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Hello, my name
is Rettberg,

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it I am majoring in
geography and graduating

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from Portland
State University

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summer term 2021.

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The advisor for my
McNair research project

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is Dr. Joseph Aisha body,

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assistant professor
of geography and

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affiliated faculty
Black Studies

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at Portland State
University.

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My research project is

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titled spatial analysis of

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African-American residency

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and we'll NOMA county

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according to the
2010 US Census.

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This project explores

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the US Census
data to see if

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the shifting
demographic trends

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first of jure by

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Dr. Gibson and bleeding

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out by Anna are
still being

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reproduced in the
contemporary urban space

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of Portland, Oregon.

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I employed GIS software,

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that is geographic
information systems,

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to measure the amount of

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African-American
residency found

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in the neighborhoods
individually.

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And we'll NOMA
county as a whole,

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using Moran's one and

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get scored
clustering models.

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This and it lies is

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the dispersion

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of African-American
residents from the

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historically
segregated out by

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the neighborhoods
into the rest of

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them are NOMA county
to determinants.

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A spatial
clustering effects

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show new loci of
ethnic clustering.

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Or if the demographic

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movement out from Albania

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has been to statistically

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randomized reagents.

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From this study,
we can better

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learn how
gentrification causes

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demographic shifts
within multiple zones

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of the urban area.

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Additionally, this
project demonstrates

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a method by which
future research

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produced with these models

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using a broader scope of

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racial and income data

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over a longer period can

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be employed to track
shifting demographics

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for the purposes
of ensuring

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equitable distribution of

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public resources

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between different
neighborhoods.

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This project answers
the questions.

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Does the currently
available in

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US census data continue to

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support that
demographic shifts

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previously outlined
and Dr. Gibson,

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2000 bleeding now by Anna,

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does the data show
a continuation or

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reversal of prefect
previous trends?

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Half black
residents leaving

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the alpine and
neighborhoods clustered

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together in new
ethnic enclaves,

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or have they spread
out across the city,

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census tracts and
seemingly randomized,

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but often economically
driven patterns.

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Before we go further,

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let's discuss

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the limitations
of this project.

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While originally can see

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that a study comparing

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2010 and 2020 census data,

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multiple delays in

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the US Census
Bureau's releasing

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of the 2020
census results.

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Because of these delays,

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the tiger line shapefiles

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necessary for
spatial analysis of

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2020 census data were
not available from

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the Oregon spatial
data library in

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time for GIS analysis
in this study.

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Additionally,
no tiger line

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shapefiles could
be found in

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the 2019 American
Community Survey

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for the
corresponding areas.

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This produces a
severe limitation.

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And the results
of this study,

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due to the lack

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of modern
demographic patterns

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that could be shown with

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more recent census data.

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This limitation can be

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resolved in
later research.

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After all, 2020
census data

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has been encoded
public access.

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The Moran's one model
uses principles laid

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out by PAP Miranda to

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measure the spatial
autocorrelation between

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a single point in

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a series of
nearby locations.

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A value between one and

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negative one is
assigned with

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perfectly even
distribution across

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an area given is a
value of negative one.

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While extremely
segregated returns

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are assigned a value of

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one and randomized returns

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are assigned a value of 0.

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If you imagine
a set of dice,

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the five pips show
even distribution,

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and six pips show
extreme segregation.

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The results of
our study show

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high and low clustering in

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pale red and
pale blue deal

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with outliers and
bolder colors.

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These outliers
represent census tracts

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with high clustering of

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black residents
surrounded by

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low clustering
and vice versa.

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Track showing
these outlier

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results are areas of

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current demographic
shift where

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longtime black residents
are living near,

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but not necessarily among

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clusters of
non-black residents.

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These outlier tracks or

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areas of interests for

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gentrification research.

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And the Moran's one
model and figure

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B may show us the
clearest picture of

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which neighborhoods
are in the midst of

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demographic
shifts to get us,

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or does that get

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us 4D model here
uses that get us,

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or in general g
statistic to measure

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the amount of high value

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and low value clustering.

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There isn't a given area.

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This is different
from Iran one.

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In that Miranda
one Measures

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Clustering Using

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the distance between
point locations.

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While it get us 4D
Measures Clustering by

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comparing the density of

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values within
a given area.

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Well, not a perfect
analogy for those

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familiar with
graphic design

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and computer modeling.

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This difference is
conceptually similar to

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the difference
between vector

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and raster images.

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Get a sword Measures
Clustering by comparing

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the density of values
in each area to it in,

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to an expected value

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corresponding to the
overall average.

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The resulting model shows

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hot spots and cold spots,

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or high density and

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low density African
American residency.

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In our case.

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These hotspots are rated

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by their statistical
confidence.

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This statistical
confidence rating measures

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how

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similar the given
sample is to

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the overall
population using

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the confidence
intervals of

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a generally distributed
bell curve.

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And some very complex
mathematics that I

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mostly outsource to
the GIA GIS software.

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We can determine the
statistical level

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of accuracy within
our models.

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To the given
percentages. You get.

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A 4D model shows us

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in which census tracks
we see a higher or

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lower than average
density of

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black residency
when compared

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to the city at large.

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Mutual color tracks fall

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within the
statistical average.

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While in the West
Hills neighborhoods

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and regions
southeast Portland,

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west of mouth Taber,

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we see percentage rates of

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black residency much lower

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than the city average.

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Most notable for
planning purposes,

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we see that much
of the movement

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of black residents out of

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the albino neighborhoods
have been into

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census tracks
farther north

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and west developed by NA,

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into other north
portland neighborhoods.

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We also see a second
clustering of

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black residency in

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several census tracts
and east portland,

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reflecting the
growing population

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of black residents in

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the formerly white flight

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suburbs of Rockwood,

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an area known
colloquially as

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the numbers in
which light,

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which like
albino, has been

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seeing large
demographic shifts in

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both racial and
economic demographics

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over the past few decades.

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Conclusion, this
study found that

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the currently available
US census data

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shows a continuation of

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the demographic shifts

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previously outlined in Dr.

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Gibson's 2007
bleeding out by now.

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However, given
the limitation of

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only using 2010
census data,

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this is not as substantial

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as a result as
the study had

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previously hope to
find by comparing

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2010 to 2020 census data.

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What we find with
these models

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is that it can be said

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with the confidence rate
of 90 to 99 percent,

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but the areas outside of

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the outline and
neighborhoods where

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black residents
have moved had

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not been in a
randomized pattern

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as shown by the Get us or

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statistical
reports seen here.

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Rather we find ongoing
ethnic clustering in

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residents moving out of

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the outline and
neighborhoods.

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This study shows that

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ongoing exploration

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into the structural
factors that

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govern demographic
shifts in

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Portland neighborhoods
are necessary.

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Like like what?
Likewise, we

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need to analyze what,

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if anything, in

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the built environment

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differentiates
neighborhoods.

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That's the high
ethnic clustering

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from those which do not.

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If clustering
patterns are shown to

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correlate with lower
income neighborhoods

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that lack infrastructure

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improvements than the City

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of Portland has a moral
and fiduciary duty

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and responsibility
to its citizens

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to invest public
resources and in

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providing equal access

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to public
transportation and

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safety infrastructure for

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all Portland residents.

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Thank you all for your
time and attention.

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I will now be
taking questions.