Recently the importance of urban density and agglomeration advantages have seen a remarkable revival in the field of economic geography and urban planning. For example, Emil Malazia et al (2015), have found a correlation between urban density and economic growth in census track areas of Washington, D.C. Also the importance of dense mixed land use is commonly understood as an enabler of healthy and efficient communities (e.g. Musakwa and Niekerk (2013) ,Song and Rodríguez (2005). In our study, we have predicted which areas in Helsinki Metropolitan Region could benefit from more dense and mixed urban structure. Our analysis recognized two zip code areas with huge housing demand: Ruoholahti and Pitäjänmäki in western Helsinki. The areas with second highest housing demand were in Center of the Helsinki, Munkkiniemi, Itä-Pasila, Kaartinkaupunki and Meilahti in western Helsinki as well as Pohjois-Tapiola, Otaniemi, Pohjois-Leppävaara, Etelä-Leppävaara, Niittykumpu and Nihtisilta in eastern Espoo and Oitmäki in western Espoo as well as Kirkonkylä-Veromäki in Vantaa. Areas with highest potential for innovative growth locate mainly between areas’ with demand for housing. Especially the edges of the inner city as well as few subcenters on the ring roads get predictions of higher innovative output than today. Länsi-Pasila, Jätkäsaari and Pikku Huopalahti from Helsinki are on their own class with their innovative potential. Second highest innovative growth is predicted from Helsinki to Kulosaari, Kaitalahti and Kaivopuisto and from Espoo Laajalahti-Friisinmäki from Espoo and from Vantaa the zip code area Jokiniemi.
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Some previous innovation studies have recognized the existence of innovation paradoxes, meaning that some regions exhibit stronger (innovation prone) and some exhibit weaker (innovation averse) than expected economic growth relative to their R&D activity (Rodriguez-Pose 1999, Makkonen & Inkinen 2013).
In my PhD thesis, I have identified the under- and overachieving clusters in Helsinki Metropolitan Area in regard to the present state geography of human capital. This gives major advice to planning authorities of the region by highlighting the areas with the most potential for innovative growth. Method is multivariate spatial regression with GeoDa software. Analyzing the connection between zip code area's and its neighbors level of human capital and cluster's innovative output, we get the predictions of the estimated innovative output of the cluster and the residual values of each area, which show how much the area's development is lagged at the present. Results show that new clusters of knowledge intensive jobs and thus innovations could emerge into outskirts of inner city of Helsinki as well as some sub centers in Espoo and Vantaa. Areas rasterized with both styles represent innovation prone areas in regard to both, absolute and relative level of nearby human capital. These areas are southern and northern edge of the inner city in Helsinki as well as Kera, Mankkaa and Laajalahti in Espoo and Jokiniemi in Vantaa. Findings encourage to sufficient zoning of commercial space in ongoing planning of inner city extensions in Helsinki and planning of certain new or developing sub centers in Espoo and Vantaa. The role of innovations in local economic development dates back to the late 1800s, when Marshall introduced the concept of industrial district. However, not until hundred years later Marshall’s thoughts saw a revival and refined into concepts of learning regions, innovative milieux, the triple helix model, innovation clusters and regional innovation systems.
One of the key elements in Marshall’s industrial districts and late followers is the role of skilled labor in innovative growth. Few studies have recognized the importance of skilled workforce in regional innovation systems (e.g. Zucker et al., Henry & Pinch, Florida, Boschma et al, Lawton Smith, Makkonen & Inkinen). The concept of regional innovation system has some advantages as it recognizes the role of the active networks between different actors from regional to global level (Asheim). However, in comparison to innovation clusters (Porter), regional innovation systems are a-spatial in nature (Asheim). The role of skilled workforce in spatially more locally defined innovative growth, i.e. in innovation clusters, hasn’t been studied yet. The following statistics includes different indicators of human capital and creative class from the literature as well as some new introductions. Also the concept of examining both the relative and the absolute level of human capital is somewhat new to the subject. In addition, the effect of human capital in area's or region's innovativeness hasn't been studied before in regard to the neighbor areas as well. Significant human capital indicators predicting intra-metropolitan innovativeness are the following:
Findings are based on spatial regression models. I classified variables into absolute and relative sets and the result showed that absolute human capital indicators predict 83 % of the innovative output of the area as relative human capital indicators explain only 51 % of the innovativeness of the area. Examining absolute human capital indicators is something that can be implemented to the subject. Also new introduction, popularity of the immigration critical political parties, is something that can be used in other studies as a indicator of area's tolerance. Introducing the effect of not only the examined area itself but also the neighbor areas, i.e. spatial regression, didn't bring much to the subject. Indicators Measuring Area's Innovative Capacity and the Ranking of Helsinki Metropolitan Area4/11/2015 Innovative capacity of regions have often been measured by amount of patents, research & development spending and number of knowledge intensive workplaces. Inkinen & Kaakinen (2015) classified KIBS workplaces into three categories: IT, R&D and business services. They also used both, area’s absolute and relative amount of workplaces. In my PhD thesis, I have collected the most prominent indicators at zip code area level from Helsinki Metropolitan Area. All of them were kind of open data, you just had to ask from right places. R&D data is from Tekes - the Finnish Funding Agency for Innovation. I included only private sector projects in my analyses. There’s certainly a number of other R&D projects as well, but I think projects funded by Tekes indicates quite reasonably the overall amount of research and development activity. Data of patents I got from Finnish Patent and Registration Office. Data was a list of patents with the applicants address and zip code, like in the data from Tekes. GIS data of workplaces is from HSY - the Helsinki Region Environmental Services. I aggregated the point data into zip code area level, so it could be compared with other indicators as well. All the variables are from 2012, so they can be compared to human capital/creative class indicators as well. I tested which variables are most significant indicators of innovation capacity, at least what comes to this case study. Method I used was Principal Component Analysis (PCA) Analysis. Results of the PCA analysis (Table 1) shows that from collected variables, everyone but proportion of KIBS II (R&D) workplaces, indicates the same phenomena. The highest correlations are in all the KIBS workplaces together and in the number of R&D projects. Number of patents seems to indicate innovative capacity the least of significant variables. Contributing to local authorities of HMA, I made also kind of a ranking of the most innovative areas in HMA. I included all the significant variables from PCA analysis into ranking. First, I scaled every variable into index from 0-100. After that, I scaled them into one index, which I call Innovation index. The most innovative areas in HMA are listed in table 2. Maybe surprisingly, the center of the Helsinki is the second innovative area despite the first place in three of the categories. Seven of the top ten innovative areas are from Helsinki, and three from Espoo. |
Photo by Rob Hurson
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July 2018
AuthorJuho Kiuru, geographer living in Helsinki, Finland. |