1 00:00:00,000 --> 00:00:12,700 I don't know if I'm going to be a zoo and it's looking, actually fairly, you've got all those people's part. I mean, you need to look at it like next. Yeah. 2 00:00:12,860 --> 00:00:15,700 Yeah, parents are walking, I thought it was a good old man. 3 00:00:15,700 --> 00:00:17,760 Well, man, yeah. 4 00:00:17,880 --> 00:00:22,840 I want to put parking along, it's definitely done to go to public heights. 5 00:00:22,840 --> 00:00:29,000 Yeah, a lot of people park on Westchester, the go-to Westchester. 6 00:00:29,780 --> 00:00:32,840 Yeah, who of us came through and got, I mean, we'll hold the chain. 7 00:00:33,440 --> 00:00:35,600 Oh, I see the safe route side. 8 00:00:35,980 --> 00:00:37,220 You won't be proud of that. 9 00:00:37,240 --> 00:00:37,420 I don't know. 10 00:00:38,820 --> 00:00:45,160 And they came out of the idea of working with more and more and walk on this trail to the back. 11 00:00:45,160 --> 00:00:48,580 to the hospital and to the hospital because I could have gone to the hospital. 12 00:00:48,580 --> 00:00:49,080 So as to the hospital. 13 00:00:49,080 --> 00:00:50,100 So that's a real kind of building. 14 00:00:50,420 --> 00:00:55,860 I feel like I got to do like single dance in the right here. 15 00:00:57,980 --> 00:00:59,120 It's a school in the park down the hill. 16 00:00:59,120 --> 00:01:00,560 It's full of dancing, but it's full of dance... 17 00:01:00,560 --> 00:01:01,460 The park is full of dance and the park is full of dance! 18 00:01:01,460 --> 00:01:03,160 You can't... 19 00:01:03,900 --> 00:01:04,420 Hello. 20 00:01:04,420 --> 00:01:04,900 Well I can't tell you. 21 00:01:05,000 --> 00:01:05,580 I can't tell, too. 22 00:01:05,600 --> 00:01:05,760 What? 23 00:01:06,080 --> 00:01:06,500 I can't tell. 24 00:01:06,500 --> 00:01:07,160 Yeah, the can't. 25 00:01:07,420 --> 00:01:07,780 The can't. 26 00:01:07,780 --> 00:01:08,140 The can't. 27 00:01:08,240 --> 00:01:08,640 I can't. 28 00:01:09,280 --> 00:01:09,800 You can't tell. 29 00:01:10,400 --> 00:01:12,920 I can't tell. 30 00:01:14,280 --> 00:01:19,200 We have to be like straw or excessive, so it's not like a height of the wood, 31 00:01:33,330 --> 00:01:35,570 but right, that's the density of the wood. 32 00:01:38,520 --> 00:01:41,040 In any case, we can do 33 00:01:54,540 --> 00:01:57,000 it. 34 00:01:59,740 --> 00:02:01,340 Are you all ready to start? 35 00:02:02,020 --> 00:02:03,240 Okay, I think we're ready. 36 00:02:03,840 --> 00:02:06,960 You know, the diversity is a big issue in the cater bin, 37 00:02:06,960 --> 00:02:15,340 been the continuing concern for really several decades and this year our public information 38 00:02:15,340 --> 00:02:24,040 officer at Casey Yoder decided to go a little fact-finding and recruited an intern, Christian 39 00:02:24,040 --> 00:02:30,060 Perry, who's a student at the Henry Young School at Georgia State to do some basic research 40 00:02:30,060 --> 00:02:31,860 about this and the catering. 41 00:02:31,860 --> 00:02:37,080 They have a product to their efforts efforts that they're going to share with us tonight. 42 00:02:38,060 --> 00:02:38,640 Good evening. 43 00:02:39,240 --> 00:02:45,120 I'm going to introduce kind of what the project was about that we got started with at the beginning of the summer. 44 00:02:45,960 --> 00:02:54,100 And then I'll hand it over to Chris Jen and he'll kind of go through the data that he pulled and talking about the qualitative that he talked to people. 45 00:02:54,780 --> 00:02:57,740 And then we'll kind of wrap it up and give you some time for some questions. 46 00:02:59,520 --> 00:03:06,780 So the reason why we decided that we needed to do some baseline research on the state of diversity 47 00:03:06,780 --> 00:03:12,560 indicator is because Decaturites have said over time that they value a diverse community 48 00:03:12,560 --> 00:03:13,760 and a diverse Decatur. 49 00:03:14,540 --> 00:03:19,760 And they've said that since the city has really started doing strategic planning, it was part 50 00:03:19,760 --> 00:03:21,480 of the 2000 strategic plan. 51 00:03:21,500 --> 00:03:25,640 It was goal number four to maintain an encourage various types of diversity. 52 00:03:25,640 --> 00:03:30,440 And it also came up again during the 2010 strategic planning process. 53 00:03:30,980 --> 00:03:36,000 There were quite a few roundtable discussions that happened during that planning process. 54 00:03:36,540 --> 00:03:39,520 And time and again people said that they valued a diverse community, 55 00:03:40,140 --> 00:03:45,740 but they were also concerned that economic forces in the city may diminish diversity. 56 00:03:47,020 --> 00:03:51,940 And so out of that came principle B, which is to encourage a diverse and engaged community. 57 00:03:52,640 --> 00:03:59,040 And we really started to realize that until we knew what the actual state of diversity was and looked at the actual data, 58 00:03:59,800 --> 00:04:04,980 we really couldn't figure out what the next steps were and how the city could continue to plan for this diversity in the future. 59 00:04:07,020 --> 00:04:07,560 So, Christian. 60 00:04:12,340 --> 00:04:18,180 So, Casey introduced the why to this project and I'm going to jump into the how the research is conductive. 61 00:04:19,240 --> 00:04:23,100 So, the foundation of this is in empirical quantitative data. 62 00:04:23,100 --> 00:04:28,940 that's what I received my training in, I studied economics as an undergraduate, and all the 63 00:04:28,940 --> 00:04:33,300 data that I pulled is maybe publicly available through the United States Census Bureau. 64 00:04:34,000 --> 00:04:39,360 I have a number of statistical products available, but I use the DCNL Census, which is 65 00:04:39,360 --> 00:04:45,580 conducted every 10 years since 1790, and the American Community Survey, which is conducted 66 00:04:45,580 --> 00:04:48,140 on a rolling basis since 2005. 67 00:04:48,140 --> 00:04:49,240 So 68 00:04:51,450 --> 00:04:56,530 the other aspect of it to complement the data is the qualitative research, took a 69 00:04:56,530 --> 00:05:03,150 convene sample of 21 community members and I asked them a series of questions to try to get 70 00:05:03,890 --> 00:05:10,930 their take on how the caters change over time. A convene sample is one that it's based off 71 00:05:10,930 --> 00:05:17,070 of volunteers so there is that bias, but the 21 people that I spoke with live both in and outside 72 00:05:17,070 --> 00:05:21,770 the city and the healthy mix of ages and tendors within the community. 73 00:05:22,330 --> 00:05:27,150 I also assured them that the information would be kept anonymous so that they would be 74 00:05:27,150 --> 00:05:30,230 comfortable with speaking candidly about the topic. 75 00:05:34,650 --> 00:05:40,920 So the scope of this project is of course the city of the cater which is conveniently 76 00:05:40,920 --> 00:05:48,800 split into four separate census tracks and a census track is the basic geographical unit 77 00:05:48,800 --> 00:05:55,780 that you do demographic research on, on average, to have about 4,000 people, and for the 78 00:05:55,780 --> 00:06:00,420 purposes of this research, I gave them neighborhood names to put it in the context. 79 00:06:01,740 --> 00:06:07,920 And so, clockwise from census track 2 to 5, I'm calling that Claremont neighborhood, and 80 00:06:07,920 --> 00:06:14,180 to the right of that, calling that Sikomore and Great Lakes in the southeast quadrant, calling 81 00:06:14,180 --> 00:06:17,380 that went on a park and Southwest is occurs. 82 00:06:20,040 --> 00:06:22,520 What I'm making mentioned to the county, of course, 83 00:06:22,620 --> 00:06:23,760 I'm talking about the cab county, 84 00:06:24,360 --> 00:06:25,820 and what I reference the region. 85 00:06:25,920 --> 00:06:29,200 I'm talking about the Atlanta metropolitan statistical area, 86 00:06:29,340 --> 00:06:32,820 which is the 28 or so counties around the city of Atlanta. 87 00:06:39,670 --> 00:06:41,630 So the research design use for this project 88 00:06:41,630 --> 00:06:44,290 is known as a cross section of research. 89 00:06:44,770 --> 00:06:47,630 And what that is, it looks at a set of variables 90 00:06:47,630 --> 00:06:51,190 for a case over a specified period of time. 91 00:06:51,190 --> 00:06:56,070 And it's not experimental, and it's actually used to observe trends and data over time. 92 00:06:56,790 --> 00:07:01,010 The variables you spend three different levels, the spend nominal level, 93 00:07:01,450 --> 00:07:03,850 coordinate, and interval level. 94 00:07:04,210 --> 00:07:08,910 An example of a nominal or a categorical variable that I use in this research is race. 95 00:07:09,690 --> 00:07:13,090 And for the purposes of this project, I use three race categories. 96 00:07:14,130 --> 00:07:19,130 Black or African American alone, white or Caucasian alone, and non-black minority, 97 00:07:19,130 --> 00:07:26,070 which is essentially everything else on a clue to Spatty and Latino designations include native American or Asian 98 00:07:26,710 --> 00:07:31,590 ethnicities, multi-racial, essentially everything up. That's not black or white alone. 99 00:07:33,550 --> 00:07:38,650 And it's a good point to mention that race, when you form a research perspective, is not a static concept. 100 00:07:39,310 --> 00:07:43,830 It's self-identified and the definitions have changed and evolved over time. 101 00:07:43,830 --> 00:07:49,770 And nowadays, there's more options that you can identify with than there have ever been. 102 00:07:50,190 --> 00:08:01,770 An example that is this 1970 census form here on the left, which is the last year that they listed Negro as a category later to be replaced by African-American dissetrics in example. 103 00:08:01,770 --> 00:08:05,310 And so that's a categorical or non-operatable. 104 00:08:05,890 --> 00:08:10,370 In Warner Variable is one that increases in a non-domarical degree. 105 00:08:11,570 --> 00:08:14,770 And an example that I use in this research is educational attainment. 106 00:08:15,690 --> 00:08:18,290 And so the census has seven education categories, 107 00:08:19,090 --> 00:08:21,710 starting from less than a ninth grade degree, 108 00:08:21,770 --> 00:08:24,430 all the way up to a graduate or professional degree. 109 00:08:25,790 --> 00:08:28,650 And lastly, an interval level measure is one with an absolute zero 110 00:08:28,650 --> 00:08:31,450 and so on this research, I use income levels 111 00:08:31,450 --> 00:08:33,470 and they've all been adjusted for inflation. 112 00:08:38,680 --> 00:08:41,720 So moving into what exactly the research found. 113 00:08:48,080 --> 00:08:49,560 So a quick search of the definition 114 00:08:49,560 --> 00:08:52,880 of a diversity comes with a lot of different things. 115 00:08:52,940 --> 00:08:56,160 So this is just a synthesis that I use to define diversity 116 00:08:56,160 --> 00:08:58,780 in a most technical form, the state of being 117 00:08:58,780 --> 00:09:02,540 diverse, the state of unlikeness, variety, or multi-formity. 118 00:09:03,500 --> 00:09:06,940 And as this quote here below, it's a good summary 119 00:09:06,940 --> 00:09:13,380 of the context of diversity is key. Historically, when you look at how the world was used, it 120 00:09:13,380 --> 00:09:18,340 was not used to identify people at all. So nowadays, when people think about diversity, 121 00:09:18,760 --> 00:09:23,440 they automatically think of characteristics of people, which is important, moving forward. 122 00:09:27,100 --> 00:09:33,200 And so what I asked, these 21 community members, what they thought of, what they personally defined 123 00:09:33,200 --> 00:09:39,320 diversity is, essentially had five different response types. The first of those being, I don't know, 124 00:09:39,320 --> 00:09:44,600 with that means which is fair because that diversity is an abstract and 125 00:09:44,600 --> 00:09:49,540 intangible concept that was about two or three of the cases the second 126 00:09:49,540 --> 00:09:54,580 level was a technical definition for example a resident said that diversity 127 00:09:54,580 --> 00:10:01,560 is having a lot of choices of variety of people white female age 72 building 128 00:10:01,560 --> 00:10:07,920 on that the next type of category was one that automatically implied race 129 00:10:07,920 --> 00:10:17,360 specifically black and white, for example, black female age 78 defined diversity simply as white and black coming together. 130 00:10:20,140 --> 00:10:22,080 So the fourth level, 131 00:10:24,420 --> 00:10:33,360 in a few years, acknowledged that race was the first implication, but expanded that definition to talk about more than just race as a variable. 132 00:10:34,360 --> 00:10:40,180 For example, race is the tip of the iceberg. Diversity is a mixture of income, talent, 133 00:10:40,820 --> 00:10:48,280 experiences, and nationalities. Blackmail age 53. And the last level, last type of definition, 134 00:10:48,560 --> 00:10:54,300 I got introduced the notion of embracing and valuing differences, kind of the above and beyond 135 00:10:54,300 --> 00:11:00,940 kind of definition. Diversity is more than being accepting a tolerant of people different than 136 00:11:00,940 --> 00:11:03,200 It's embracing that as a positive part of your life. 137 00:11:03,300 --> 00:11:05,520 Different experiences, backgrounds, interests. 138 00:11:06,020 --> 00:11:08,840 It's not racially based, age based, gender based, 139 00:11:09,120 --> 00:11:12,960 or even family status based, it's having all of that stuff. 140 00:11:13,520 --> 00:11:15,020 White female age 53. 141 00:11:20,830 --> 00:11:23,930 So the first variable that I looked at to define diversity 142 00:11:23,930 --> 00:11:27,370 is race, and that's for two major reasons. 143 00:11:28,150 --> 00:11:30,790 It's the most visible indicator of diversity. 144 00:11:31,030 --> 00:11:31,970 It's the color of your skin. 145 00:11:32,110 --> 00:11:34,550 You can't change it and you can't hide it. 146 00:11:34,550 --> 00:11:39,670 And in the Cater, particularly, there's a historically only bento races, which is not unique 147 00:11:39,670 --> 00:11:48,930 of the Cater, it's indicative of the Southern culture and the Southern legacy that we have. 148 00:11:49,970 --> 00:11:55,190 And so the next few slides, we're going to show you kind of like a heat map of the concentration 149 00:11:55,190 --> 00:12:01,890 of African-American in the city of the Cater, starting back in 1940, 150 00:12:05,450 --> 00:12:06,450 and the darker the area, 151 00:12:06,450 --> 00:12:09,610 the higher concentration of black or African Americans. 152 00:12:28,020 --> 00:12:33,180 So this is looking at the sheer of the ratio 153 00:12:33,180 --> 00:12:37,540 of the population for those three race categories, white, black, 154 00:12:37,540 --> 00:12:38,480 and non-black minority. 155 00:12:39,800 --> 00:12:42,180 And so the first thing you notice is that the share of the white 156 00:12:42,180 --> 00:12:46,080 population indicator has increased over the last 20 years, 157 00:12:46,200 --> 00:12:49,100 why that of the black population has decreased. 158 00:12:49,100 --> 00:12:57,380 But you do see the emergence of a non-black minority, which did not exist in 1990. 159 00:13:02,420 --> 00:13:07,020 So this is breaking out that racial composition of the population by neighborhood. 160 00:13:07,840 --> 00:13:11,000 And it's interesting because, by the way, at the back, you can see that 161 00:13:12,080 --> 00:13:17,900 South Dakota held the majority of the black population on North Dakota held the majority of the white population. 162 00:13:18,060 --> 00:13:19,080 And this is 1990. 163 00:13:21,870 --> 00:13:27,430 And to contrast, you see that the racial composition is a lot different. 164 00:13:28,510 --> 00:13:31,350 While it is more integrated, yes, the share of the black population, 165 00:13:31,890 --> 00:13:36,670 and the absolute number, that's the scale we're looking at here, has decreased tremendously. 166 00:13:42,640 --> 00:13:48,140 Oh, of course, just as an example, formally held the largest share of the black population. 167 00:13:48,960 --> 00:13:56,360 And from 1990 to 2010, it dropped from about 4,000 black residents to just 1,200 residents. 168 00:14:08,120 --> 00:14:13,680 So, trying to put the cater into context, this is comparing it to the amount of metropolitan region. 169 00:14:14,700 --> 00:14:19,440 And immediately, you can see that the opposite ratio trend is going on. 170 00:14:19,440 --> 00:14:22,920 regionally seated the white chair, the population is decreasing the black 171 00:14:22,920 --> 00:14:28,180 chairs increasing at a slower rate and that the non-black minority is 172 00:14:28,180 --> 00:14:31,780 increasing at a much faster rate than it is in decator. 173 00:14:35,530 --> 00:14:36,450 So comparing it to 174 00:14:36,450 --> 00:14:42,390 two of the the nine benchmark cities that the city uses to compare performance 175 00:14:42,390 --> 00:14:47,490 standards we have Carter'sville which is in industrial town of 75 in Bartow 176 00:14:47,490 --> 00:14:54,950 county and swan in Georgia, which is up 85 in Gwynette County and while they both have 177 00:14:54,950 --> 00:14:59,970 different racial compositions they do more or less mirror the regional trend and decayed or 178 00:15:03,040 --> 00:15:03,660 So 179 00:15:10,860 --> 00:15:32,080 when you break out the racial composition by age group, I would like this quote because it introduced the notion that decadence moving away from an old diversity, which is characterized by black and white alone and moving towards a new diversity where these different ethnic and racial groups have a much larger presence. 180 00:15:32,080 --> 00:15:38,120 And for example, in the chat population under five is where you see this presence taking the most form. 181 00:15:38,680 --> 00:15:42,520 And as the age groups get older, you see this become less and less prevalent. 182 00:15:48,500 --> 00:15:55,200 You can see that the non-black minorities are disproportionately in the 17 and below age group 183 00:15:55,800 --> 00:16:00,560 while the African-American population is disproportionately in the older age groups. 184 00:16:04,720 --> 00:16:07,440 And to put that in the more perspective, while one in five 185 00:16:07,440 --> 00:16:11,620 Decatur residents is black, about one in three of Decatur residents, 186 00:16:11,740 --> 00:16:13,500 65 and over is black. 187 00:16:20,030 --> 00:16:23,250 So this is looking at the age breakdown alone. 188 00:16:26,570 --> 00:16:32,270 So regionally and countywide, we sway more towards the younger side. 189 00:16:33,070 --> 00:16:37,970 But Decatur is unique because it is a community that's 190 00:16:37,970 --> 00:16:41,670 ways towards the older side within a county and a region that 191 00:16:41,670 --> 00:16:49,270 primarily young. The top arrow here shows between 2000 and 2010 how the share 192 00:16:49,270 --> 00:16:57,400 the population 25 to 34 years old is quickly replaced by the share that it's 55 to 64 years old. 193 00:16:59,190 --> 00:17:03,310 And to the right of it, you see how that trend is not observed in the county, 194 00:17:03,550 --> 00:17:04,970 no national renewal regionally. 195 00:17:10,740 --> 00:17:13,260 So this is looking at an average household sizes 196 00:17:15,660 --> 00:17:16,730 and it's 197 00:17:16,730 --> 00:17:21,370 that researchers try to get a beat on the different types of families that are living in 198 00:17:21,370 --> 00:17:21,870 the community. 199 00:17:22,430 --> 00:17:26,230 And so to the right side of the chart, you can see that the K-D-R-N-Average has had smaller 200 00:17:26,230 --> 00:17:32,750 household sizes, but if you look at it spatially, by neighborly, then North Dakota has 201 00:17:32,750 --> 00:17:38,270 had much smaller household sizes, while South Dakota has had much larger household sizes. 202 00:17:41,290 --> 00:17:46,050 K-D-R-Z-O-S-O-S-Stork had a lower share of households with children than the other benchmarks. 203 00:17:50,940 --> 00:17:56,040 So the census acts a question, I'm going to ask, what's the sex of the householder, the 204 00:17:56,040 --> 00:17:58,620 head of the household, and the sex of their partner? 205 00:17:58,760 --> 00:18:05,040 And this is what research is used to get a beat on the gay and lesbian community within 206 00:18:05,040 --> 00:18:05,560 an area. 207 00:18:06,020 --> 00:18:11,320 And so the blue bar is looking at the share of same-sex households as a percentage of the total 208 00:18:11,320 --> 00:18:13,000 households in that area. 209 00:18:13,280 --> 00:18:18,620 So nationally, about 8% of the households are same-sex households split, almost evenly 210 00:18:18,620 --> 00:18:23,720 between same-sex male and same-sex female households. And just looking at the blue bar, 211 00:18:23,820 --> 00:18:28,080 you can see that Atlanta and the Cap County already have a much higher share of these types 212 00:18:28,080 --> 00:18:33,980 of households. And looking at the cater, it has about four times the national concentration, 213 00:18:35,200 --> 00:18:41,360 and it means having it towards the same-sex female households. In fact, between 2000 and 2010, 214 00:18:41,640 --> 00:18:46,340 there was actually a 5% net decrease in the number of same-sex male households. 215 00:18:49,860 --> 00:18:55,680 If I read that right, it's still less than three and a half percent of the same sex households. 216 00:18:57,040 --> 00:18:58,640 For the kid overall, yes. 217 00:19:01,190 --> 00:19:06,910 That seems like a small percentage for my home observations. 218 00:19:13,980 --> 00:19:16,120 But that's just self identified on the census. 219 00:19:17,580 --> 00:19:21,140 That's just self identified on the census data. 220 00:19:21,140 --> 00:19:27,200 Right, right. It's a proxy for the community. It's not a direct question at X, which is actually warrantational. 221 00:19:29,290 --> 00:19:35,010 So next we're going to add educational attainment from 1999 to 2010. 222 00:19:35,770 --> 00:19:37,970 Decatur is extremely well-educated. 223 00:19:38,510 --> 00:19:42,810 While in 1990 roughly 1 in 6 resident hat, 224 00:19:43,430 --> 00:19:48,190 a graduate of professional degree, fast-forward to 2010 and 1 in 3 resident hat, 225 00:19:48,190 --> 00:19:50,930 the highest level of education on this survey, 226 00:19:52,370 --> 00:19:55,710 which is about three times the concentration of the national. 227 00:20:00,030 --> 00:20:02,930 So looking at meeting household income, 228 00:20:03,630 --> 00:20:05,710 and you use meeting when you talk about income levels, 229 00:20:05,910 --> 00:20:08,990 because the averages are easily swayed by outliers. 230 00:20:10,010 --> 00:20:10,410 The cater, 231 00:20:11,630 --> 00:20:15,670 meeting household level of historically was lower than that of the benchmarks, 232 00:20:16,350 --> 00:20:20,110 but quickly caught up in the early 2000s. 233 00:20:20,670 --> 00:20:25,050 And if you look at that spatially, you see that the greatest growth has been in South 234 00:20:25,050 --> 00:20:32,030 Decatur households at about 1985,000 a year, respectively. 235 00:20:34,780 --> 00:20:41,380 And you also noticed that the green and the purple bars between 2000 and 2010 that all 236 00:20:41,380 --> 00:20:47,100 the other benchmarks saw a net loss and then meeting income level, which was of course the recession 237 00:20:47,100 --> 00:20:51,060 years. Yeah, Decatur's meeting income level still continued to increase. 238 00:20:53,970 --> 00:20:54,970 And if you look at this 239 00:20:54,970 --> 00:21:00,990 increase in that same time period by the race of the household, you see that while Decatur 240 00:21:00,990 --> 00:21:07,510 household overall increased and white households overall saw an increase in their income, that black 241 00:21:07,510 --> 00:21:14,430 households saw a decrease of about 50% of their meeting income. It dropped from about 34,000 a year 242 00:21:14,430 --> 00:21:16,550 to about 17,000 a year. 243 00:21:28,950 --> 00:21:33,470 And of course, if variables are presented or not comprehensive, I encourage you to read the report 244 00:21:33,470 --> 00:21:37,770 and I'd get into all the native-gritty details of all the data that have worked on this 245 00:21:37,770 --> 00:21:38,250 summer. 246 00:21:38,970 --> 00:21:41,290 And I'm going to hand it back over to Katie's here to talk about what's next. 247 00:21:42,050 --> 00:21:45,890 Well, before you do that, would you just back up one minute at those incomes? 248 00:21:46,290 --> 00:21:47,810 We went through that so quickly, 249 00:21:50,210 --> 00:21:51,410 and so 250 00:21:54,130 --> 00:21:56,870 the highest incomes in the 2010, 251 00:21:56,870 --> 00:22:05,010 and senses are on the south side, yes, and the very highest are in Oakhurst. 252 00:22:08,390 --> 00:22:12,710 Yep, and it's also interesting because North Decatur neighborhoods also saw a net loss 253 00:22:12,710 --> 00:22:17,870 in the immediate income in the recession years, but that's when South Decatur saw the greatest 254 00:22:17,870 --> 00:22:19,730 increases in their income levels. 255 00:22:21,450 --> 00:22:24,810 And of course it's not to say that everyone that lives in Oakhurst got a promotion or 256 00:22:24,810 --> 00:22:31,110 It's the only counts, the income level of the people that look there during the census year. 257 00:22:33,990 --> 00:22:35,970 And that was 2010, correct? 258 00:22:37,210 --> 00:22:40,670 When the actual counting of heads happens. 259 00:22:43,480 --> 00:22:45,060 Let me go back to the slide. 260 00:22:45,200 --> 00:22:47,240 They had like the arrows going. 261 00:22:48,020 --> 00:22:49,160 Each bird down. 262 00:22:49,760 --> 00:22:50,940 Yeah, that way. 263 00:22:55,580 --> 00:22:56,840 And tell us again what we're looking at. 264 00:22:56,940 --> 00:22:59,140 Okay, so this is just here. 265 00:23:00,380 --> 00:23:01,000 Come right outside. 266 00:23:01,000 --> 00:23:02,960 I don't know. Thanks Keesing. 267 00:23:04,360 --> 00:23:10,740 This is the share of the population as a percentage of that age group. 268 00:23:11,260 --> 00:23:19,500 And so the highest age group in 2004, the city indicator was the 25 to 34 year old age group. 269 00:23:20,600 --> 00:23:24,140 And that share of the population decreased about 5%. 270 00:23:24,140 --> 00:23:30,200 And at the same time, the share of the population that was 55 to 64-year-olds increased 271 00:23:30,200 --> 00:23:32,600 to 5% in just 10 years. 272 00:23:32,700 --> 00:23:35,880 And you don't see that replicated at any other levels. 273 00:23:37,460 --> 00:23:38,400 And what is the blue bar? 274 00:23:38,560 --> 00:23:40,140 That's an 18%. 275 00:23:41,660 --> 00:23:42,980 That's a 35-year-old. 276 00:23:43,940 --> 00:23:45,780 So what do you take that to me? 277 00:23:48,000 --> 00:23:53,780 It means that you've got to be in your higher earning years to live in the cater anymore. 278 00:23:54,540 --> 00:23:58,300 Well, looking at it alone, it's hard to make any concrete differences. 279 00:24:00,100 --> 00:24:10,520 But I will say that if you look at these populations as a share of the county population, 280 00:24:10,760 --> 00:24:17,320 overall, that the caterers have a disproportionately high amount of the cab counties older 281 00:24:17,320 --> 00:24:21,340 residents from 65 and older, and that's going back to 1980. 282 00:24:25,410 --> 00:24:33,750 we'll be interesting to see how this may change in the next census count because I think 283 00:24:33,750 --> 00:24:43,910 with the continued, I guess, influx of people with children we may see this shift a little bit 284 00:24:43,910 --> 00:24:52,210 of that 25 to 34 and the 35 to 44. Also with the multifamily young professionals. 285 00:24:52,750 --> 00:24:57,270 So we made a place that I think that's one thing that was hard. 286 00:24:57,890 --> 00:25:02,030 You don't get people out of college not having people with any opportunities to move to the care. 287 00:25:02,890 --> 00:25:03,350 Yeah, yeah. 288 00:25:03,350 --> 00:25:13,580 So the 18 to 24 may also, or that, yeah, go up considerably, because that's pretty low for the cater. 289 00:25:18,460 --> 00:25:20,160 Do you have a question on A's in particular? 290 00:25:23,530 --> 00:25:24,290 You can move on now. 291 00:25:24,550 --> 00:25:24,770 Thank you. 292 00:25:26,310 --> 00:25:28,030 I guess to tell them a little bit. 293 00:25:35,310 --> 00:25:40,850 So since this project that Christian worked on was only for eight weeks this summer there 294 00:25:40,850 --> 00:25:46,070 were some limits to what we could do in terms of our research and we do think that definitely 295 00:25:46,070 --> 00:25:51,510 we should consider doing more in-depth qualitative research meaning more interviews with residents 296 00:25:51,510 --> 00:25:57,250 and people who live maybe just outside to cater as well as possibly some actual like real focus groups. 297 00:25:57,870 --> 00:26:03,330 You know Christian was a one-man show so he was able to talk to 21 people which was great but 298 00:26:03,330 --> 00:26:06,710 I think there's more opportunity to have more conversations there. 299 00:26:07,770 --> 00:26:13,730 Quantitatively, it would be interesting to get more data and I know the city is in the process 300 00:26:13,730 --> 00:26:18,650 of this with some of the pedestrian counts and cyclists counts and other types of transportation 301 00:26:18,650 --> 00:26:22,070 patterns to see what people are doing in the city. 302 00:26:22,890 --> 00:26:28,090 It would also be interesting to get some new and updated demographic information on event 303 00:26:28,090 --> 00:26:33,070 attendance that something that can be difficult to capture unless you hire an outside 304 00:26:33,070 --> 00:26:39,330 firms to actually do some of those surveys on site the day of the event, because most of 305 00:26:39,330 --> 00:26:45,070 our events are open and are not ticketed. So it's very hard to capture exactly how many 306 00:26:45,070 --> 00:26:51,290 people are coming much less what their demographic makeup is. It would also be great if we could 307 00:26:51,290 --> 00:26:56,210 get some data on the demographics of city staff to see if the people who actually work for this 308 00:26:56,210 --> 00:27:02,670 city and sort of the community represent the community as well. Political affiliation I think would 309 00:27:02,670 --> 00:27:07,370 interesting but there are challenges pulling that type of data in the state of Georgia because 310 00:27:07,370 --> 00:27:14,090 we do not have party registration. There are ways to kind of extrapolate people's party registration, 311 00:27:14,630 --> 00:27:20,690 but it's a little bit more in depth than it's kind of complicated. Speaking of quantitative things 312 00:27:20,690 --> 00:27:25,350 that we really are really interesting to get, but are very difficult to capture would be migration patterns, 313 00:27:25,770 --> 00:27:32,650 meaning where people coming from when they move into the city and where are they going when they 314 00:27:32,650 --> 00:27:34,490 It's very difficult to capture. 315 00:27:35,890 --> 00:27:39,570 There's a question, there's not really anything that tracks that, right? 316 00:27:39,730 --> 00:27:41,330 What we came to our conclusion? 317 00:27:41,770 --> 00:27:42,090 You came to our conclusion. 318 00:27:42,750 --> 00:27:42,910 You came to our conclusion. 319 00:27:45,140 --> 00:27:49,080 You came to our conclusion, but when they come here, but when they leave here, we have no way 320 00:27:49,080 --> 00:27:51,280 of knowing we're on our thermoving too. 321 00:27:52,120 --> 00:27:54,960 Which actually would probably be the most useful piece of data. 322 00:27:55,680 --> 00:27:56,680 Also occupation. 323 00:27:57,620 --> 00:28:02,960 Christian started to look at occupation, but even more than race as a changing definition 324 00:28:02,960 --> 00:28:08,420 over time, occupations really change from census to census, just in 10 years. 325 00:28:08,880 --> 00:28:13,280 I mean, you think about some jobs that we have now in the tech industry, they didn't exist 326 00:28:13,280 --> 00:28:17,460 five years ago and much less 10 years ago. So it's very hard to capture occupational trends 327 00:28:17,460 --> 00:28:19,520 in the city over time and what people are doing. 328 00:28:23,150 --> 00:28:25,310 So that kind of brings us to our final point, 329 00:28:25,490 --> 00:28:29,750 which are, I'm sorry, Casey. No, they're at a hand. Unless you did it in some very general 330 00:28:29,750 --> 00:28:36,730 terms, this is a service industry, this is a manufacturing industry, this is a 331 00:28:36,730 --> 00:28:40,290 tech industry, something that just very general terms we might 332 00:28:40,290 --> 00:28:45,590 get some handles on it, because clearly, you know, it's more of a service 333 00:28:45,590 --> 00:28:51,570 than a work force today than it was 25 years ago, we know that, so you could 334 00:28:51,570 --> 00:28:54,730 see some trends like that if you did it in a very general process. 335 00:28:55,570 --> 00:29:01,950 I don't know about the migration patterns. Do you know if any of the real estate folks 336 00:29:01,950 --> 00:29:09,250 keep track of any of that data and we get a wholesale information? I wonder if we have 337 00:29:09,250 --> 00:29:17,850 to answer those sources to at least get a snapshot look at how many houses are being sold 338 00:29:17,850 --> 00:29:24,710 because an elderly past person passes away or they go to live with a family member. 339 00:29:24,710 --> 00:29:31,810 that doesn't live indicator or that might be one source that we could not get exact figures, 340 00:29:32,210 --> 00:29:34,030 but certainly trending data. 341 00:29:34,550 --> 00:29:38,530 Now, that's a really great idea. I just wrote it down. We'll look into that and see what our options 342 00:29:38,530 --> 00:29:46,130 are with that. So, you will actually start talking about some options that the city can do to 343 00:29:46,130 --> 00:29:52,250 encourage diversity already when you mentioned that 25 to 34-year-old age demographic and how 344 00:29:52,250 --> 00:29:55,350 There's not a lot of housing options in the city for them right now. 345 00:29:55,990 --> 00:29:58,530 And we're doing a couple of things to try and address that. 346 00:29:58,550 --> 00:29:59,970 One is the three. 347 00:30:00,000 --> 00:30:29,980 The apartment complex is that are currently in development around downtown. The other is the unified development ordinance that I know you're all very intimately acquainted with, that is actually going to have some new zoning options for housing, which means that there might be some options to build smaller cottage court still houses and different things like that that would encourage maybe smaller households which often tend to skew a little bit younger than people who are looking for the larger house where they can have several kinds. 348 00:30:30,000 --> 00:30:30,460 kids. 349 00:30:32,500 --> 00:30:38,160 In terms of economic incentives that the city could do, our size is kind of our challenge 350 00:30:38,160 --> 00:30:44,280 here. A lot of larger cities can actually do more economic incentives than would make sense 351 00:30:44,280 --> 00:30:49,020 for the city of Decayters since we're only 4.2 square miles. So what I'm talking about here 352 00:30:49,020 --> 00:30:54,800 are things like wages, you know, the city of Decayter passing a minimum wage or something like 353 00:30:54,800 --> 00:30:59,520 that doesn't really make a lot of sense since most of the people who live in the city of Decayter 354 00:30:59,520 --> 00:31:05,320 actually don't work in the city of Decatur. They work, you know, Emory, CDC, Downtown Atlanta, 355 00:31:05,580 --> 00:31:14,060 Midtown, Primeter, so anything along those lines really probably wouldn't impact the household 356 00:31:14,060 --> 00:31:22,120 income residents in the city. In terms of transit, we actually are very unique especially for the 357 00:31:22,120 --> 00:31:28,300 Atlanta area than that we have three transit stations in the city limits of Decatur and we are actually 358 00:31:28,300 --> 00:31:31,880 working with Marta to redevelop some of those empty parking lots, you know, the 359 00:31:31,880 --> 00:31:36,180 avondil station, which I think will also help with that multi-family housing we 360 00:31:36,180 --> 00:31:40,100 are talking about and I believe some of that is actually going to have some senior 361 00:31:40,100 --> 00:31:42,140 targeted housing as well. 362 00:31:45,090 --> 00:31:48,530 Oh senior and targeted income. So that's obviously not 363 00:31:48,530 --> 00:31:53,130 going to be built tomorrow but that's in our long-range plans already. Also with 364 00:31:53,130 --> 00:31:57,750 transit is when you're talking about that 25 to 34 year old age range, the 365 00:31:57,750 --> 00:32:04,470 shirt shows time and time again and with where people my age choose to live that we're 366 00:32:04,470 --> 00:32:10,050 looking for walkable communities which the cater has obviously invested highly into sidewalks 367 00:32:10,050 --> 00:32:16,170 which are great actually for every age anyone can use them and bike ability so we've been doing 368 00:32:16,170 --> 00:32:21,290 a lot with bike lanes and caros and I know that something that we're continuing to do and a lot of 369 00:32:21,290 --> 00:32:26,630 our long range transportation planning so that seems to be on the right track to help kind of encourage 370 00:32:26,630 --> 00:32:32,090 some younger residents to move into the city and then finally I know we're in the process 371 00:32:32,090 --> 00:32:39,390 of developing a annexation plan and that also will probably give us some options to further 372 00:32:39,390 --> 00:32:45,650 encourage diversity in the city and possibly annex in areas that have a different ethnic 373 00:32:45,650 --> 00:32:50,350 and racial groups than are already living the city and so that will be presents new opportunities 374 00:32:50,350 --> 00:32:51,230 as well. 375 00:32:55,110 --> 00:33:03,170 So questions, this is a fantastic graphic from the U.S. Census Bureau. And I don't 376 00:33:03,170 --> 00:33:08,110 think they use those machines anymore. Although it's the federal government, so I can't 377 00:33:08,110 --> 00:33:08,710 say for certain. 378 00:33:11,550 --> 00:33:17,890 There are two things that come to my mind. I certainly understand us why to get information 379 00:33:17,890 --> 00:33:23,550 of for those who actually attend our events on the square and so forth, but I've always wondered 380 00:33:23,550 --> 00:33:31,430 about those who do not. What is it that folks aren't coming to? What is it that residents 381 00:33:31,430 --> 00:33:33,490 who have ever are missing? 382 00:33:35,730 --> 00:33:38,750 And then another thought that came to mind is we were looking at those 383 00:33:38,750 --> 00:33:44,690 graphs and so forth. I wonder if there's any place where there's kind of a layering of 384 00:33:44,690 --> 00:33:52,250 of age and what's happening with real estate and tear downs and reconstructions and that type 385 00:33:52,250 --> 00:33:52,550 of thing. 386 00:33:52,850 --> 00:33:58,690 I think I have an idea of what has happened of course it is all assumptions and so forth. 387 00:33:58,890 --> 00:34:04,730 But I wonder if there are resources where those can be layered on top of each other to 388 00:34:04,730 --> 00:34:05,930 give us a sense of what's happening. 389 00:34:06,550 --> 00:34:10,190 I think that that would definitely be something for further research because it'd be something 390 00:34:10,190 --> 00:34:11,610 of a multi-step process. 391 00:34:12,050 --> 00:34:16,090 I know that having taught to our design environment 392 00:34:16,090 --> 00:34:18,410 and construction division before I'm this issue, 393 00:34:19,110 --> 00:34:20,970 we've only really been tracking tear downs 394 00:34:20,970 --> 00:34:24,050 for a couple of years now as distinct from renovations. 395 00:34:25,150 --> 00:34:27,130 So the data doesn't go back very far, 396 00:34:27,210 --> 00:34:30,070 but it would be interesting, I think, to layer that, 397 00:34:30,070 --> 00:34:31,830 even just for a couple of years, 398 00:34:32,050 --> 00:34:34,870 to kind of see where those intersections happen. 399 00:34:35,390 --> 00:34:37,810 So I'd be pulling that real estate data 400 00:34:37,810 --> 00:34:41,650 and the permitting data, and matching them up. 401 00:34:46,600 --> 00:34:49,340 I have a question for Kristen, that's OK. 402 00:34:49,800 --> 00:34:51,960 How are the 21 people selected? 403 00:34:52,320 --> 00:34:56,200 And can you tell us just, I mean, you highlighted some of the folks, 404 00:34:56,480 --> 00:35:00,700 can you tell us kind of the basic demographics that you looked at? 405 00:35:01,080 --> 00:35:04,720 Well, the most important qualification was that they'd 406 00:35:04,720 --> 00:35:07,000 be interested in the had time to do it this summer. 407 00:35:07,940 --> 00:35:17,380 But I did try to not have all people that lived in a certain neighborhood, I tried to have people that had moved out of the city and lived just on the city limits. 408 00:35:17,700 --> 00:35:27,160 I tried to be as diverse in any other variables that describe them as I could, but it's pretty limited with only 21 people. 409 00:35:27,820 --> 00:35:32,620 Were there any people in that say 18 to 24 age groups in that? 410 00:35:32,620 --> 00:35:35,380 Not in that young age group. No. I think the youngest was 32. 411 00:35:44,720 --> 00:35:45,340 Is that it? 412 00:35:45,960 --> 00:35:50,260 Oh, what's that? Thank you. Tell us now. Tell us for what's next for you. 413 00:35:51,540 --> 00:35:56,060 All right. Well, uh, we're working on another project. So I... 414 00:35:57,040 --> 00:35:59,640 I'm the biggest. I'm the biggest. Thank you. 415 00:36:02,060 --> 00:36:03,060 Cool. That's good. 416 00:36:04,220 --> 00:36:06,060 Did we kind of like a phase two of this? 417 00:36:06,060 --> 00:36:12,920 Okay, that's great. And you said that there was a report. Is there a full-on report that we're 418 00:36:12,920 --> 00:36:16,480 there is? We're wrapping it up. Now, we're doing some last minute editing and 419 00:36:17,620 --> 00:36:20,660 cross-reference in data. Okay. Well, will there be specific 420 00:36:21,600 --> 00:36:25,000 next steps if you would recommend or recommendations as to 421 00:36:25,000 --> 00:36:31,140 what you think might be the next? Yeah, absolutely. It's more developed than what's in the slide, for sure. 422 00:36:33,530 --> 00:36:35,890 Nice job. Really, really, seriously. 423 00:36:36,430 --> 00:36:41,890 Yeah, thank you very much for the presentation and thank you for all that. Good work. 424 00:36:42,950 --> 00:36:45,210 That's good. Thank you very much. 425 00:36:45,210 --> 00:36:47,670 And good luck with whatever it is you're going to do next. 426 00:36:49,490 --> 00:36:56,050 Alright, if that concludes the work session, we'll talk a few minutes break and be back here at 7.