How to Attract the Right Economic Activities in a Certain Spatial Environment?

During the past few years, our research has examined and described the spatial patterns and organisation of economic activities. In order to link these findings to policy, we introduced the concepts of demand and supply segments, and applied them on the scale of an area or a certain spatial environment. Considering the business needs of companies on a certain location, we identified 16 demand parameters of companies, that are spatially relevant on the scale of an area: the size of good flows, the alternative freight transport, the nearness to the market,... Literature, interviews and observations offer supporting evidence for the parameters. We linked them to 24 other parameters that reflect the characteristics of the area where a company is located. These include amongst others mobility, level of foot fall, the presence of green infrastructure, other companies (or mix of companies), density, parking possibilities,... The combination of this information with our typology of economic area’s (Giaretta, Pennincx, De Mulder, Zaman, 2019) resulted into 24 main segments, that show the relation between demand of companies and supply of spatial characteristics on the scale of an area. The segments are ideally grouped according to the characteristics, and in this sense they differ from typology of economic areas, that is based on the observed location preferences of companies. This way of grouping into segments generates new questions, that enable us to spatially differentiate economic environments, and to make decisions regarding the location of economic activities. We aim at getting concrete answers to three main questions: (1) Is my company located in the right place? Does this area spatially deliver what my company needs? (2) Does the area deliver the right services, that the companies in this area need? (3) If we want to transform an area, which area characteristics do we need to change in order to attract the wanted companies? We subdivide these three main questions into sub questions. The first question considers the demand side and uses the micro-economic considerations, made by a company, in order to choice a certain segment. Several questions succeed each other and deal with the demand of companies regarding the effects of agglomeration, economic and environmental spatial use, freight transport, price per square meter,... The second question can lead to the segment that is the closest to the actual situation, based on the typology of economic areas. Indeed, there is usually a gap between the actual situation and the best fitting segment. Using the typology and the segments on an actual situation uncovers information about visibility, land price value, good flows, land use plan. The third and last question deals with areas that are in a process of transformation. After finding out the desirable segment, it is possible to evaluate which companies belong to this segment, which need to adapt or to disappear. In addition, the transition in terms of services that the area delivers (which is implied when transforming from one segment to another), can be determined

Enhanced Economic Typology for Spatial Economic Policy

This paper tries to actively contribute to the discussion concerning spatial planning and related policies being frequently criticised for their poor ability to accommodate economic dynamics, resulting in tension between spatial and economic development and inefficient planning decisions or instruments. Considering the importance of the economy and its fundamental role in our society, in addition to the lack of knowledge about what the word economy really means and how it is organised in a territory, we strongly believe that it should also be deeply studied and understood by planners and policy makers. In our previous papers we defined some instruments to use to fill this existing gap in knowledge. The first was economic activities mapping, consisting of an attempt of auditing and classifying economic activities in a given area (Giaretta & Zaman, 2017). Although this led to some interesting results, its use as a tool for the definition of the spatial distribution of economic activities or for the comparison of different economic areas,proved to be complicated. Therefore, in a later stage, two new versions of the typology of areas with economic activities were elaborated, in which we tried to divide a real territory into different types of existing economic fabrics. The first version, was based on more subjective criteria, using generally known planning concepts, such as city centres, core shopping centres, access roads, industrial areas and so on, to delimit several economic areas (Gruijthuijsen et al., 2018). The second version was based on more objective criteria, such as the combinations of the mapping data and the proximity between economic activities(Giaretta, Pennincx, De Mulder, Zaman, 2018). The second version turned out to be more interesting, as it really showed economic structures and patterns from an economic perspective, to which other layers, such as housing, could be added. Indeed, the existing economic fabric is not only about shopping streets and industrial areas.It follows residential patterns, creating areas in which economy, intended not only as services or facilities but also as industry and production, is mixed with housing. This creates a set of area types that are rarely defined or even considered by planners and politicians. Therefore, this second version was further elaborated, and we will explain the results in this paper (section 3 to 5). Finally, this last version gave us the possibility to translate it into possible market segments (section 6 and conclusion). First, this article will explain the concept of market segmentation, and make the link with types and policy questions. Secondly,we will presentan enhanced version of the economic typology based on what has beenpresented in our previous papers. The typology consistsof a set of defined economic areas. This term refers to areas with a specific economic fabric proximity, the predominant presence of an economic use (or a combination of uses) and similar environmental characteristics, such as for example accessibility and visibility. This can be used to define how economy is structured, spread and organised in an area, while subdividing the built up space that accommodates economic activities intoeconomic structures or clusters. We use the types to describe and compare different areas throughout Flanders and Brussels. The work isbased on data about economic activities collected in the field and not coming from existing databases. These databases are mostly conceived for uses that are not related to planning or policy preparations (Gruijthuijsen et al., 2018)and for that reason their use can give a misleading view on the economy. In this paper we present a revised and a tested method that is used to define the economic area types and their classification.At last, we will present our first attempt to translate the types into market segments. This illustrates the possible role the types can have in a policy making process, and it gives an idea on how it could be implemented in the future. We focus on both the potential for spatial transformation and future economic development and intensitification within each of these types

Defining Economic Typologies based on an Economic Activities Database

Economy and especially economic activities play a fundamental role in cities and in surrounding areas: it keeps the city functioning, in terms of jobs, goods and services. Considering this fundamental role of economy and economic activities and the vast amount of space it uses, it should be deeply studied and understood in order to guarantee a solid future to the sector itself and the cities. This paper respesents an attempt to research more in depth this matter: it tries to show how economy is organised and structured in a city and surrounding areas and how it can be analysed and considered by policy makers. The aim is to define economic types that represent the frame on which different types of spatial policies, ideally one for each economic location type, should be developed and implemented by policies makers. The project is based on a visual economic activities database. This contains information on all the visible economic units where people work or that are meant to be worked in. The study areas are the northern part of Brussels and two other areas located in Flanders (Belgium). Thanks to this visual inventory, we first tried to define a GIS methodology that defines and subdivides the economic fabric into different areas, according to the concentration of economic activities. This work is based on the hypothesis that the morphology of parcels with economic units on them is an indicator of location choice of companies. Secondly, we combined the database of economic activities with the data on economic fabric concentration in order to define and analyse different economic location types which represent potential economic locations for companies. In this paper we will explain the method used for the data elaboration and the difficulties encountered during the work. We will also discuss how this new economic location types could be used as an instrument in a planning or policy process to define the future perspective for a specific area

Assessing Expanding Space Use versus Infill for Economic Activities

In order to limit additional (net) land take for economic activities, the reality of space use needs to be properly understood since the location of economic activities and the patterns of space use vary in different environments. This was assessed by comparing the spatial patterns obtained from a field inventory with those from existing data for 5 case areas in Flanders (Belgium). Each case area is a transect from a high density urban area to a suburban neighbourhood or even a semi-rural zone, in different (types of) regions: inland-coastline transect, transects in the metropolitan areas of the major cities Antwerp and Ghent (exluding the city centres), in the medium sized city of Hasselt (and its suburbs) and th smaller city of Aalst (and the zone along an important access road), and transects incorporating small towns such as Deinze and Veurne . The observations in the field were made from what is visible from the street, thus representing what is normally perceived as economic activity. The statistics are based on official data, mostly derived from tax returns and social security contributions, and on commercial retail data. The location of economic activities and the patterns of space use vary in different settlement environments. The analysis then compared similar settlement environments in different regions, and identified typical characteristics for 8 location environments (with some further subcategories). These were presented to experts in workshops and (group) interviews. This revealed that, in some environments, (the combination of) data and statistics give a good understanding of the space use while, in other environments, gaps with realities in the field are obvious. Therefore, suggestions are made for targeted new data collection methods, such as remote sensing, crowd sourcing, and web data extraction

Methods for Regrouping Economic Activities into Meaningful Clusters

“The Flemish territory is characterized by a large urban sprawl […]. Even last years, an additional 6 hectares of undeveloped space is being built on daily. As a consequence open space is highly fragmented in Flanders“ (Pennincx, De Maeyer, Leroy, & De Mulder, 2021). As a strategic objective, the Flemish spatial government aims at a transition towards a net zero landtake daily by 2040. In this context, our spatial economy research group takes the choices and behaviour of individual companies and their use of space as a starting point. The main goal of the research is informing policy and supporting decision making by discerning spatial patterns, related to economic locations, and more precisely by focusing on the spatial environment of these locations. Over the years, we developed a the business-oriented approach for local spatial-economic policy and location advice for companies (Giaretta, Zaman, Pennincx, & De Mulder, 2019; Zaman, Pennincx, & De Mulder, 2020). For this, we need the exact location of the activity and the exact activity of every economic site. However,this information is difficult to gather from the only area-wide economic administrative database for the whole territory of Flanders (VKBO) (Gruijthuijsen et al., 2018). This area-covering database is used for major spatial-economic analyses, but it falls short in precision at the detail level needed for our work. We have carried out quite a lot of research in recent years to get to know the terrain situation by creating a field inventory. A key element of the research is the search for the right spatial synthesis of the data collected at the level of the parcel: through economic ecotopes and market segments we sought to combine the (economic) parcels into meaningful groups with similar characteristics. We described this step in previous papers (Giaretta et al., 2019; Zaman et al., 2020). Although the past research is interesting for the local policy makers of the mapped area’s, we still need to find a way to also make meaningful statements on spatial economic patterns for other areas in Flanders that have not been mapped. Producing this area-covering map for Flanders is rather important, as it will enable us to translate the analyses and the knowlegde we have gathered to (regional) policy. Although being thourough and rather precise, the visual inventory method has some drawbacks: it is time consuming and at this point, it cannot be easily applied to the entire area of Flanders. We therefore opt to first assess if we can extract useful statements regarding economic patterns from administrative databases. The main research question is whether the synthesis of the mapping data into the economic ecosystems or economic segments can be reproduced with the administrative database. Obviously, the results from the administrative database and the inventory will not be 100% alike. However, we believe it is possible come to spatial economic meaningful groups, even using the administrative database. The purpose of this grouping remains the same as with the inventory work and economic ecotopes and segments: being able to inform policy choices related to economic locations. In a first step, we examined whether and how the area synthesis (starting from the inventory and resulting into economic ecotopes and segments), that was carried out with manual work, field knowledge and expert opinion can be reproduced through automated methods, specifically through (1) statistical approach and/or machine learning and (2) a spatial predefined spatial clustering. The automated grouping results are reviewed and spatially analysed by spatial planners with territory knowledge. Only in a second step, when the grouping results on basis of the inventory are satisfying, we will rerun the method with the administrative data of the VKBO. In this paper we will discuss the first few steps of the grouping methods, in particular the distance and the activities clustering. We will outline the next steps, using the VKBO-data, assessing if we can come to meaningful economic clusters

Behavioural Studies in Spatial Planning

The main strategic planning policy in Flanders has in view to reduce the growth of the net settlement area. It is important to enthuse people to live closer together in the centres of villages, towns or in more dense urban areas to preserve open areas. The question is how to achieve this.In the last few years, there has been a growing awareness of the importance of convincing citizens to change their behaviour voluntarily. A crucial question is which behavioural change we can expect from the population, whether this can be met at once by everyone and what a government has to change or needs to provide to make the change possible. In recent years, the Flemish Planning Bureau for the Environment and Spatial Development commissioned several behavioural studies, conducted by Endeavour. It concerns two studies carried out on compact housing, on travel behaviour, on choice of residence in relation to facilities, and a new study on the behavioural influencers of housing. Thanks to an approach that combines the fields of architecture, urban planning, sociology and design Endeavour created a methodology of participation and co-creation with citizens, aiming at different segmentations of the population. This method successfully brought a multitude of experiences to the surface by means of a variety of people who sufficiently represent the diversity in Flanders. Insights into attitudes and motivations of a broad public are the key to understanding how to change certain aspects of behaviour into something more sustainable. This paper focuses on two themes. The first reflects on practices, tools and knowledge that are nowadays common in (regional) spatial planning and how they relate to human behaviour. The second introduces insights from qualitative research for behavioural change that focuses on how to approach different target groups within the population. The aim is to guide behaviour in function of an inclusive and sustainable spatial transition. As the paper shows, it is important that in this transition process people do not get the feeling of 'losing' something. such as comfort or choice

Analysis on financial consequences of spatial decisions: framework and case studies

A large number of policy decisions at both the spatial and other policy departments have an effect on the financial value of an individual property. In the current society, where the economic crisis and the debate about the position of the government have changed the conditions for the development of a spatial policy and the realization of real estate projects, arguing about the financial impact of spatial policy decisions is more than ever relevant. In 2014 the Flemish spatial development department initiated a research into the financial consequences of spatial decisions regarding private owners and into the actual performance of the existing financial compensation systems in Flanders. The study also defined and operationalized the concept of real estate value and made an in-depth analysis of thirteen different cases and their impact on the total real estate value within a defined time period. The cases used a wide variation of financial valuation techniques (comparative method, hedonic method, capitalization rental income, residual value method). The cases illustrated that the current compensation mechanisms are mainly focused on the ‘zoning’ of the properties. Changes to generic regulations or changes to the floor space of the property (e.g. limit/ increase number of floors) also have significant effects on the real estate value but are not captured within the actual regulations. Flanders intends to implement those new insights about financial impacts of spatial development and planning. Increasing the spatial efficiency and further exploring and harmonizing the compensation mechanisms in Flanders are two major challenges for the new Spatial Policy Plan of Flanders

Assessing Discrepancies between Official Economic Statistics and Land Use through a Field Inventory System

Abstract: To limit additional (net) land take for economic activities, the reality of space use needs to be properly understood. This was assessed by comparing the spatial patterns obtained from a field inventory with those from existing data for five case areas in Flanders (Belgium). Each case area is a transect from a high-density urban area to a suburban neighborhood or even a semi-rural zone. The statistics on these areas, based on official data, mostly derived from tax returns, social security contributions, and on commercial retail data, were checked with field observations. The location of economic activities and the patterns of space use vary in different settlement environments, resulting in the identification of typical characteristics for eight location environment types. While in, for example, core shopping centers a strong convergence can be noticed between existing statistics and the field inventory (71% of companies and 93% of parcels are detected on the field), in residential areas (21% of companies and 17% of parcels are detected on the field) the convergence is very limited. In other words, in some environments, (the combination of) data and statistics give a good understanding of the space use while, in other environments, gaps with realities in the field are obvious. Therefore, a field inventory system can enrich the picture and present another reality to complement both existing statistics and other land-use data methods such as remote sensing and web data extraction.status: publishe

Assessing Discrepancies between Official Economic Statistics and Land Use through a Field Inventory System

To limit additional (net) land take for economic activities, the reality of space use needs to be properly understood. This was assessed by comparing the spatial patterns obtained from a field inventory with those from existing data for five case areas in Flanders (Belgium). Each case area is a transect from a high-density urban area to a suburban neighborhood or even a semi-rural zone. The statistics on these areas, based on official data, mostly derived from tax returns, social security contributions, and on commercial retail data, were checked with field observations. The location of economic activities and the patterns of space use vary in different settlement environments, resulting in the identification of typical characteristics for eight location environment types. While in, for example, core shopping centers a strong convergence can be noticed between existing statistics and the field inventory (71% of companies and 93% of parcels are detected on the field), in residential areas (21% of companies and 17% of parcels are detected on the field) the convergence is very limited. In other words, in some environments, (the combination of) data and statistics give a good understanding of the space use while, in other environments, gaps with realities in the field are obvious. Therefore, a field inventory system can enrich the picture and present another reality to complement both existing statistics and other land-use data methods such as remote sensing and web data extraction