mapping attractive urban areas - inspire€¦ · perception survey” of the european commission...

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Mapping attractive urban areas– a geographical and quantitative approach to determining “Quality of life” parameters

Svein Reid - Senior adviser

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General objective of project : combine relevant statistical registers and georeferenced data to complement the “Quality of life in cities -Perception survey” of the European Commission

• “Quality of life in cities - Perception survey” in 79 European cities published in October 2013.

• New edition of the same surveys carried out in 2004, 2006 and 2009.

• In total 41.000 people were interviewed on various aspects of urban life - assess quality of services such as public transport, health care, education, cultural and sport facilities,...

• Asked to identify the three most important issues for their city. For Oslo health services, education facilities and public transport

Mapping attractive urban areas

How complement the Perception survey ?

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• Qualitative approach > Quantitative approach

• Making interviews of 41.000 persons is time consuming. Testing out alternative data resources first, such as statistical registers and georeferenced data, can provide valuable information, and make the following information gathering process more effective.

• Between cities > within cities

• Does “household income levels” or “Educatation levels” mirror which parts of a city are more attractive than others, shedding light on how divided a city is.

• To which degree is this true in other comparable cities?

• Are there variables found significant throughout which are open to policy change –”distance to school”?

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• Step 1: A measure of “truth” on objective attractivity

A : Real estate sales of dwellings in Norway, 2014 - sales sum, square meters dwelling area

B : - Joined with georeferenced building register

Mapping attractive urban areas

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• Step 1: A measure of “truth” on objective attractivity

A : Real estate sales of dwellings in Norway, 2014 - sales sum, square meters dwelling area

B : - Joined with georeferenced building register

• Step 2: Spatially joined «inhouse» (STATISTICS NORWAY) data to each Real estate sale

A : Potential explanatory variables :

- Distance to : Hospitals, Centre zones, recreation areas, schools, public transport, Restaurants, coastline,…

- Intensity: Noise,..

- Socioeconomic variables based on population in proximity:

Mean education level (age26+), median income, immigration levels

Mapping attractive urban areas

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• Step 3: Use regression analysis to find «inhouse» explanatory variables which best explain the different sales prices within Oslo

A : Choice of dependent variable - What do we want to explain ?

B : Choice of explanatory variables - Iterative process, try and fail - applying logic and patience

Mapping attractive urban areas

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Choice of Dependent variable

Total sales sum Kroner pr square meter

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Total sales sum Kroner pr square meter

Average KrSQM+-5 Kroner pr square meter for properties of 20sqm DIVIDED by Mean Kroner pr square meter for properties 16sqm-25sqm

Choice of Dependent variable

Exploratory regression analysis in order to obtain insights in the relationships.

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• Step 4: Extend scope to all Norwegian urban settlements > 50 000 inhabitants,

• Explore significant variables throughout all settlements

• defining a few general models with explanatory value for all the nine urban settlements

• Define an attractivity index

• Step 5: Calculate and join chosen explanory variables (step 4) to Norways Georeferencedbuilding register

• Create attractiveness datasets for all nine urban settlements

• Step 6: Deliverables:

• A report describing how to combine various data sources in order to generate an attractiveness dataset in Norway and a methodology to be used by other European settlements.

• Datasets over the attractiveness in the nine most populated urban settlements of Norway.

Mapping attractive urban areas

Explanatory variables -

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Sqaure meters of sold real estate• Significant,Education level of population within 250 m of each sale :• Significant, with rising Adjusted R2 (explanatory value) by size of urban settlement. Distance to Centre zone in urban settlement:• SignificantDistance to Restaurant : • Significant - attractivity within neighbourhoodsDistance to Coast :• SignificantDistance to lakes/rivers :• SignificantMedian Household income within 250 m of sale:• Ambiguous, possible problem with dataPercentage non-western immigrant popluation within 250 m of sale:• Mostly Significant, ambigous

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Distance to schools, public transport, recreational areas, etc,,,, • Not Significant or ambiguous

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Oslo – Total sum and Predicted – 500 m grid

AdjR2 = 0.78 -977383.2012+ ( [Restaurant] * -180.890153) + ([Kyst_avst] * 5.184099) + ([AND_REGANN] * -6598.063153) + ([SNITTUTD8] * 578436.57762) + ([SENTRS_A_1] * -65.424740) + ([Allvann_avst] * -85.246223) + ([Bra_just] * 30088.716810)

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Oslo – Kr pr SQM and Predicted – 500 m grid

AdjR2 = 0.66

24501.321552 + ( [Restaurant] * -2.231185) + ([Kyst_avst] * 0.193288) + ([AND_REGANN] * -67.676577) + ([SNITTUTD8] * 8087.861703) + ([Bra_just] * -

103.906825) + ([SENTRS_A_1] * -0.746497) + ([Allvann_avst] * -1.530269)

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Oslo – Index and Predicted – 500 m grid

AdjR2 = 0.55

0.380682 + ( [Restaurant] * -0.000044) + ([Kyst_avst] * 0.000002) + ([AND_REGANN] * -0.001932) + ([SNITTUTD8] * 0.171025) +

([SENTRS_A_1] * -0.000014) + ([Allvann_avst] * -0.000028)

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Trheim – Total sum and Predicted – 500 m grid

AdjR2 = 0,72619334.88611 + ( [Restaurant] * -96.734154) + ([Kyst_avst] * -61.790997) + ([AND_REGANN] * -3942.392443) + ([SNITTUTD8] *

247575.40726) + ([SENTRS_A_1] * -30.931909) + ([Allvann_avst] * -25.901570) + ([Bra_just] * 17480.697175)

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Trheim – KR SQM and Predicted – 500 m grid

AdjR2 = 0,6942724.269090 + ( [Restaurant] * -1.786759) + ([Kyst_avst] * -0.810291) + ([AND_REGANN] * -57.475531) + ([SNITTUTD8] *

2707.434822) + ([Bra_just] * -130.737514) + ([SENTRS_A_1] * -0.396539) + ([Allvann_avst] * -0.780047)

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Trheim – Index and Predicted – 500 m grid

AdjR2 = 0,320.714207 + ( [Restaurant] * -0.000038) + ([Kyst_avst] * -0.000018) + ([AND_REGANN] * -0.001956) + ([SNITTUTD8] * 0.090805) +

([SENTRS_A_1] * -0.000003) + ([Allvann_avst] * -0.000017)