Growth Plan: Monitoring and Reporting Indicators -...

Growth Plan: Monitoring and Reporting Indicators

March 9, 2017

March 9, 2017 2 | p a g e Filename: 2017 Growth Plan Monitoring & Reporting Indicators.docx

growth plan: monitoring and reporting indicators

Table of Contents

Introduction ................................................................................................................................................................ 5

Indicator 1 - Total Regional Population ................................................................................................................. 6

Table 1.1: Total Regional Population ........................................................................................................................ 6

Figure 1.1: Total Regional Population ....................................................................................................................... 6

Table 1.2: Total Population by Member Municipalities ............................................................................................ 7

Indicator 2 – Population Growth Rate ................................................................................................................... 8

Table 2.1: Population Growth by PGA ....................................................................................................................... 8

Table 2.2: Population Growth by CCRA ..................................................................................................................... 8

Figure 2.1: Map of Population Growth ..................................................................................................................... 9

Table 2.3: Population Growth by Municipality ........................................................................................................ 10

Indicator 3 – Distribution of Population Growth ............................................................................................... 11

Figure 3.1: Distribution of Population Growth by Policy Areas (2006 to 2011) ...................................................... 11

Figure 3.2: Distribution of Population Growth by Policy Areas (2011 to 2016) ...................................................... 11

Figure 3.3: Distribution of Population Growth by Individual Policy Area (2011 to 2016) ....................................... 12

Indicator 4 - Dwelling Unit Density ....................................................................................................................... 13

Table 4.1: Dwelling Unit Density by PGA ................................................................................................................. 13

Table 4.2: Dwelling Unit Density by CCRA ............................................................................................................... 13

Figure 4.1: Map of Dwelling Unit Density by Dissemination Area .......................................................................... 14

Table 4.3: Dwelling Unit Density by Urban Municipalities in PGAs ......................................................................... 15

Indicator 5 – Fuel Sales and Vehicle GHG Emissions ......................................................................................... 16

Figure 5.1: Total Fuel Sales and Fuel Sales per Capita by Year ................................................................................ 16

Figure 5.2: Total GHG Emissions and GHG Emissions per Capita by Year ............................................................... 17



Indicator 6 - Ambient Air Quality .......................................................................................................................... 18

Figure 6.1: AQHI Index Legend ................................................................................................................................ 18

Figure 6.2: Tomahawk Station – AQHI Ratings by Year ........................................................................................... 18

Figure 6.3: Genesee Station – AQHI Ratings by Year .............................................................................................. 19

Figure 6.4: Edmonton Station – AQHI Ratings by Year ............................................................................................ 19

Figure 6.5: Fort Saskatchewan Station – AQHI Ratings by Year .............................................................................. 19

Figure 6.6: Bruderheim Station – AQHI Ratings by Year ......................................................................................... 20

Figure 6.7: Lamont Station – AQHI Ratings by Year ................................................................................................ 20

Indicator 7 – Distribution of Market and Non-Market Housing ...................................................................... 21

Figure 7.1: Market vs Non-Market Housing Stock by Sub-Region (2011)……………………….....……………...21

Indicator 8 – Funding for Non-Market Housing .................................................................................................. 22

Figure 8.1: Provincial Funding for Non-Market Housing by Fiscal Year .................................................................. 22

Indicator 9 – Housing Mix ....................................................................................................................................... 23

Figure 9.1: Regional Housing Distribution by Structure Type (2011) ...................................................................... 23

Table 9.1: Housing by Structure Type and Municipality (2011) .............................................................................. 24

Indicator 10 – Household Spending ...................................................................................................................... 25

Indicator 11 – Commuting Duration ..................................................................................................................... 26

Table 11.1: Commuting Duration by Municipality (2011) ....................................................................................... 27

Indicator 12 – Commuting Mode Split ................................................................................................................. 28

Figure 12.1: Community Mode Split in the Capital Region (2011) .......................................................................... 28

Table 12.1: Commuting Mode Split by Municipality (2011) .................................................................................... 29



Indicator 13 – Employment Diversity ................................................................................................................... 30

Figure 13.1: Employment Diversity in the Capital Region by NAICS Job Class (2011) ............................................. 30

Indicator 14 – Employment Rate ........................................................................................................................... 31

Indicator 15 – Distribution of Regional Employment ........................................................................................ 32

Table 15.1: Regional Employment by Municipality (2011) ..................................................................................... 32

Indicator 16 – Planned Densities of Newly Approved Residential Areas ...................................................... 34

Table 16.1: Planned Densities of Approved New Residential Areas* Since March 2010 ........................................ 35



Introduction

With the Capital Region Growth Plan (CRGP) in place, the next step is focusing on the implementation of the recommendations from the plan. An important component of implementation is monitoring the success of the outcomes of the CRGP based on specific measurable policies in the plan.

Why Monitor?

Monitoring is valuable when done effectively and consistently. Key benefits of monitoring and reporting include:

• Tracking progress in achieving goals: Monitoring creates a means and process to track changes occurring in the region over time that are relevant to the Capital Region and the CRGP.

• Assessing the effectiveness of policies: Monitoring helps evaluate how well the actions are assisting in achieving the overarching vision of the CRGP, along with its strategic principles and policies. This helps to assess the effectiveness of the policies and provides supporting information to guide future decisions.

• Encouraging accountability and transparency: Ongoing reporting establishes a platform for consistently communicating outcomes of the implementation of the CRGP. The benefits of this are that it a) promotes transparency in the CRB’s activities; b) celebrates accomplishments and focuses attention on the areas that need work; and c) demonstrates accountability to implementing the CRGP.

The purpose of a monitoring and reporting program is to enable the CRB and its provincial partners to assess whether the CRGP and its related implementation actions are having the desired effect. The objectives are two-fold:

1. To assess whether or not the sum of actions being taken in the Region are in line with the vision, principles and policies of the CRGP, and

2. To provide feedback to the Board on the regional sustainability within the Capital Region.

Overall, monitoring helps to gauge the effectiveness of the policies in the CRGP and can be used to identify areas where changes in the CRGP may be required in subsequent updates.

The following 16 indicators in this report are based on the feedback and recommendations of the regional stakeholders. This list of indicators was originally reviewed and presented to the Governance Committee in March 2011.



Indicator 1 - Total Regional Population

Description

This is a measure of the aggregated population in the region. Why is this Indicator Important?

Regional population directly influences the region’s economy and the pace and extent to which the communities in the region are developing. This indicator provides context for the principles and policies in the Growth Plan.

Table 1.1: Total Regional Population

Census Year 2001 Federal

Census 2006 Federal

Census 2011 Federal

Census 2016 Federal

Census

Population 939,597 1,035,878 1,161,551 1,321,498

Figure 1.1: Total Regional Population

939,5971,035,878

1,161,551

1,321,498

2001 2006 2011 2016

Popu

latio

n

Census Year

+10.2% +12.1% +13.8%


growth plan: monitoring and reporting indicators Table 1.2: Total Population by Member Municipalities

Municipality 2001 Federal

Census 2006 Federal

Census 2011 Federal

Census 2016 Federal

Census Beaumont 7,006 8,961 13,284 17,396 Bon Accord 1,532 1,534 1,488 1,529 Bruderheim 1,202 1,215 1,155 1,308 Calmar 1,902 1,959 1,970 2,228 Devon 4,969 6,261 6,510 6,578 Edmonton 666,104 730,372 812,201 932,546 Fort Saskatchewan 13,121 14,957 19,051 24,149 Gibbons 2,654 2,642 3,030 3,159 Lamont 1,692 1,669 1,753 1,774 Lamont County 4,167 3,905 3,872 3,899 Leduc 15,032 16,967 24,279 29,993 Leduc County 12,528 13,135 13,524 13,780 Legal 1,058 1,192 1,225 1,345 Morinville 6,540 6,775 8,569 9,848 Parkland County 27,252 29,220 30,568 32,097 Redwater 2,172 2,202 1,915 2,053 Spruce Grove 15,983 19,541 26,171 34,066 St. Albert 53,081 57,764 61,466 65,589 Stony Plain 9,589 12,363 15,051 17,189 Strathcona County 71,986 82,511 92,490 98,044 Sturgeon County 18,067 18,566 19,578 20,495 Thorsby 799 945 951 985 Wabamun 601 601 661 682 Warburg 560 621 789 766

Total 939,597 1,035,878 1,161,551 1,321,498

Data Source Federal Census

Method of Measurement Collect population data for each community and calculate the sum total.



Indicator 2 – Population Growth Rate

Description

This is a measure of the rate of population growth experienced in the region as a whole, and the proportion of the growth that is occurring in the Priority Growth Areas (PGAs), Cluster Country Residential Areas (CCRAs), and in other areas. It measures the growth rate over the five-year period between Census years. Why is this Indicator Important?

Tracking growth rates helps interpret the pace of growth in the region. The growth rate informs the timeframe requirements for implementing new initiatives, such as new housing and transit.

Table 2.1: Population Growth by PGA

PGA 2006 2011

2001-2011 Population Change % 2016

2011-2016 Population Change %

A 32,534 41,727 +28.26% 52,636 +26.14% B 836,507 922,052 +10.23% 1,036,093 +12.37%

Cw 451 2,965 +557.43% 22,291 +651.80% Ce 12,308 22,653 +84.05% 27,812 +22.77% E 17,167 24,452 +42.44% 30,089 +23.05% F 3,590 4,433 +23.48% 3,368 -24.02% G 15,043 19,151 +27.31% 24,187 +26.30%

Total 917,600 1,037,322 +13.06% 1,196,476 +15.34% Table 2.2: Population Growth by CCRA

CCRA 2006 2011



S 2,734 3,695 +35.15% 2,979 -26.19% O 2,494 3,454 +38.49% 3,653 +5.76% I 3,028 3,643 +20.31% 3,696 +1.45% Q 1,300 1,638 +26.00% 1,809 +10.44%

Total 9,556 12,430 +30.08% 12,137 -2.36%


growth plan: monitoring and reporting indicators Figure 2.1: Map of Population Growth


growth plan: monitoring and reporting indicators Table 2.3: Population Growth by Municipality

Municipality 2006 2011



Beaumont 8,961 13,284 +48.24% 17,396 +30.95% Bon Accord 1,534 1,488 -3.00% 1,529 +2.76% Bruderheim 1,215 1,155 -4.94% 1,308 +13.25% Calmar 1,959 1,970 +0.56% 2,228 +13.10% Devon 6,261 6,510 +3.98% 6,578 +1.04% Edmonton 730,372 812,201 +11.20% 932,546 +14.82% Fort Saskatchewan 14,957 19,051 +27.37% 24,149 +26.76% Gibbons 2,642 3,030 +14.69% 3,159 +4.26% Lamont 1,669 1,753 +5.03% 1,774 +1.20% Lamont County 3,905 3,872 -0.85% 3,899 +0.70% Leduc 16,967 24,279 +43.10% 29,993 +23.53% Leduc County 13,135 13,524 +2.96% 13,780 +1.89% Legal 1,192 1,225 +2.77% 1,345 +9.80% Morinville 6,775 8,569 +26.48% 9,848 +14.93% Parkland County 29,220 30,568 +4.61% 32,097 +5.00% Redwater 2,202 1,915 -13.03% 2,053 +7.21% Spruce Grove 19,541 26,171 +33.93% 34,066 +30.17% St. Albert 57,764 61,466 +6.41% 65,589 +6.71% Stony Plain 12,363 15,051 +21.74% 17,189 +14.21% Strathcona County 82,511 92,490 +12.09% 98,044 +6.00% Sturgeon County 18,566 19,578 +5.45% 20,495 +4.68% Thorsby 945 951 +0.63% 985 +3.58% Wabamun 601 661 +9.98% 682 +3.18% Warburg 621 789 +27.05% 766 -2.92%

Total 1,035,878 1,161,551 +12.12% 1,321,498 +13.77%

Data Source Federal Census

Method of Measurement

Collect population data for each municipality and dissemination blocks from federal

census data. Calculate the difference in population between years and map based on dissemination blocks. Determine which dissemination blocks are in which PGAs and

CCRAs and aggregate the total population changes for each.



Indicator 3 – Distribution of Population Growth

Description

This is a measure of the distribution of population growth experienced in the Priority Growth Areas (PGAs), Cluster Country Residential Areas (CCRAs), and in remaining areas. It measures the growth rate over the five-year period between Census years. Why is this Indicator Important?

This indicator directly measures where growth is occurring in the region in relation to the defined growth areas. A high share of growth located within the growth areas indicates growth in occurring in a compact manner. A compact urban form supports more efficient use of infrastructure, transit and the protection of the environment and natural resources.

Figure 3.1: Distribution of Population Growth by Policy Areas (2006 to 2011)

Figure 3.2: Distribution of Population Growth by Policy Areas (2011 to 2016)

95.80%

2.30% 1.90%

PGAs CCRAs Remaining Area

90.54%

0.92% 8.54%

PGAs CCRAs Remaining Area


growth plan: monitoring and reporting indicators Figure 3.3: Distribution of Population Growth by Individual Policy Area (2011 to 2016)

Data Source Statistics Canada


Using the data from the previous indicator (Indicator 2 - Population Growth Rate),

percentages of the total population changes are calculated for each PGA, CCRA and Remaining Areas.

3.98%

78.40%

1.69% 2.10% 2.28% 0.25% 1.83% 0.23% 0.28% 0.28% 0.14%

8.54%



Indicator 4 - Dwelling Unit Density

Description

This indicator measures the number of dwellings per net residential hectare within municipalities, and within PGAs.

Why is this Indicator Important?

Knowing the dwelling unit density will show how concentrated growth is. Comparing these results over time will demonstrate how density is changing in each PGA and the related municipalities relative to other areas.

Table 4.1: Dwelling Unit Density by PGA

PGA Occupied

Dwelling Count (2016) Net Residential

Area (ha) 2016

Du/Nrha* 2011

Du/Nrha*

Density Target

(Du/NRha*) B 399,521 15,878 25.16 21.98 30-45+ E 11,357 500 22.71 18.93 25-30

Cw 8,114 372 21.81 10.25 30-40 A 19,686 1,100 17.90 14.96 25-30 G 9,276 537 17.27 14.86 25-30 Ce 9,113 639 14.26 9.78 25-35 F 1,215 98 12.40 15.21 30-40

Table 4.2: Dwelling Unit Density by CCRA

CCRA Occupied

Dwelling Count (2016) Gross Area (ha) 2016

Du/Gha** 2011

Du/Gha**

Density Target

(Du/Gha**) S 950 1,410 0.67 0.96 2 O 1,180 4,539 0.26 0.25 2 I 1,434 5,759 0.25 0.31 2 Q 572 5,283 0.11 0.16 2

Sorted in descending order

*Du/Nrha = Dwelling Units per Net Residential Hectare **Du/Gha = Dwelling Units per Gross Hectare



Figure 4.1: Map of Dwelling Unit Density by Dissemination Area (Based on Table 4.1)



Table 4.3: Dwelling Unit Density by Urban Municipalities in PGAs

Municipality

Private Occupied Dwellings (2016)

Net Residential Area (ha)*

Du/Nrha**

City of Edmonton 360,828 13,274 27.18 City of Leduc 11,319 500 22.64

City of Spruce Grove 12,552 569 22.06

Strathcona County: Sherwood Park 26,132 1,371 19.06 City of St.Albert 23,954 1,308 18.31

Town of Stony Plain 6,641 373 17.80 City of Fort Saskatchewan 9,261 526 17.61

Town of Beaumont 5,633 339 16.62

*Last calculated March 2016 ** Du/Nrha = Dwelling Units per Net Residential Hectare ( Sorted in descending order)

Data Source Statistics Canada, Member Municipalities


a) PGAs (Table 4.1) – The number of ‘Private dwellings occupied by usual residents’ is collected for each dissemination area in the region from federal census data. Dissemination areas are then assigned into PGAs based on if the majority of each dissemination area is within a PGA. The sum total of dwellings between all dissemination areas in each PGA is then calculated. Residential zoning/districting for the region is then ‘clipped’ to each PGA to determine the amount of designated residential area in each PGA. Sub-divided land parcels of 4ac/1.62ha or less are then overlaid with the residential area of each PGA to determine the net residential area in hectares for each PGA. The dwelling counts are then divided by this net residential area to achieve dwelling units per net residential area in hectares for each PGA (Du/Nrha).

b) CCRAs (Table 4.2) - The number of ‘Private dwellings occupied by usual residents’ is collected for each dissemination area in the region from federal census data. Dissemination areas are then assigned into CCRAs based on if the majority of each dissemination area is within a CCRA. The sum total of dwellings between all dissemination areas in each CCRA is then calculated. The total dwelling count is then divided by the total gross area of each CCRA to achieve the dwelling units per gross area in hectares (Du/Gha).

c) Municipalities (Table 4.3) – The number of ‘Private dwellings occupied by

usual residents’ is collected for each dissemination area in the region from federal census data. Residential zoning/districting for each municipality is then overlaid with sub-divided land parcels of 4 ac/1.62ha or less to determine the net residential area of each municipality. The number of dwellings are then divided by the net residential area to achieve the dwelling units per net residential area in hectares (Du/Nrha)



Indicator 5 – Fuel Sales and Vehicle GHG Emissions

Description

This indicator measures the total fuel sales (in litres) for vehicles and the GHG emissions that are created and emitted into the atmosphere when the fuel is used. The fuel sales are based on available data for a subset of communities in the Capital Region.


Vehicles use a significant portion of the total energy use and GHG emissions. Fossil fuels are a non-renewable form of energy that exist in finite quantities and therefor need to be managed. The burning of fossil fuels contributes to climate change and impacting the environment.

Figure 5.1: Total Fuel Sales and Fuel Sales per Capita by Year

1350

1400

1450

1500

1550

1600

1,400,000,000

1,500,000,000

1,600,000,000

1,700,000,000

1,800,000,000

1,900,000,000

2,000,000,000

2010 2011 2012 2013 2014 2015 2016

Fuel

Sal

es p

er C

apita

(L/c

ap)

Fuel

Sal

es (L

)

Year

Fuel Sales

Fuel Sales (Litres) Fuel Sales Per Capita (L/cap)


growth plan: monitoring and reporting indicators Figure 5.2: Total GHG Emissions and GHG Emissions per Capita by Year

3.32

3.37

3.42

3.47

3.52

3.57

3.62

3.67

3.72

3.77

3,500,000

3,700,000

3,900,000

4,100,000

4,300,000

4,500,000

4,700,000

4,900,000

2010 2011 2012 2013 2014 2015 2016

GHG

per

Cap

ita

GHG

(ton

nes o

f CO

2e/L

)

Year

GHG Emissions

GHG (tonnes of CO2e/L) GHG per Capita

Data Source Kent Group Ltd.


Fuel Sales data is surveyed for Beaumont, Edmonton, Fort Saskatchewan, Leduc, Parkland County, Spruce Grove, St.Albert, Stony Plain and Strathcona County. This data is aggregated for total ‘Fuel Sales’ in the region and divided by population to determine the ‘Fuel Sales per Capita’. GHG Emissions are determined by the following formulas: For gasoline: L of gasoline x 0.00238 tonnes of CO2e/L For diesel: L of diesel x 0.00279 tonnes of CO2e/L



Indicator 6 - Ambient Air Quality

Description

This indicator measures the percentage of hours in a year that each air quality reporting station in the region experienced the various risk levels of the Air Quality Health Index (AQHI). The AQHI uses readings from three air pollutants which are required to calculate a single numerical value to evaluate the health risk associated with air pollution. The three pollutants are fine particulate matter, nitrogen dioxide and ground-level ozone. The charts are presented here in geographical order from West to East.


The AQHI is a health protection tool that is designed to help the public make decisions to protect their health by limiting short-term exposure to air pollution and adjusting their activity during increased levels of air pollution.

Figure 6.1: AQHI Index Legend

http://esrd.alberta.ca/air/programs-and-services/air-quality-health-index/about-AQHI.aspx Figure 6.2: Tomahawk Station – AQHI Ratings by Year

97.20% 98.69% 99.01% 99.28% 97.51% 98.90%

2.75% 1.11% 0.99% 0.72% 2.25% 1.10%0.05% 0.21% 0.24%

75%

80%

85%

90%

95%

100%

2011 2012 2013 2014 2015 2016

Low Moderate High Very High

http://esrd.alberta.ca/air/programs-and-services/air-quality-health-index/about-AQHI.aspx



Figure 6.3: Genesee Station – AQHI Ratings by Year

Figure 6.4: Edmonton Station – AQHI Ratings by Year

Figure 6.5: Fort Saskatchewan Station – AQHI Ratings by Year

99.13% 99.35% 99.22% 99.62% 98.79% 99.62%

0.82% 0.50% 0.78% 0.38% 0.88% 0.38%0.05% 0.15% 0.33%

75%

80%

85%

90%

95%

100%

2011 2012 2013 2014 2015 2016


78.42%

87.03%

80.91%86.43%

90.63% 91.47%

20.99%

12.79%

18.36%

13.56%8.94% 8.41%

0.58% 0.18% 0.73% 0.01% 0.43% 0.11%

75%

80%

85%

90%

95%

100%

2011 2012 2013 2014 2015 2016


91.60%95.29%

91.68% 93.77% 93.98% 94.60%

8.22%4.68%

7.88%6.11% 5.72% 5.33%

0.18% 0.03% 0.45% 0.13% 0.29% 0.06%

75%

80%

85%

90%

95%

100%

2011 2012 2013 2014 2015 2016



growth plan: monitoring and reporting indicators Figure 6.6: Bruderheim Station – AQHI Ratings by Year

Figure 6.7: Lamont Station – AQHI Ratings by Year

93.17%97.39%

90.39%95.62% 95.33%

100.00%

6.53%2.40%

9.30%4.34% 4.38%

0.30% 0.22% 0.31% 0.05% 0.29%

75%

80%

85%

90%

95%

100%

2011 2012 2013 2014 2015 2016


94.24%98.20%

95.67% 96.93%94.36%

96.72%

5.71%1.77%

4.32% 2.98%5.24%

3.08%0.06% 0.03% 0.01% 0.09% 0.40% 0.19%

75%

80%

85%

90%

95%

100%

2011 2012 2013 2014 2015 2016


Data Source Clean Air Strategic Alliance (CASA)


1. Go to CASA website to download data: www.casadata.org 2. Select ‘Data Reports’ link 3. Select ‘AQHI Data Download’ link 4. Select ‘Start and End Date’ for the full calendar year 5. Select Stations – Bruderheim, Edmonton, Fort Saskatchewan, Lamont County 6. Run Report 7. Calculate the total number of hours in each AQHI category by year

http://www.casadata.org/



Indicator 7 – Distribution of Market and Non-Market Housing

Description

This indicator measures the distribution of market and non-market housing units as a proportion of the total number of housing units, by sub-region.


A housing market should provide housing to a range of family types and income levels. This requires different housing types, including subsidized housing (ongoing government subsidies), non-market affordable housing (no operating subsidies), market affordable housing (housing available for moderate incomes but not requiring government subsidies) and market housing. Having a range in housing affordability options is important because it will attract and retain a stable workforce.

Figure 7.1: Market vs Non-Market Housing Stock by Sub-Region (2011)

92.36%(324,760)

92.48%(2,630)

96.60%(22,455)

97.74%(26,640)

97.75%(40,460)

97.92%(34,970)

93.74%(451,915)

7.64%(26,869)

7.52%(214)

3.40%(791)

2.26%(616)

2.25%(931)

2.08%(742)

6.26%(30,163)

88.00%

90.00%

92.00%

94.00%

96.00%

98.00%

100.00%

Edmonton Lamont Leduc Parkland Strathcona/FortSaskatchewan

Sturgeon Total

Market Non-Market



Indicator 8 – Funding for Non-Market Housing

Description

This indicator measures the amount of funding provided for non-market housing development by the province, and by the federal government.


Non-market housing requires subsidies and on-going funding from senior levels of government. It is the Province’s role to ensure sufficient and sustainable funding is available to meet the needs of non-market housing across the Capital Region.

Figure 8.1: Provincial Funding for Non-Market Housing by Fiscal Year

$0

$20,000,000

$40,000,000

$60,000,000

$80,000,000

$100,000,000

06/07 07/08 08/09 09/10 10/11 11/12 12/13 13/14 14/15

Fund

ing

Fiscal Year

Data Source Government of Alberta, Ministry of Seniors and Housing

Method of Measurement Collect funding data and aggregate by fiscal year.



Indicator 9 – Housing Mix

Description

This indicator measures the distribution of housing types across the Capital Region.


The full range of housing types is necessary to create complete communities. A diversity of housing types suggests greater choice for a variety of lifestyles and budgets and supports improved housing affordability. This measure is also useful for monitoring policies related to minimizing the regional footprint. Increasing the share of multi-unit dwellings uses land more efficiently and promotes population densities that are better able to support walking, cycling, and transit. Energy, materials, and water consumption also tend to be lower on a per capita basis.

Figure 9.1: Regional Housing Distribution by Structure Type (2011)

58.70%

15.15%

24.45%

1.69%

Single Detached Semi-Detached/Row/Duplex Apartment Other


growth plan: monitoring and reporting indicators Table 9.1: Housing by Structure Type and Municipality (2011)

Municipality Single

Detached

Semi-Detached/Row

/Duplex Apartment Other Total Beaumont 3,765 465 140 5 4,375 Bon Accord 465 60 0 15 540 Bruderheim 385 25 20 15 445 Calmar 615 45 50 55 765 Devon 1,885 290 200 10 2,385 Edmonton 165,760 55,695 100,280 3,015 324,750 Fort Saskatchewan 4,795 1,335 1,205 0 7,335 Gibbons 955 35 35 50 1,075 Lamont 595 25 10 15 645 Lamont County 1,440 0 0 140 1,580 Leduc 6,085 1,265 1,565 375 9,290 Leduc County 4,420 30 0 515 4,965 Legal 395 35 5 0 435 Morinville 2,335 430 245 70 3,080 Parkland County 9,655 70 20 1,195 10,940 Redwater 655 50 35 30 770 Spruce Grove 6,930 1,245 1,155 290 9,620 St. Albert 16,690 3,365 2,455 5 22,515 Stony Plain 3,610 725 1,165 320 5,820 Strathcona County 27,375 2,945 1,890 915 33,125 Sturgeon County 5,750 300 0 500 6,550 Thorsby 285 35 10 5 335 Wabamun 215 5 15 30 265 Warburg 235 5 5 65 310

Total 265,295 68,480 110,505 7,635 451,915

Data Source Statistics Canada

Method of Measurement Collect housing structure data for each community and calculate the sum total.



Indicator 10 – Household Spending

Description

This indicator measures the average household spending on housing and transportation costs as a percentage of total household spending. Household spending categories include: food, shelter, household operation, clothing, transportation, etc…


Affordable housing is more prevalent in suburban or rural areas however a direct trade-off of living in these areas is increased car dependency resulting in higher commuting costs. This measure tracks the proportion of spending on shelter and transportation. If the livability of communities is strengthened, households will be able to reduce their spending on transportation without compromising the affordability of their housing.

Data source terminated in 2009

(Statistics Canada Table 203-0001)



Indicator 11 – Commuting Duration

Description

Median commuting duration.


Knowing how far residents are traveling to work is an important first step in developing strategies to reduce commuting distances through such things as increased transit options and creating more local jobs.

The median commute duration in 2011 for the Capital region:

24.85 minutes


growth plan: monitoring and reporting indicators Table 11.1: Commuting Duration by Municipality (2011)

Municipality 2011 Median Commuting Time

(Minutes) Leduc 15.6 Fort Saskatchewan 20.3 Devon 20.7 Edmonton 20.7 St. Albert 20.7 Spruce Grove 20.9 Strathcona County 20.9 Beaumont 23.7 Leduc County 25.1 Calmar 25.4 Lamont 25.5 Bruderheim 25.6 Stony Plain 25.6 Lamont County 25.7 Parkland County 30.0 Morinville 30.1 Gibbons 30.3 Sturgeon County 30.3 Bon Accord 35.1 Legal * Redwater * Thorsby * Wabamun * Warburg *

Total (Average) 24.85 Sorted in ascending order * Data for this area has been supressed for data quality or confidentiality reasons

Data Source National Household Survey

Method of Measurement Collect median commuting duration for each municipality and calculate the total.



Indicator 12 – Commuting Mode Split

Description

This indicator measures the distribution of transportation modes used to travel to work, expressed as a percent. The breakdown includes: motorized vehicles (driver), motorized vehicles (passenger), transit, cycling, walking and other.


Mode split shows how people are travelling in the region and helps track the success of reducing car dependence.

Figure 12.1: Community Mode Split in the Capital Region (2011)

76.7%

5.5%

11.3%

4.1%1.1% 1.3%

Driver

Passenger

PublicTransit

Walked

Bicycle

Other


growth plan: monitoring and reporting indicators Table 12.1: Commuting Mode Split by Municipality (2011)

Municipality Driver Passenger Public Transit Walked Bicycle Other

Beaumont 91.5% 3.2% 1.2% 1.9% 0.5% 1.6% Bon Accord 89.3% 2.7% 5.4% 2.7% 0.0% 0.0% Bruderheim 86.0% 8.8% 0.0% 5.3% 0.0% 0.0% Calmar 84.2% 7.7% 0.0% 4.8% 0.0% 3.3% Devon 91.2% 4.6% 0.3% 3.2% 0.0% 0.8% Edmonton 72.2% 5.7% 14.8% 4.6% 1.4% 1.3% Fort Saskatchewan 84.1% 5.9% 2.7% 5.1% 0.5% 1.6% Gibbons 83.7% 9.3% 3.0% 1.0% 0.0% 3.0% Lamont 92.1% 3.3% 0.0% 4.6% 0.0% 0.0% Lamont County 91.2% 2.6% 1.5% 3.2% 0.0% 1.5% Leduc 87.2% 4.9% 1.6% 3.8% 0.8% 1.7% Leduc County 89.8% 3.6% 0.4% 5.1% 0.0% 1.1% Legal * * * * * * Morinville 89.6% 4.5% 1.3% 3.5% 0.0% 1.1% Parkland County 90.6% 4.8% 1.9% 1.5% 0.0% 1.3% Redwater * * * * * * Spruce Grove 87.2% 6.1% 1.7% 3.1% 0.6% 1.3% St. Albert 84.8% 4.7% 5.6% 3.2% 0.3% 1.4% Stony Plain 87.3% 6.5% 1.7% 3.0% 0.3% 1.1% Strathcona County 87.4% 5.0% 3.9% 1.8% 0.4% 1.5% Sturgeon County 91.3% 3.2% 1.6% 1.9% 0.2% 1.8% Thorsby * * * * * * Wabamun * * * * * * Warburg * * * * * *

Total 76.7% 5.5% 11.3% 4.1% 1.1% 1.3% * Data for this area has been supressed for data quality or confidentiality reasons


Method of Measurement Collect median commuting duration for each municipality and calculate the total.



Indicator 13 – Employment Diversity

Description

This indicator measures the distribution of employment by industry category.


Measuring employment diversity helps understand the underlying economic health of communities. A labour force that is employed by a broad range of industries increases resiliency to economic downturn because diverse economies tend to experience lower employment volatility. These trends can be used when developing employment strategies and in identifying economic opportunities.

Figure 13.1: Employment Diversity in the Capital Region by NAICS Job Class (2011)


Method of Measurement Collect industry data for each member municipality and aggregate the regional totals.

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000



Indicator 14 – Employment Rate

Description

This indicator measures the number of employed people as a proportion of the total labour force (where labor force is population 15 years of age and over, excluding institutional residents).


The employment rate is a measure of the proportion of working age population that are working for pay, and thus in a position to earn income to take care of themselves and their families. A high employment rate is a good marker of a healthy economy.

Total Regional Labour Force (2011):

683,480

Total Employed Labour Force (2011):

645,240 (94.4%)


Method of Measurement Collect labour force data for each member municipality and aggregate the regional totals.



Indicator 15 – Distribution of Regional Employment

Description

This indicator measures where people are reporting they work within the Capital Region. It provides an understanding of how employment is distributed in the region, both by PGAs and by municipality.


Knowing how employment opportunities are distributed throughout the region helps plan for potential additional housing and infrastructure.

Table 15.1: Regional Employment by Municipality (2011)

Municipality Total Labour Force Employed Unemployed % Employed % of Regional

Labour Force

Beaumont 7,565 7,195 365 95.11% 1.11% Bon Accord 835 790 50 94.61% 0.12% Bruderheim 710 645 65 90.85% 0.10% Calmar 1,145 1,095 50 95.63% 0.17% Devon 3,685 3,480 205 94.44% 0.54% Edmonton 480,650 451,395 29,250 93.91% 70.33% Fort Saskatchewan 10,915 10,365 550 94.96% 1.60% Gibbons 1,645 1,555 85 94.53% 0.24% Lamont 945 930 115 98.41% 0.14% Lamont County 2,385 2,320 70 97.27% 0.35% Leduc 14,555 13,830 725 95.02% 2.13% Leduc County 8,530 8,340 190 97.77% 1.25% Legal * * * * * Morinville 5,090 4,845 245 95.19% 0.74% Parkland County 18,360 17,555 810 95.62% 2.69% Redwater * * * * *


growth plan: monitoring and reporting indicators Spruce Grove 15,470 14,705 765 95.05% 2.26% St. Albert 36,430 34,870 1,560 95.72% 5.33% Stony Plain 8,255 7,850 405 95.09% 1.21% Strathcona County 54,780 52,460 2,320 95.76% 8.02% Sturgeon County 11,480 11,015 465 95.95% 1.68% Thorsby * * * * * Wabamun * * * * * Warburg * * * * *

Total 683,430 645,240 38,290 94.41% * Data for this area has been supressed for data quality or confidentiality reasons


Method of Measurement Collect labour force data for each member municipality and break it down by member municipality.



Indicator 16 – Planned Densities of Newly Approved Residential Areas

Description

This indicator measures the planned densities of new residential areas in priority growth areas (PGAs) approved since the original Growth Plan went in to effect in March of 2010.


Knowing what is planned gives us an idea of what to expect in the future. This indicator also shows the direct effect that density intensification targets in the Growth Plan has had on newly planned residential areas.

100% of residential plans approved since the Growth Plan have met density targets.

Residential plans approved since the Growth Plan:

39

Data Source Approved Residential Plans (NSPs and ASPs)

Method of Measurement The planned densities for each NSP/ASP is collected and put into a single table



Table 16.1: Planned Densities of Approved New Residential Areas* Since March 2010

Municipality Name PGA** Planned

Residential Area (ha)

Planned Dwelling

Units

Planned Density

(Du/Nrha***)

Density Target

(Du/Nrha***)

Meets Density Target

Beaumont Dansereau Meadows Ce 35.23 979 27.79 25-35

Beaumont Ruisseau Ce 18.7 498 26.63 25-35

Edmonton Decoteau B 862.6 29,333 34.01 30-45+

Edmonton Goodridge Corners B 81.01 3,385 41.78 30-45+

Edmonton Maple B 73.7 2,700 36.64 30-45+

Edmonton Crystallina Nera East B 41.92 1,466 34.97 30-45+

Edmonton Cy Becker B 58.18 2,019 34.70 30-45+

Edmonton Hawks Ridge B 65.81 2,216 33.67 30-45+

Edmonton Starling B 58.42 1,962 33.58 30-45+

Edmonton Desrochers Cw 46.51 2,067 44.44 30-40

Edmonton Cavanagh Cw 59.45 2,209 37.16 30-40

Edmonton Hays Ridge Cw 62.31 2,199 35.29 30-40

Edmonton Heritage Valley Cw 36.67 1,287 35.10 30-40

Edmonton Graydon Hill Cw 16.03 558 34.81 30-40

Edmonton Keswick Cw 188.35 6,299 33.44 30-40

Edmonton Edgemont Cw 180.04 5,950 33.05 30-40

Edmonton Glenridding Heights Cw 60.46 1,975 32.67 30-40

Edmonton Riverview Cw 611.18 18,730 30.65 30-40

Edmonton Horse Hill F 890.3 27,599 31.00 30-40

Fort Saskatchewan Southfort G 322.64 8,700 26.97 25-30

Leduc West E 109.68 3,211 29.28 25-30

Leduc Southfork E 120.38 3,498 29.1 25-30

Leduc Blackstone E 34.15 1,032 30.2 25-30

Leduc Brightwell E 36.8 1,103 29.97 25-30

Leduc South East Leduc E 77.83 2,063 26.5 25-30

Leduc County East Vistas Ce 317.5 8,673 27.32 25-35

Spruce Grove East Pioneer A 85.06 2,904 34.14 25-30

Spruce Grove Shiloh A 50.04 1,440 28.78 25-30

Spruce Grove West A 183.32 5,200 28.37 25-30

Spruce Grove Heritage Estates A 44.01 1,201 27.29 25-30

St. Albert Ville Giroux B 14.36 746 51.95 30-45+

St. Albert Avenir B 41.75 1,730 41.44 30-45+

St. Albert Jensen Lakes B 70.49 2,119 30.06 30-45+

Stony Plain Fairways North A 10.58 279 26.37 25-30

Stony Plain East Boundary A 46.28 1,154 25 25-30

Stony Plain Parkland Gateway A 9.9 422 42.6 25-30



* New Residential Areas - Newly planned residential areas where no prior plans existed. ** PGA = Priority Growth Area as defined In the Capital Region Growth Plan ***Du/Nrha = Dwelling Units per Net Residential Hectare

Strathcona: Sherwood Park Salisbury Village B 10.29 694 67.44 30-45+

Strathcona: Sherwood Park Emerald Hills B 31.05 1,935 62.32 30-45+

Strathcona: Sherwood Park Cambrian Crossing B 87.27 2,638 30.23 30-45+

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