Mapping attractive urban areas

Transcription

1 Mapping attractive urban areas a geographical and quantitative approach to determining Quality of life parameters Svein Reid - Senior adviser 1

2 Mapping attractive urban areas 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 New edition of the same surveys carried out in 2004, 2006 and In total 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 2

3 How complement the Perception survey? Qualitative approach > Quantitative approach Making interviews of 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? 3

4 Mapping attractive urban areas Step 1: A measure of truth on objective attractivity A : Real estate sales of dwellings in Norway, sales sum, square meters dwelling area B : - Joined with georeferenced building register 4

5 Mapping attractive urban areas Step 1: A measure of truth on objective attractivity A : Real estate sales of dwellings in Norway, 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 5

6 Mapping attractive urban areas 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 6

7 Choice of Dependent variable Total sales sum Kroner pr square meter 7

8 Choice of Dependent variable 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 8

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

10 Mapping attractive urban areas Step 4: Extend scope to all Norwegian urban settlements > 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 Georeferenced building 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. 10

11 Explanatory variables - 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: Significant Distance to Restaurant : Significant - attractivity within neighbourhoods Distance to Coast : Significant Distance to lakes/rivers : Significant Median Household income within 250 m of sale: Ambiguous, possible problem with data Percentage non-western immigrant popluation within 250 m of sale: Mostly Significant, ambigous Distance to schools, public transport, recreational areas, etc,,,, Not Significant or ambiguous 11

12 Oslo Total sum and Predicted 500 m grid AdjR2 = ( [Restaurant] * ) + ([Kyst_avst] * ) + ([AND_REGANN] * ) + ([SNITTUTD8] * ) + ([SENTRS_A_1] * ) + ([Allvann_avst] * ) + ([Bra_just] * ) 12

13 Oslo Kr pr SQM and Predicted 500 m grid AdjR2 = ( [Restaurant] * ) + ([Kyst_avst] * ) + ([AND_REGANN] * ) + ([SNITTUTD8] * ) + ([Bra_just] * ) + ([SENTRS_A_1] * ) + ([Allvann_avst] * ) 13

14 Oslo Index and Predicted 500 m grid AdjR2 = ( [Restaurant] * ) + ([Kyst_avst] * ) + ([AND_REGANN] * ) + ([SNITTUTD8] * ) + ([SENTRS_A_1] * ) + ([Allvann_avst] * ) 14

15 Trheim Total sum and Predicted 500 m grid AdjR2 = 0, ( [Restaurant] * ) + ([Kyst_avst] * ) + ([AND_REGANN] * ) + ([SNITTUTD8] * ) + ([SENTRS_A_1] * ) + ([Allvann_avst] * ) + ([Bra_just] * ) 15

16 Trheim KR SQM and Predicted 500 m grid AdjR2 = 0, ( [Restaurant] * ) + ([Kyst_avst] * ) + ([AND_REGANN] * ) + ([SNITTUTD8] * ) + ([Bra_just] * ) + ([SENTRS_A_1] * ) + ([Allvann_avst] * ) 16

17 Trheim Index and Predicted 500 m grid AdjR2 = 0, ( [Restaurant] * ) + ([Kyst_avst] * ) + ([AND_REGANN] * ) + ([SNITTUTD8] * ) + ([SENTRS_A_1] * ) + ([Allvann_avst] * ) 17