hills 337 77,819 0.4% Total 13,944 755,334 1.8%

WY k RAMONA AVE CENTRAL AVE RIVERSIDE DR CHINO AVE k [` Æq Chino Center SCHAEFER AVE Canyon Ridge Hospita Industry Industry Industry MOUNTAIN AVE EDISON AVE EUCLID AVE S EUCLID AVE POMONA FRWY Undeveloped 30,000 Dwellings in Ontario Under Construction CORONA EXWY Industry c: 2,200 Projected Dwellings [` Chino Campus Undeveloped Area Undeveloped Area c: Æm PRADO RD "The Preserve" 9,800 Projected Dwellings Legend Chino Golf "The Preserve" c: Spring 2006 Chaffey Students Living in Chino Portion of Spring 2006 Chaffey Students Living in Ontario

References Borges, Guilherme and Cherpitel, Cheryl. (2001). Selection of screening items for alcohol abuse dependence among Mexican and Mexican Americans in the emergency department. Journal of Studies on Alcohol, 62, 277-.