Report: Applied Planning - Galveston A fact base for the future

A Thoroughfare Classification Plan for Galveston, Texas

Transportation Element

Applied Planning II ‐ Transportation Texas A&M University Avinash Shrivastava, Li Wenhao, Josh Shane, Matt Sandidge

Table of Contents A Thoroughfare Classification Plan for Galveston, Texas ....................................................................... 1 Introduction ........................................................................................................................................................... 1 Functional Classification Theory .................................................................................................................. 2 Methodology .......................................................................................................................................................... 3 Definitions .............................................................................................................................................................. 5 Functional Street Classification ..................................................................................................................... 5 ARTERIALS ........................................................................................................................................................ 6 COLLECTORS .................................................................................................................................................... 6 LOCAL .................................................................................................................................................................. 9 Conclusions/Implications ............................................................................................................................. 11 Appendix ................................................................................................................................................................... 12 References ................................................................................................................................................................ 14

A Thoroughfare Classification Plan for Galveston, Texas

Introduction The purpose of this report is to serve as a preliminary functional classification system for Galveston, Texas. Additionally, this plan can be used as a tool for shaping land use planning and guiding development. The primary purpose of the thoroughfare plan is to aid the transportation planning process so that comprehensive planning goals may be achieved. It is important to understand the role of the streets in order to effectively shape planning and development decisions. This plan is broken into the following four sections:

Functional Classification Theory provides the main idea of how and why streets are classified into their unique category.

Methodology provides the data and method used to determine the classification of all streets in Galveston.

Definitions provide the definitions of each street classification.

Conclusions/Implications provide recommendations for on‐going monitoring and updating of the functional classification map.

At a minimum, the thoroughfare plan should provide a basic functional classification of roadways. A functional classification system designates all streets by the way in which each street serves the network. While the classification system is the most important element, there are many other elements which may be included in the thoroughfare plan.

The plan can also provide recommendations for future roadway improvements, street design recommendations, and address intermodal facilities and function within the network system.

Before the next four sections, it is important to understand that this report only performs a functional classification on the existing network. It does not project future street classification. Our main goal for this document is to be used as a starting point in working towards a complete thoroughfare plan that addresses current and 20 year transportation goals.

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Functional Classification Theory There are many benefits that result from classifying a network’s streets. As streets become classified, they are able to take common shapes which provide for consistent cost estimates. A classification system enables planners and developers to project future lane requirements, create design standards by type of classification, and right‐of‐way needs and costs. Additionally, classification creates a sense of consistency that enables effective planning and construction estimation of costs. If a minor arterial needs to be upgraded, the nearest major arterial can serve as a reliable example of right‐of‐way needs, lane requirements, and design standards.

Streets are classified based by their role and function. The role and function of a street is characterized by the amount of movement and access it allows. The types of streets that would have the highest vehicular movement rather than access would be labeled freeways and arterials. The streets with more access points, thereby decreasing vehicle speed from increased stops, would be considered collectors and locals (Stover & Koepke, Transportation and Land Development, 2006). Figure 1 is a conceptual graphic of how streets function; some contain more movement and others more access. The ideal hierarchical structure of a road network suggests that roads with particular functions should only be accessed from the next highest classification. For example, a major arterial should only be accessed via a minor arterial, or a collector is meant to provide access from a local street to an arterial street.

Figure 1. Functional Classification Theory

Source: Transportation and Land Development by Vergil G. Stover and Frank J. Koepke

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Methodology Data Collection and Management This section describes how to begin to assemble necessary data to classify a network. To start, we must mention that our classification relies heavily on the use of GIS software. The first step in the process should be to obtain and assemble traffic count data for as many streets as possible; preferably, data will be in the form of Annual Average Daily Traffic (AADT) counts. Current and past counts can provide estimates of future traffic counts in case future counts are not available.

Our dataset comes from the most recent County Appraisal District (CAD) streets and railroad data, September 2009. This data was selected as our basic data for this thoroughfare plan as it represents current thoroughfare conditions of the county. This data also included a basic functional classification for each street. From the data we were able to obtain 3 shapefiles (streets, railroads, and parcels). By categorizing these shapefiles in GIS, a projected basic functional classification is illustrated.

The most detailed and comprehensive street functional classification data was found in the Street_CAD data, which already included 3 different street classification types with 4 code types. These original classifications are listed and described below: Table 1. Baseline Classification

Code Description 1

INTERMEDIATE

2

MAJOR

3

LOCAL

The baseline dataset, used as the starting point for analysis, was to be compared to other sources of data to determine inconsistencies in potential functional classifications.

Additional data source and criteria In addition to the above data, we also acquired Galveston traffic count data from TxDOT via Texas Transportation Institute (TTI). The TxDOT dataset was used to add official TxDOT street counts to the projected classification created by the initial dataset.

Inserting this data into our existing dataset enables us to project TxDOT counts. Next, we

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created a final street classification that will be used in the thoroughfare plan. The categories are summarized in the table below.

Table 2. Final Street Classification

Code Description 1

Free Way

2

Major Arterial

3

Minor Arterial

4

Collector

5

Local

This list of street classifications was used to reclassify our preliminary datasets.

This was accomplished in GIS by creating a new field to the baseline street dataset. In this new field, the newly assigned classification for each segment of street was placed. This provided quick reference to the street classification information.

Methodology of Street Classification When the streets are categorized in GIS by AADT counts, GIS illustrates streets with higher amounts of daily traffic by thicker bands over the street. For example, local streets would are visually represented by thin, narrow bands, while major arterials are thick bands. To create our classification tables and codes, we referenced the Federal Highway Administration (FHWA) Functional Classification Guide as our primary basis for determining street classifications. The individual street classifications are defined in a later section in the report. Additionally, our final GIS product, illustrating a current thoroughfare classification, can be found in the appendix to this report.

Limitations The traffic count data TxDOT provided has reliability problems according to TTI officials who provided the data. As a disclaimer, the obtained data was prepared for internal use only within TxDOT and accuracy is limited to the validity of available data as dates shown. In essence, the counts cannot be concluded as 100% accurate. Therefore, any data obtained and resulting GIS analysis was balanced against the judgments of the group 4 | P a g e

before final classifications were determined. Additionally, it should be mentioned that we did not include data outside of Galveston Island, other than highway and railroad data, as part of our classification analysis.

Definitions Functional Street Classification A system of classifying roadways based on their capacity, volume of flowing traffic, function (the type of traffic typically handled by the roadway), origin, connectivity to other streets, speed limits, and number of lanes. Roadways are classified as Freeway, Major Arterial, Minor Arterial, Collector, and Local streets from the highest, most intensive function to the lowest. We referenced the FHWA Functional Classification Guidelines website and the Transportation and Land Development (TLD) 2nd Edition for guidance in classifying the network. The definitions from these two references are analyzed below. It is also important to recognize that streets with the same classification will function differently in urban and rural areas. For the purposes of this report, we refer to urban and small urban classification systems as Galveston’s population is above 25,000, but possibly below 50,000. As a guide throughout this section, table 3 illustrates the fundamental characteristics of each functional classification.

Table 3. Fundamental Characteristics by Classification Type

C R I T E R I A

Capacity Traffic Volume Traffic from Traffic to Speed Limits Number of Lanes

S T R E E T C L A S S I F I C A T I O N ( F U N C T I O N A L )

Minor Major Minor Arterial Collector Collector Local Higher High High Low Higher High High Low Major Freeway Major Arterial Minor Arterial Collector Minor Collector Minor Major Minor Abutting Land Arterial Collector Collector Local Use 35 ‐ 55 mph 30 – 45 mph 25 ‐ 35 mph 25 – 35 mph < 25 mph

Major Arterial Highest Highest

4 or > 4

4 or < 4

2 to 3

2 or < 2

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ARTERIALS As table 3 shows, there are two arterial classifications. The TLD definitions for major and minor arterials are summarized as follows. Figures 2 – 5 below illustrate examples of each type in Galveston.

“Major arterials are intended to provide a high degree of mobility and serve longer trips. They should provide for high operating speeds and levels of service. Movement, not access, is their principal function. Major arterials provide continuity for the intercity arterials through urban areas; therefore, they serve most trips entering and leaving urban areas, including pass through trips. The major arterial system interconnects major developments such as the central business district, large suburban commercial centers, large industrial centers, major residential communities and other major activity centers within the urbanized area.” It is also important to understand that major arterials should not invade identifiable neighborhoods.

“Minor arterials interconnect with and augment the major arterial system. These facilities interconnect residential, shopping, employment and recreational activities at the community level. These streets, similar to major arterials, do not penetrate identifiable neighborhoods. They accommodate shorter trips and provide more access than major arterials. Speeds are slightly slower. Intersections with public and private access drives should be designed so that the speed differentials are no more than 10 mph.”

COLLECTORS

Once again, the key characteristics of collectors are illustrated by table 3, at the start of this section, and a summary of TLD definitions below. Figures 6 and 7 illustrate an example of a collector in Galveston.

“Collectors provide land access and movement within residential, commercial and

industrial areas. Collectors may penetrate residential areas, but they should not have continuity through the area. Speeds are slower and traffic volumes are lower than on arterials. More importantly, drivers expect other vehicles to be making turning movements; therefore, higher speed differentials of 15 to 20 mph can be accepted on these streets.”

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Figure 2. Major Arterial ­ Broadway Avenue J / Texas 87

Figure 3. Major Arterial ­ Broadway Avenue J / Texas 87

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Figure 4. Minor Arterial ­ Strand Rear Street / 25th Street

Figure 5. Minor Arterial ­ Strand Rear Street / 25th Street

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Figure 6. Collector ­ 15th Street

Figure 7. Collector ­ 15th Street

LOCAL Local streets are most likely the streets that receive the most daily activity from the majority of a city’s population. A sizable percentage of land is accessed from these streets.

Below is a summary definition of local streets by the TLD. This classification is illustrated by figures 8 and 9 below.

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Local streets serve to provide land access. They can, and do, exist in any land‐use setting.

There can be local residential streets, local downtown streets, and local industrial streets.

The movement on local streets is ancillary and involves traveling to or from a collector.

Travel distance on local streets is short, volumes are low, and speeds are slow. Where residential uses abut local streets, speeds should be 25 mph or less.

Figure 8. Local ­ Avenue G

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Figure 9. Local ­ Avenue G

Conclusions/Implications There are many benefits to classification of each street in Galveston. For one, traffic control can be simplified by arranging streets whose classification works in partnership with the surrounding street classification. As an example, a arterials can be designated and designed to safely move high traffic volumes and speeds, while the connecting.

Additionally, a functional classification system allows for more predictable and effective construction costs. For instance, high volume roads may be constructed to accommodate heavy loads, increasing construction costs, while local streets maybe be completed more cheaply. Using the street classification system helps facilitate appropriate development along streets that are able to accommodate the amount of traffic generated by the development. The City of Galveston should use the functional classification map as a tool for determining appropriate development along the street and design standards creating the areas context. Similarly, the City may also use the map as a tool for identifying future roadway improvements, or for identifying locations where streets can be extended or altered to better meet the needs of the community. It is our hope that Galveston utilize this report as a guide, or starting point, for creating and implementing the City’s transportation plan and companion thoroughfare plan.

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Appendix

Figure 10. Functional Classification of the Current Network

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Figure 11. DRAFT Street Classification System

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References

Stover, V. G., & Koepke, F. J. (2006). Transportation and Land Development. Washington, D.C.: Institute of Transportation Engineers.

Federal Highway Administration. (2000). Planning. Retrieved May 4, 2010from FHWA Website: http://www.fhwa.dot.gov/planning/fcsec2_1.htm#fsc

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Population & Demographic Estimates

May 4

2010

Created by Texas A&M’s Master’s of Urban Planning Applied Planning Studio supervised by Dr. Shannon Van Zandt and Dr. Walter Gillis Peacock.

Submitted by: Dustin Henry Anita Hollmann Courtney Payne Joshua Shane Martin Siwek Susan White Sonja Willems Avinash Shrivastava

Post Hurricane Ike Estimates

Population & Demographic Estimates

2010

Contents Population & Demographic Estimates ............................................................................................................................ 3 Population Estimates ........................................................................................................................................................ 3 Local Characteristics..................................................................................................................................................... 3 Data Sources..................................................................................................................................................................... 5 Multi-Family Household Population Estimate ..................................................................................................... 10 Multi-Family Data Collection................................................................................................................................... 10 Multi-family Data Analysis ....................................................................................................................................... 13 Population Estimates â&#x20AC;&#x201C; Different Scenarios .......................................................................................................... 15 Scenario 1 ........................................................................................................................................................................ 16 Scenario 2 ........................................................................................................................................................................ 18 Scenario 3 ........................................................................................................................................................................ 19 Chosen Population Estimate Scenario................................................................................................................. 20 Demographic Composition of Galveston Population .............................................................................................. 23 Galveston Independent School District Enrollment Totals Analysis........................................................... 23 Utilization of PEIMS Data.......................................................................................................................................... 23 Total Enrollment .......................................................................................................................................................... 24 Overall Demographic Changes in School Enrollment ................................................................................... 25 Distribution of Enrolled Students .............................................................................................................................. 27 Racial and Ethnic Composition ................................................................................................................................... 29 Current Data Estimates ............................................................................................................................................. 29 Past Data Estimates..................................................................................................................................................... 30 Implications and Opportunities....................................................................................................................................... 35

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Population & Demographic Estimates The purpose of Galveston’s comprehensive plan is to serve as a “guide for the management of change.” In the wake of a major disaster such as Hurricane Ike, it is critical for officials to consider how their population has changed – in its size, distribution, and composition – since the comprehensive plan sets the foundation for all city policies, strategies, and actions1.

The goal of this population and demographic estimate is to provide Galveston officials with a snapshot of the current nt size, distribution and composition of its community community,, which is vital for guiding decision makers in their determination of where resources and services should be distributed throughout the community.

Population Estimates The most accurate method to deter determine the population size for a community would be to count each individual resident; however this method can prove to be a challenge due to its intense demand for personnel, time, and resources, as well as other obstacles such as counting groups within the population that might be unwilling to participate, including undocumented immigrants or temporary workers.

Another method to accurately determine a community’s population relies upon several characteristics of the community, which, when plugged in to a formula, can provide us with an a estimate of the population. Known nown as the Housing Unit method, this estimate utilizes a count of the total number of housing units in the community, and combines that count with household characteristics resulting in a fairly accurate estimate. The formula for the Housing Unit method is: Total Population = (HU x Occ x PPH PPH), and a description of the variables is found below:

HU - the number of Housing Units Units,

Occ - the Occupancy Rate for those housing units, and

PPH - the ratio of persons living in each housing unit, known as Persons Per Household.

Household

Local Characteristics 1

According to the 2001 Galveston Comprehensive Plan

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There are a number of local characteristics that should be taken into consideration when estimating Galveston’s population. Particularly, the composition of the built environment on the Island should be considered. For example, much of Galveston’s earliest development occurred around the eastern extent of the Island, known as the Urban Core (shown in the map below).

Urban Core

West End

Figure 1 : Galveston's Urban Core and West End Contrasting Galveston’s more densely populated Urban Core is the sparsely populated West End – characteristic of a hybrid between vacation beach homes and large-lot, single-family suburban sprawl. The table below provides a summary of some of the distinct characteristics captured in the 2000 Census that should be taken into consideration when preparing an estimate of the current population for Galveston’s Urban Core and West End: Table 1 : Summary Table, Galveston's West End and Urban Core Characteristic (in 2000)

West End

Urban Core

Population

6,790 (11%)

55,852 (89%)

Housing Units (HU)

5,473 (18%)

25,375 (82%)

Occupancy Rate (Occ)

47%

85%

Persons Per Household (PPH)

2.22

2.34

46% own / 54% rent

60% own / 40% rent

Tenure (Owner/Renter)

These disparities between characteristics for Galveston’s West End and Urban Core suggest that Housing Unit counts, Occupancy Rates, and PPH ratios specific to the area in the community should be used, when available. Data made available at the Census Tract level can be used to compare Texas A&M University Applied Planning Studio

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these two areas. Census Tracts 7240 through 7258, which terminate approximately at 81st Street, roughly represent sent the area in Galveston known as the Urban Core, and the remaining tracts west of 81st Street (7259, 7260 and 7261) correspond to the area known as the West End, as shown in the map on the previous page (Figure Figure 1).

Acknowledgement of these distinct differences between the Urban Core and West End of Galveston, Galveston as well as the differences between single single-family and multi-family housing, can result in a more mo accurate estimate of the current population. To recognize these distinctions, we have utilized the following model of the Housing Unit method in order to develop a more precise population size estimate, seen in Figure 2, below:

Figure 2:: Housing Unit Method for Galvestonâ&#x20AC;&#x2122;s Population Estimate

Data Sources The disruption caused by a major disaster like Hurricane Ike can limit the utility of existing data sources that were created prior to the event event, such as decennial census counts orr periodic estimates provided by the U.S. Census. Instead, we relied upon a number of primary data sources â&#x20AC;&#x201C; where we were able to collect data by making direct observations about the communit community by visiting a number of properties and by making contact with property owners and managers of multi--family housing.

In addition to these primary sources of data, the following secondary sources of data were utilized in the preparation of our population size and distribution estimate. A summary ry of the types of

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primary and secondary data sources we utilized in making our estimates is provided in Table 2 below: Table 2: Primary & Secondary Data Sources Data

Source

Scale

Availability

Occupancy Rate, Destruction Rate, Ratio of Units (West End to Urban Core), and Household Size (Single-Family)

Damage Assessment, Texas A&M Hazard Reduction & Recovery Center

Parcel

December 2008, and January 2010

Housing Unit Count, Occupancy Rate (Multi-Family)

Multi-Family Housing Assessment, Texas A&M University

Parcel

January-April 2010

Housing Unit count (for the Village of Jamaica Beach)

Galveston County Appraisal District parcel data

Parcel

September 2009

Total Housing Unit Count, Occupancy Rate (all units)

HUD Aggregated USPS Administrative Data On Address Vacancies

Tract

February 2008 to December 2009

Housing Unit count, Occupancy Rate, Household Size, Tenure (owner/renter)

U.S. Census Count

Block Group

1990 and 2000

Housing Unit count, Occupancy Rate, Household Size, Tenure (owner/renter)

U.S. Census Bureau’s American Community Survey

Citywide

Estimates from 2006-2008

Primary Data

Secondary Data

The following is a summary of the available data for the housing characteristics to be included in our Housing Unit model for a population estimate.

Total Housing Units U.S. Census – American Community Survey The City of Galveston had an estimated 33,439 housing units according to the most recent U.S. Census American Community Survey conducted between 2006 and 2008. This estimate is approximately 3% higher than the last count made in the 2000 U.S. Census (which counted 30,848 units). While a reliable estimate, the ACS does not take into consideration the loss of housing units following Hurricane Ike’s devastating landfall in September 2008, which is estimated to have damaged between 75% and 80% of all buildings in the city according to the Galveston Long-Term Recovery Plan. Other data sources exist for which we can get a better idea of the post-Ike count of housing units on the Island, leading us to a more refined population estimate.

U.S. Postal Service Housing Counts Texas A&M University Applied Planning Studio

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Each quarter, the U.S. Postal Service tabulates a count of commercial and residential addresses in each zip code, as well as vacant units. In an agreement with the U.S. Department of Housing and Urban Development (HUD), this data is made available to the public in an aggregated form at the Census Tract level. Information is available from the first quarter of 2008, to the most recent data from the 4th Quarter of 2009, through December 31st. In the most recent quarter, 4Q 2009, a total of 31,899 residential addresses are reported to be within Galveston (after having subtracted the 1,160 housing units in the Village of Jamaica Beach2). This number is a 3.4% increase from the 2000 Census. Since this data is provided to us at a Census Tract scale, a more specified count of the number of housing units on the West End and Urban Core areas of the Island can be calculated (Urban Core = 27,858 and West End = 4,041).

Destruction Due to Ike The Hazard Reduction and Recovery Center (HRRC) conducted damage assessments for a representative sample3 of single-family homes in December 2008 and again in January 2010, as part of a longitudinal study of housing recovery following a major disaster through research funded by the National Science Foundation. Approximately 2.7% of the homes assessed in January 2010 were determined to be uninhabitable due to having been severely damaged or completely destroyed by the hurricane. This destruction rate can be applied to a pre-hurricane total housing unit count, such as the 2006-2008 American Community Survey estimate, in order to also determine the total number of units on the Island.

Total Housing Unit Totals, Summary The most recent count by the U.S. Postal Service appears to be a reliable source for a total count of housing units in Galveston. The USPS housing unit count from the 4th Quarter of 2009 falls between the count of units in the 2000 Census (3.4% greater) and the 2006-08 American Community Survey (ACS) estimate (4.6% less). The 4th Quarter USPS count is less than 2% from the 2006-08 estimate, after having applied the destruction rate calculated by the HRRC to the ACS figure. Table 3 below, provides a summary of the Housing Unit counts, by geography, from our collection of data sources.

2

The Village of Jamaica Beach is a separate municipality from the City of Galveston. Utilizing parcel data from the County Appraisal District, 1,160 housing units are estimated to be within the jurisdictional boundaries of Jamaica Beach. This total is subtracted from housing unit totals for Census Tract 7261, which encompasses the entirety of Jamaica Beach and portions of Galvestonâ&#x20AC;&#x2122;s city limits.

3 A random sample of 1,166 single-family homes was drawn, using parcels coded as single-family residences according to the Galveston County Appraisal District. The units assessed represent approximately 3.5% of the total housing in Galveston.

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Table 3 3: Housing Unit Totals, Summary Table Source

Urban Core

West End

Citywide

Census 1990

27,270

4,412

32,510

Census 2000

25,375

5,473

30,848

ACS 2006 2006-08

n/a

n/a

33,439

ACS 2006-08, 08, after having applied HRRC Destruction Rate

n/a

n/a

32,523

USPS 4Q 2009

27,858

4,041

31,899

Average of USPS Quarters, since Ike

27,752

3,937

31,689

To account for the spatial distribution of single-family family units in the West End and Urban Core, C we calculate a ratio of how many housing units are located in each area. This calculation is based upon all single-family family properties that were included into the HRRC Damage Assessment and their location. Table 4 summarizes the results and shows that the majority, i.e. 73% of single-family single units are located within the Urban Core.

Table 4: Single Single-Family Units in Urban Core versus West End SF Units in Urban Core vs. West End HRRC Damage Assessment Ratio

Urban Core

West End

Total

827

304

1,131

73.12%

26.88%

100%

Occupancy Rates Occupancy rates collected from our data sources vary widely. In particular, the rates reported by the U.S. Postal Service appear to be defined by different criteria than the U.S. Census, us, as they are considerably higher than recent estimates and observations. The direct observations made by the HRRC Damage Assessment in December of 200 2008 8 and again in January of 2010 reflect what we might expect to observe immediately following a major disaster, and in the year that follows.

Figure 3 to the right shows us how in December 2008 the occupancy rate observed in the HRRC damage assessment was considerably lower than the pre-hurricane estimate, which could be expected with a high number of households Texas A&M University Applied Planning Studio

Figure 3: Occupancy Rates for Galveston Households

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having been displaced by the hurricane at that time. The most recent observation made in January 2010, shows us how the occupancy rate has begun to return to the pre-hurricane level.

HRRC Damage Assessment – Occupancy Rate While the HRRC Damage Assessment has some limitations4, the most recent assessment in January 2010 appears to be the most plausible rate that could be applied to the community’s housing stock in determining an occupancy rate. The January 2010 assessment follows the trend demonstrated by previous observations of occupancy rates (the 2000 Census and 2006-08 American Community Survey); especially when considering the extent of the population loss expected after a major disaster such as Hurricane Ike. Occupancy Rates, Summary A summary of the Occupancy Rates from our data sources and their corresponding geography are found below, in Table 5.

Table 5: Housing Occupancy Rates, Summary Table Source

Urban Core

West End

Citywide

U.S. Census, 1990

81.7%

43.6%

75.6%

U.S. Census, 2000

84.5%

47.2%

76.1%

U.S. Census ACS Survey, 2006-2008

n/a

n/a

67.9%

USPS, mean of 4Q 2008 to 4Q 2009

93.6%

87.7%

92.7%

USPS, 4Q 2009

93.7%

88.0%

92.8%

HRRC Damage Assessment, 12/2008

49.2%

38.0%

45.7%

HRRC Damage Assessment, 01/2010

70.6%

58.0%

66.6%

Persons Per Household / Average Household Size The ratio of persons per household is the final household characteristic used in our Housing Unit model for estimating Galveston’s population. As we have observed with Occupancy Rates, the Persons Per Household ratio in Galveston deviated considerably following Ike. Galveston has been trending toward a lower ratio of PPH in recent years, with the most recent estimate in the 2006-08 American Community Survey being lower than the ratio for both the Urban Core and West End, as recorded in the 2000 Census, and shown in Table 6 below.

4

The HRRC damage assessment is for single-family households

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Table 6: Persons Per Household Ratios in Galveston, Summary Table Source

Urban Core

West End

Citywide

U.S. Census, 1990

2.38

2.25

2.32

U.S. Census, 2000

2.34

2.22

2.28

U.S. Census ACS Survey, 2006-2008

n/a

n/a

2.21

HRRC Damage Assessment, 12/2008

2.57

2.41

2.53

HRRC Damage Assessment, 01/2010

2.37

2.16

2.33

In December 2008, just a few months after Hurricane Ike hit Galveston, PPH ratios observed for both the Urban Core and West End were considerably higher than any that had been observed prior to the hurricane; providing evidence that, in at least some households, families are doubling up into one home as they make repairs to another.

Multi-Family Household Population Estimate Multi-Family Data Collection From January to April 2010, students from the Applied Planning Studio class at Texas A&M University conducted an assessment of multi-family housing units on Galveston Island. Around 300 properties were visited in person by students, with the remaining properties contacted by a followup phone call. Property owners and managers were asked how many units exist on the property and the overall Occupancy Rate for those units.

Initial List of Properties First, the team acquired an existing list of multi-family properties derived from County Appraisal District (CAD) data in the form of an Excel spreadsheet. Using Geographic Information Systems (GIS), team members mapped out the multi-family properties on the Island by joining the spreadsheet to the September 2009 CAD parcel data GIS file, which was the latest available data.

Once the multi-family parcels were identified, it was easier to visually see where all of the properties were located on the Island and also identify concentrations of properties, as well as large versus small properties according to parcel size.

Site Survey Team members physically visited the multi-family properties on several days throughout February and March 2010. To facilitate efficiently physically visiting all of the sites, the map of multi-family properties was divided into three roughly equal sections according to geographic location: Texas A&M University Applied Planning Studio

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Population & Demographic Estimates

East of 26th Street

26th Street - 61st Street

West of 61stStreet

2010

The teams strategically visited properties using the lists generated by the CAD data and also used accompanying maps. The team visited the property and first assessed how many units could be seen. For larger properties, the team identified the leasing office and inquired information from the property manager. If someone was available to speak with the team members, questions were asked as shown below in Figure 3.

Parcel_ID

Date:

Time:

Surveyor Initials:________

MULTI-FAMILY OCCUPANCY Property Name: Is this property in operation? (CIRCLE ONE)

Yes

No

Street Address:

Zip Code:

Mailing Address:

Zip Code:

Name of Owner: Phone Number Owner:

Name of Manager: Phone Number Manager: Before Ike

Now

No. of Permanent Units Occupancy Rate Permanent

No. of Seasonal Units Occupancy Rate Seasonal

Figure 3: Multi-Family Survey Questions

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If no one was available, the team counted doors, mailboxes, utility meters, windows with plants in them, cars, and other signs of occupancy to help determine the number of units there were on the premise, and how many of them appear to be occupied. We also spoke with residents, neighbors, and construction workers to get estimates for certain properties.

Several properties posted and hung signs around the units with phone numbers to call for sale or leasing information. We noted these phone numbers to make follow-up calls to check the validity of gathered information and to get information on properties where we did not get the relevant information.

The original multi-family list had 253 properties on it, however upon surveying the properties, the team found approximately forty properties that were not on the list and added these to the list for a final number of 292 properties.

Telephone Survey Contact persons were not available at all properties. In such an instance the team members gathered available data generally consisting of the name of the multi-family property if available and any posted phone numbers or contact persons. The list was then updated with the new contact information and divided among team members to call properties where no one was available to speak with in person. The telephone survey asked questions similar to those asked in person on site, but was sometimes limited by the personâ&#x20AC;&#x2122;s willingness to share the information over the telephone. The questions the team prioritized asking in this instance were:

How many units currently exist on the property?

How many are occupied? What is the occupancy rate?

How many units existed before Hurricane Ike?

What was the occupancy rate before Hurricane Ike?

Institutional and Public Multi-Family Residences For the institutional residences, such as the dorms located at the University of Texas Medical Branch, Texas A&M Galveston, and Galveston College, the team found contact information on the institutionâ&#x20AC;&#x2122;s website and called or emailed to receive the number of units that exist at the property, also giving the occupancy rate. Justin Herter, Public Information Officer for the Galveston Housing Authority gave information regarding the senior and disabled public housing developments, Holland House and Gulf Breeze.

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Multi-family Data Analysis After having conducted a primary data collection on housing units and their occupancy rates for multi-family housing units in the City of Galveston, we analyzed this data in order to obtain a population estimate.

Summary of Available Primary Data The collected data can be divided into the following categories:

Properties with a known number of housing units and a known occupancy,

Properties with a known number of housing units and an unknown occupancy,

Properties with an unknown number of housing units and an unknown occupancy,

Vacant and demolished properties, and

Properties with other land uses than multi-family residential.

The survey reveals that besides multi-family residential uses, there were 7 properties with other land uses. These properties were included into the survey at the beginning because they were marked as multi-family residential by the County Appraisal District (CAD) data. Additionally, 21 properties and 7 public housing properties are categorized multi-family residential by CAD, but were vacant and to be demolished or had already been demolished by the time the survey was conducted. These properties were excluded from the analysis of the survey. For 11 properties we have been unable to get reliable data on the number of housing units.

Multi-Family Housing Units Table 7 illustrates a summary of the remaining 246 properties that have been included into the survey. Similar to the housing categories of the Census, the properties were divided into 5 different categories according to the number of units within the property. This categorization is necessary to obtain a more accurate population estimate since a small multi-family property with less than 10 units shows different occupancy patterns than a large apartment complex. The categorization accounts for this phenomenon. It is to be noted that our definition of multi-family properties includes three units or more, which means that we excluded 15 properties of duplexes from the analysis.

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Table 7: Summary of Collected Multi-Family Data Housing Unit Category

Multi-Family Properties

Units with known occupancy

Units with unknown occupancy

Sum Housing Units

3 or 4 units

50

143

50

193

5 to 9 units

64

310

123

433

10 to 19 units

43

363

173

536

20 or more units

49

6,579

695

7,274

Occupancy Rates Based upon the number of housing units for which we were able to obtain occupancy counts, we computed an average occupancy rate as shown in Table 8. This rate reflects the percentage of housing units that were occupied at the time the survey was conducted.

Table 8: Calculation of Occupancy Rates Housing Unit Category

Units with known Occupancy

Occupied Units

Average Occupancy Rate

3 or 4 units

143

104

73%

5 to 9 units

310

182

59%

363

203

56%

6,579

4,900

74%

10 to 19 units 20 or more units

The computed occupancy rate is believed to reflect the best measure in estimating occupancy rates for the housing units in which adequate information was not gathered during the survey. For this reason, the average occupancy rate is applied to the 1,045 housing units with unknown occupancy.

The process results in the following data:

Total Housing Units:

Total Occupied Housing Units: 6,112

8,436

Household Size According to the formula used to estimate the population, the total number of housing units is now multiplied by the number of persons per household to get an estimate of the people living in multiTexas A&M University Applied Planning Studio

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family housing units. Table 9 shows an estimate of persons per household according to the American Community Survey Estimates 2006-08. For the purposes of estimating population living in multi-family households, the household size of renter-occupied housing units was chosen.

Table 9: Average Household Size in Persons per Household Average Household Size (from 2006-2008 ACS): Renter-Occupied 2.10 Owner Occupied 2.35

Spatial Distribution of Multi-Family Units To account for the spatial distribution of multi-family units in the West End and Urban Core, a ratio of how many housing units located in each area was calculated. This calculation is based upon all multi-family properties that were included in the survey and their location. Table 10 summarizes the results and shows that the majority, i.e. 90% of multi-family units, are located within the Urban Core.

Table 10: Multi-Family Units in Urban Core versus West End MF Units in Urban Core vs. West End

Units

Ratio

Urban Core

7,709

90.26%

842

9.86%

8,541

100%

West End Total

Population Estimates â&#x20AC;&#x201C; Different Scenarios Due to the character of data sources that were used in estimating the number of single- and multifamily housing units, occupancy rates and household size, three different scenarios of total population estimates were considered. This was accomplished to give a range of population estimates, accounting for incompatibilities of different data sources and in validating the results of the above described surveys and data estimates.

Three different scenarios have been calculated â&#x20AC;&#x201C; resulting in a low, medium and high population estimate for the city of Galveston. The data sources used to compute these scenarios are summarized below in Table 11.

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Table 11: Data Sources used to compute Population Estimates Scenario

Single-Family Units

Multi-Family Units

Low (1)

ACS Data with HRRC Damage Rates

TAMU Multi-Family Survey

Medium (2)

ACS Data with HRRC Damage Rates

ACS Data with HRRC Damage Rates and TAMU Survey Occupancy Rates

High (3)

USPS Data

ACS Data with HRRC Damage Rates and TAMU Survey Occupancy Rates

Scenario 1 (Low) Single-Family Estimates From the American Community Survey Estimates 2006-2008, there are a total of 21,139 singlefamily housing units located in Galveston with its majority in the Urban Core as shown in Table 12.

Table 12: Total Single-Family Units 21,139

Single-Family Units (ACS 2006-2008 Estimates - Pre Ike) Urban Core

15,457

West End

5,682

After the application of the HRRC destruction rate of 2.74% and single-family occupancy rates as shown in Table 13, the number of total occupied single-family housing units can be estimated.

Table 13: Data used to estimate post Ike occupied single-family units Destruction Rates (HRRC data) Occupancy Rates (HRRC data 01/2010)

2.74%

Urban Core

70.6%

West End

58.0%

In the Urban Core there are 10,607 units, as opposed to 3,204 units in the West End. Table 14: Persons per Household for Single-Family Housing Units Household Size (HRRC data 01/2010) Urban Core

2.37

West End

2.16

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The application of persons per household shown in Table 14 for these units then yields a total of 32,059 people living in single-family housing units in Galveston.

Multi-Family Estimates The multi-family population estimated for Scenario 1 is based upon the TAMU multi-family survey conducted between January and April 2010. Total housing units and occupancy rates are computed as outlined in the sections describing Multi-Family Housing Units and Occupancy Rates. The rate for persons per household determined by the American Community Survey Estimates for 2006-2008 are applied as described in the section on Household Size. These values resulted in an estimate of 12,835 people living in multi-family housing units in Galveston.

Due to the fact that institutional housing units show very different occupancy rates and person per household data, these units have not been included into the above described process. Institutional housing units include dorms, apartments and rental houses by TAMU Galveston, UTMB Galveston and Galveston College. These properties mostly show occupancy rates of 100% and larger household sizes than other properties of a similar size. Consequently, if those units would be included in the computation of occupancy rates, these rates would be much higher and therefore not reflecting the actual occupancy rates of this particular housing category. Additionally, accurate data on persons living in institutional housing units was readily available from the appropriate institutions, summarized in Table 15.

Table 15: Institutional Population of Galveston Institutional Population

Units

Persons

425

850

Pre-Ike

338

418

Post-Ike

240

296

9

26

TAMUG Housing Post-Ike UTMB Housing (dorms, apts, rental houses)

College Galveston Housing Whitecaps Dorms

The institutional population of 1,172 people is added to the estimate above, resulting in an estimate of 14,007 people living in multi-family housing units in Galveston.

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Total Population Estimates Total population estimates for Galveston are computed as the sum of people living in single-family housing units of 32,059 and people living in multi-family housing units of 14,007 resulting in an overall population estimate of 46,066 people living in the City of Galveston.

Scenario 2 (Medium) Single-Family Estimates Single-family population estimates for Scenario 2 are computed in the same way as in Scenario 1.

They are based upon the American Community Survey Estimates and HRRC Damage Assessment Data for occupancy rates and persons per household. Consequently, the estimate for people living in single-family housing units in Galveston is 32,059.

Multi-Family Estimates The American Community Survey Estimates 2006-2008 reveal there are a total of 21,139 multifamily housing units located in Galveston. Multi-family units are considered to be properties with 3 units or more as shown by Table 16.

Table 16: Total Multi-Family Units Housing Units Pre Ike (2006-2008 ACS Estimate) 3 or 4 units

1,829

5 to 9 units

2,120

10 to 19 units

4,349

20 or more units

4,002

After application of the HRRC destruction rates of 2.74% and multi-family occupancy rates as computed in the section on Occupancy Rates, the number of total occupied multi-family housing units can be estimated to 7,768.

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The application of persons per household for renter-occupied housing units, as shown in the section on Household Size for these units, yields a total of 16,314 people living in multi-family housing units in Galveston.

Total Population Estimates Total population estimates for Galveston are computed as the sum of people living in single-family housing units of 32,059 and people living in multi-family housing units of 16,314 resulting in an overall population estimate of 48,373 people living in the City of Galveston.

Scenario 3 (High) Single-Family Estimates A high estimate on total housing units in Galveston is given by the U. S. Postal Service. The most recent data from the 4Q 2009 was used as shown in Table 17. This data is only available for total housing units. To get an estimate on single-family housing units, it is thus necessary to subtract the number of multi-family housing units. In this scenario, multi-family housing units are estimated based upon the American Community Survey Estimates resulting in 10,797 units. Due to the fact that the most reliable occupancy rates and household size data is available for the Urban Core and West End, we consider the spatial distribution of single- and multi-family housing units in the Urban Core and West End. The ratios are based upon TAMU Survey data as described in the section on Spatial Distribution of Multi-Family Units and the HRRC Damage Assessment as described in Total Housing Unit Totals, Summary.

Table 17: Single-Family Housing Units according to USPS data and their Spatial Distribution Total Housing Units

Urban Core

West End

Total

Total Housing Units Galveston (USPS data 4Q 2009)

27,858

4,041

31,899

Multi-Family Units

10,797

1,165

11,962

Single-Family Units

17,061

2,876

19,937

The application of occupancy rates and persons per household, as shown in Tables 13 and 14, yields an estimate of 32,131 people living in single-family housing units in Galveston.

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Multi-Family Estimates Multi-family population estimates for Scenario 3 are computed in the same way as in Scenario 2.

The numbers are based upon the American Community Survey Estimates and HRRC Damage Rates.

Occupancy rates are calculated from the TAMU Multi-Family Survey, and American Community Survey Estimates for household size are used. Consequently, the estimate for people living in multi-family housing units in Galveston is 16,314.

Total Population Estimates Total population estimates for Galveston are computed as the sum of people living in single-family housing units of 32,131 and people living in multi-family housing units of 16,314 resulting in an overall population estimate of 48,445 people living in the City of Galveston.

Chosen Population Estimate Scenario Previous sections of this report explained how different scenarios for population estimates for the City of Galveston have been computed, which data has been used, and how differences can be explained. Low, medium and high population estimates have been computed and it should be noted that all three estimates result in a very small range of estimates for the number of people living in Galveston as shown in Table 18. Despite the use of different data sources to compute population estimates for the three scenarios, the final estimates are between 46,000 and 49,000. However, all three estimates fall below 50,000 in population.

Table 18: Comparison of Population Estimate Scenarios Population Estimate

Single Family Estimate

Multi-Family Estimate

Total Population Estimate

Scenario 1

32,059

14,007

46,066

Scenario 2

32,059

16,314

48,373

Scenario 3

32,131

16,314

48,445

Due to available data, we consider the medium scenario, Scenario 2 as the most accurate population estimate for the City of Galveston. This is due to the fact that the TAMU Multi-Family Survey, according to its nature as a primary data source, probably undercounted the total number of multifamily properties within the city, resulting in Scenario 1â&#x20AC;&#x2122;s estimate being too low. Scenario 3 on the

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other hand, is based upon USPS data on housing units. Analysis has shown that this data source probably over counted housing units on the Island, resulting in a population estimate that is too high.

For these reasons, Scenario 2 is considered to provide the most reliable population estimate for the City of Galveston. Figure 5 below shows a summary of the calculations used to compute a population estimate of 48,373 people currently living in Galveston.

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Figure 4: Population Estimates for the City of Galveston

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Demographic Composition of Galveston Population Galveston Independent School District Enrollment Totals Analysis Utilization of PEIMS Data The Galveston Independent School District (GISD) played a large role in helping estimate the demographic characteristics of the Island. Specifically utilized was GISD’s Public Education Information Management System (PEIMS), a database used by the Texas Education Agency to collect and maintain records of enrolled students in Texas public school districts regarding demographics and school performance of those enrolled. GISD PEIMS coordinator, Patti Youngblood, was very helpful in allowing access to GISD school enrollment data, without revealing the students’ actual identities. The purpose in obtaining the PEIMS school enrollment data was to better understand the distribution and composition of Galveston’s population, and how it may have been affected by Ike.

It is important to note that families with children are not necessarily a representative sample of all Island households. The pre-storm population of Galveston Island had a disproportionately high number of families without children (retirement households) compared to the rest of the state, for example. School enrollment data, however, is known to be a good proxy for larger population changes at the municipal level5. It is particularly useful for helping to establish distribution patterns of the population, paying attention to overall changes rather than specific rates.

Enrollment numbers from the 2007-2008 school year (one year prior to Ike) were compared with numbers immediately following Ike, the 2008-2009 school year, as well as one year later with the 2009-2010 numbers. Numbers reported as PEIMS data to the Texas Education Agency are due in October of each school year, and therefore reflect enrollment immediately following the beginning of the new school year. Also obtained were the most current, February 2010, school enrollment information including student locations, race/ethnicity, and economic status. Economic status was based on whether or not the student was able to receive free or reduced school lunches. The criteria for determining whether a student is economically disadvantage is based on parent/guardian income levels and family size.

5

Plyer, A., J. Bonaguro, and K. Hodges. 2010. Using administrative data to estimate population displacement and resettlement following a catastrophic U.S. disaster. Population and Environment 31: 150-175.

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Total enrollment prior to Ike and after were determined by aggregated reports that the GISD PEIMS coordinator had developed and is required to send annually to the state agency. Trends for race/ethnicity and economic status were calculated for enrolled students in the district, and then shown at the neighborhood level based on student locations on a map as seen later in i the report.

The result of this data analysis is a comparis comparison of demographic trends between the various school years within the Islandâ&#x20AC;&#x2122;s neighborhoods which can therefore be used to help estimated trends for overall population change.

Total Enrollment From Figure 6 below, it is evident that Ike caused a drop in total students enrolled immediately following the storm as families were displaced, but it is encouraging to note that the number continues to rise daily; however, total GISD enrollment is currently not to what it was prior to the storm.

Figure 5: GISD Total School Enrollment 10,000

7,500

7,903 6,358

5,000

5,591

2,500

0 2007-08

2008-09

2009-10 2009

Table 19 below shows an overall decrease of 19.55% in total enrollment of the current 2009-2010 2009 school year compared to the school year prior to Ike, 2007 2007-2008.

2008. There was nearly a 30% decrease

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in school enrollment from the school year prior to Ike and immediately following the storm.

However, school enrollment in the GISD saw a 14% increase from Ike until one year post-Ike. post

Table 19:: Percent Change of Enrollment in the GISD School Year

Percent Change

2007-08 to 2008-09

29.29% decrease

2008-09 to 2009-10

13.72% increase

Overall Change: 2007 2007-08 to 2009-10

19.55% decrease

Overall Demographic Changes in School Enrollment Through use of the PEIMS data, overall demographic characteristics were analyzed using both prepre Ike and post-Ike Ike school years to compare change. Figure 7 and Table 20 reveal that the African American population has seen a 35% decrease in its numbers regar regarding ding students enrolled in the GISD. The Hispanic population has decreased about 15% from its pre pre-storm storm numbers, while the White population has experienced a 17% decrease. A look at these changes in specific neighborhoods on the Island will be discussed iin greater detail later.

Figure 6:: Demographic Make Make-up of GISD students

Pre-Ike

Current

3% 24%

5% 30%

43%

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25% 24%

46%

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Table 20: Percent Change of Demographics within the GISD African American

Hispanic/ Latino

White

Other

Pre-Ike

2,393

3,422

1,894

194

Current Change

1,557

2,893

1,568

340

-35%

-15%

-17%

75%

According to the numbers developed by the U.S. Censusâ&#x20AC;&#x2122; 2006-2008 American Community Survey, approximately 13% of Galvestonâ&#x20AC;&#x2122;s population is of school age, 5-18 years old6. In comparing the pre-Ike GISD school enrollment demographic make-up to the numbers reported by the ACS, it is evident that many of the racial percentages are not similar and nothing conclusive can be derived from comparing the two. A few issues arising in comparing the two sets of numbers include the fact that the GISD school enrollment data and the ACS utilize different categories in reporting race and ethnicity, as well as the issue that the pre-Ike numbers for school enrollment reflect the 2007-2008 school year, whereas the ACS reports numbers from a 3-year period between 2006 and 2008. As shown in Table 21, the White population constitutes approximately 69% of the population.

Taking a look back at Figure 7 above, for the school enrolled population Whites comprise only 24% of the total. This large discrepancy reflects the somewhat unusual demographic composition of the Island. The non-Hispanic white population is largely older, without school-age children. Further, those non-Hispanic whites that do have children are more likely to enroll them in private schools than non-white families. The majority of students within the GISD, 43%, are Hispanic, whereas of the total population only 28% associate themselves as being of Hispanic ethnicity. Explanations for this could include Hispanic households have more children than non-Hispanic households, or that the Hispanic population in Galveston could be of younger age.

6

http://factfinder.census.gov/servlet/ADPTable?_bm=y&-geo_id=16000US4828068&qr_name=ACS_2008_3YR_G00_DP3YR5&-context=adp&-ds_name=&-tree_id=3308&-_lang=en&-redoLog=false&format=

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Table 21: ACS 2006-2008 Demographic Characteristics for Galveston, Texas Race/Ethnicity

Number of Population

Percent of Population

White

36,518

69.1%

African American

11,184

21.2%

American Indian

941

1.8%

Asian

1,667

3.2%

Other

3,594

6.8%

Total

52,821

100%

Hispanic (of any race)

14,810

28%

Distribution of Enrolled Students Using the home addresses of enrolled students, each GISD students’ location was geocoded and mapped to determine their location7. The location of the students can be seen in Figure 8. We would expect this information to reflect the population density distribution of the Island and school district. When we initially geocoded this information, we expected it to be an initial necessary step to determine the racial/ethnic distribution of the population. However, when we saw the spatial distribution of the student population, we were surprised to see a large number of students— 336—located on the mainland. While only 5 percent of the student population, these households may represent households who have been displaced by the hurricane, and who intend to move back to the Island when possible.

We understand from talking to some City employees that GISD has a policy that allows non-Island families to enroll their children in GISD even if they do not live in the neighborhood. It is possible that some if not many of these mainland students are transfers into GISD, who may or may not be displaced from the Island by damage received during Hurricane Ike. The demographic composition of student population by location (see Table 22), however, indicates that a disproportionate number of the mainland GISD students are African-American (42% compared to 24% in the entire GISD population). Given that more than 500 public housing units were destroyed during and after the hurricane, and nearly all of these households were displaced off the Island, we believe it is likely that many of the students located on the mainland are the children of public housing residents who have temporarily or permanently relocated to the mainland. We cannot know their intentions to

7

About 1% of enrollment had incomplete or unidentifiable addresses that prevented us from geocoding them.

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return, but we do know that they have limited options to do so, given the current lack of availability of public housing units.

Figure 8. Location of GISD students in Galveston County.

Source: GISD data, geocoded and mapped by TAMU students.

Table 22: Demographic Composition of GISD Students, by Location.

Geography Bolivar Island Mainland Unknown Total

Hispanic/Latino 65 (51%) 2,612 (46%) 117 (35%) 9

White 49 (39%) 1,397 (25%) 64 (19% 5

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AfricanAmerican 1 (<1%) 1,406 (25%) 142 (42%) 6

Other 12 (9%) 270 (5%) 13 (4%) 1

Total 127 5,685 336 21 6169

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Racial and Ethnic Composition In the aftermath of Hurricane Ike, it is evident that many families were displaced from the Island.

However, whether or not the movements of these families have altered the demographic composition of Galveston’s neighborhoods is unknown. The following section addresses this issue by evaluating the current demographic and ethnic composition of neighborhoods as compared to the demographic estimates of the 2000 U.S. Census.

This analysis was completed by mapping the most dominate populations, in terms of race and ethnicity, which reside on the Island. Two different datasets were utilized to complete this analysis; current estimates were made using 2009-2010 school enrollment data provided by the PEIMS Coordinator from the Galveston Independent School District, and past estimates were reflected by using 2000 Census data. The methodologies and results for the analysis are discussed below.

Current Data Estimates The 2009-2010 school enrollment maps calculated the percentage of each population group based on pre-defined neighborhood areas. The PEIMS student data identified the ethnicity of students, and indicated whether or not the student was economically disadvantaged. These addresses were then geocoded using Geographic Information Systems (GIS) mapping software to a streets layer provided by the Galveston County Appraisal District. This means that an Excel spreadsheet was translated into address points on a map, and placed along streets on Galveston Island. Next, a layer defining neighborhood boundaries was overlayed onto the map, provided by UTMB. This allowed for a spatial join to be performed, in which the number of address points was summed within each defined neighborhood zone. The percent of each ethnicity residing within each neighborhood zone was then calculated by the following formula: % of Ethnic Group by neighborhood zone= [β] / [µ] * 100 β =summation of address points by ethnic group per neighborhood zone µ =summation of total number of address points within each neighborhood zone

The steps described above were performed for Non-Hispanic Whites, African Americans, and Hispanics which are considered the most dominate populations represented on Galveston Island.

However, other populations reside on the Island and should not be discounted. Based on the desire of the city, additional populations may also be evaluated using the prescribed above methodology.

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Past Data Estimates Current demographics estimates were compared against Census 2000 data to determine if neighborhoods were significantly altered after the storm, as seen in Figures 9, 10, and 11. Using data acquired from the U.S. Censusâ&#x20AC;&#x2122; American FactFinder8, the three most prominent populations on the Island were evaluated and, as stated above, include Caucasian, African American, and Hispanic population counts. To provide a detailed evaluation of these populations by neighborhood, all datasets were evaluated at the block group level which reflects the smallest geographic unit available with data the Census has to offer. For comparison across block groups, and ultimately neighborhoods, a formula was used to convert the number of individuals within a block group to a ratio, as provided below: % of Prominent Population 1 = (Population 1/ Total Population) * 100 Results The compared analysis between the Census 2000 block group maps and the 2009-2010 school enrollment maps illustrate no major spatial distribution changes among the population for Galveston Island as depicted in Figures 9-11. However, it is important to note that the two maps as presented cannot and should not be directly compared, but may be used for observational purposes. These maps act as indicators of potential trends that should be monitored and further investigated by the City as data becomes more readily available. Major differences between the two datasets include the following: 1) Dataset Boundaries: Block group boundaries from the 2000 Census are not identical to the neighborhood boundaries created from the 2009-2010 School Enrollment Data. However, due to the scale in which data is provided from the U.S. Census, the data may be used for the general interpretation of spatial trends throughout the Island where, for the most part, neighborhood trends tend to reflect the information provided by the 2009-2010 data.

2) Zone Composition: 2000 Census data is reflective of the entire Galveston population while the 2009-2010 School Enrollment Data only provides information pertinent to the youth of Galveston who are enrolled in a public school. For direct comparison within maps generated using current and past data, ratios were generated to reflect zone composition.

However, it is important to note when viewing percent change, this number reflects a ratio change within an individual block group or neighborhood and not a consistent number of

8

Website may be viewed at http://factfinder.census.gov/home/saff/main.html?_lang=en

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individuals. For example, Pelican Island may have a 50% change in percent of population between 2000 and 2010 for Caucasians, but the Island may only have a total 40 people that live on it, while other neighborhood or block group zones may have four to five hundred people that live within it.

3) Dataset Intervals: Moreover, the interval scale contained within the legend of each map is different due to the extent of the data. This means that the color coded percent values should be carefully examined to gain a better understanding of how intense the change between neighborhood or block group zones has occurred. Differences in observed changes can still be deduced from the maps by realizing that the colors do not directly reflect the amount of change, but the values assigned to them within each map.

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2000 Census by Block Groups

2009-2010 School Enrollment by Neighborhood

Figure 9: Percent White by Neighborhood

Population & Demographic Estimates

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2000 Census by Block Groups

2009-2010 School Enrollment by Neighborhood

Figure 10: Percent African American by Neighborhood

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2000 Census by Block Groups

2009-2010 School Enrollment by Neighborhood

Figure 11: Percent Hispanic by Neighborhood

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When viewing the maps, it is important to remember that it is not the actual percentage that is important hereâ&#x20AC;&#x201D;these proportions are based on different sets of data and are not directly comparable. In other words, the maps of minority populations based on 2010 GISD data are darker than the 2000 maps based on Census data, because more minority families have children, while non-Hispanic white families are less likely to have children, and are thus underrepresented in the GISD data. Rather, the comparison we want to make is whether the areas that are shaded are changing from 2000 to 2010. Changes in the shaded areas would indicate that the population is shifting somewhat, possibly as a result of damage received during Hurricane Ike, or the rebuilding that has taken place since the storm.

In these maps, however, we see relatively little change in the distribution of the population from 2000 to 2010. Areas that have higher concentrations of Hispanic and non-Hispanic white households are the same areas that had these concentrations in 2000. For the African-American population, we can see in Figure 10, that there has perhaps been a decrease in the proportion of African-Americans in the census block groups that are just to the north of Broadway/Ave J. This probably reflects the displacement of public housing residents and an overall loss of population in these areas, rather than a shift in the distribution of the population.

Implications and Opportunities It was estimated that Galveston currently has approximately 48,000 individuals living on the Island. This number reveals an 8% loss in population since Hurricane Ike in September 2008, according to the Census. No crucial change in the spatial distribution of the population was discovered. As the City is aware, a population in excess of 50,000 individuals serves as a bench mark in funding and planning implications as determined by the Federal government. The 2000 Census identifies such places as Urbanized Areas (UA), and defines UAs as areas â&#x20AC;&#x153;consisting of a central place(s) and adjacent urban fringe that together have a minimum residential population of at least 50,000 people and generally an overall population density of at least 1,000 people per square mile of land area.â&#x20AC;?9 The Census Bureau uses published criteria to determine the qualification and boundaries of UAs. In falling below the 50,000 benchmark, Galveston has the potential to lose this U.S. Census Urbanized Area designation. This will not only affect funding sources from Federal agencies, but there will be a loss of detailed information regarding

9

http://www.census.gov/geo/lv4help/cengeoglos.html Texas A&M University Applied Planning Studio

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2010

characteristics of the city which therefore will place a greater burden on the City in understanding the make-up of the Island.

However, there are also many positives that arise from this population estimate, including the opportunity to reach a population of 50,000 within the very near future as the estimate fall just short of this threshold and those displaced returning regularly to the Island. This is evident of the fact that more than 300 students enrolled in GISD schools reside off of the Island, revealing potential households that did not cut ties with the Island after being displaced and may have plans to return. Analysis also revealed a large concentration of African-American students displaced, residing on the mainland. This disproportionate loss is likely due to the loss in public housing on the Island, therefore serving as an incentive to quickly act on rebuilding efforts of the Islandâ&#x20AC;&#x2122;s public housing in order to bring those displaced back home.

Texas A&M University Applied Planning Studio

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