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Comparative urban performance assessment of safe cities through data envelopment analysis

2020; Elsevier BV; Volume: 13; Issue: 3 Linguagem: Inglês

10.1111/rsp3.12276

1757-7802

Karima Kourtit, Peter Nijkamp, Soushi Suzuki,

Fiscal Policy and Economic Growth

Sustainable urban development calls for a balanced package of conditions that induce a high quality of life (including safety and security) in cities. We argue that modern cities have to develop knowledge- based strategies for smart, safe and sound (3-S) city development, supported by urban performance assessment (UPA) as a framework for sustainable urban planning. The present study builds on these strategic notions and articulates the message that smart urban policy should look into input resources in relation to output performance. Particular attention will be paid to the constituents and the role of multiple safety indicators in urban performance analysis. Next, an appropriate method from the industrial management literature, namely, super-efficient data en591elopment analysis (SE-DEA) will be utilized in order to undertake a comparative performance assessment of safety conditions in 57 world cities, followed by a similar analysis for 14 large European cities. Both world-wide and Europe-wide, there appear to be significant differences in safety performance of cities, which means that there is much scope for strategic and effective safety policy in many cities. El desarrollo urbano sostenible exige un conjunto equilibrado de condiciones que puedan crear una alta calidad de vida (incluida la seguridad y la protección) en las ciudades. Se sostiene que las ciudades modernas tienen que desarrollar estrategias basadas en el conocimiento para un desarrollo de las ciudades que sea 3-S (sabio, seguro y sensato), apoyado por el uso de la evaluación del rendimiento urbano como un marco conceptual para la planificación urbana sostenible. El presente estudio se basa en estas nociones estratégicas y articula el mensaje de que una política urbana inteligente debe examinar los recursos de los insumos en relación con el rendimiento de los productos. Se prestó especial atención a los componentes y al papel de los múltiples indicadores de seguridad en el análisis del desempeño urbano. A continuación, se empleó un método apropiado obtenido de la literatura sobre gestión industrial, a saber, el análisis envolvente de datos supereficiente para realizar una evaluación comparativa de las condiciones de seguridad en 57 ciudades del mundo, seguido de un análisis similar para 14 grandes ciudades europeas. Tanto en todo el mundo como en toda Europa, parece haber diferencias significativas en el desempeño de las ciudades en materia de seguridad, lo que significa que hay un amplio margen en muchas ciudades para la introducción de políticas de seguridad estratégica y eficaz. 持続可能な都市開発には、高い生活の質 (安全及び治安を含む)を都市にもたらす、バランスのとれたいくつかの条件が必要である。持続可能な都市の計画のフレームワークとしての都市性能評価 (urban performance assessment: UPA)の支持の下、現代の都市は、スマートで、安全で、健全な (smart, safe and sound:3S)都市開発のための、知識に基づいた計画を立案しなければならない。本研究では、こうした計画的概念に基づき、スマートな都市政策はアウトプット性能に関連したインプット・リソースを調べるべきであるというメッセージを明確にする。都市のパフォーマンス分析における構成要素と複数の安全性指標の役割に特に注意を払う。次に、工業経営研究で用いられている適切な方法、すなわち、超効率データ包絡分析法 (super-efficient data envelopment analysis: SE-DEA)を用いて、世界の57の都市における安全条件の比較性能評価を行い、続いてヨーロッパの14の大都市を対象に同様の分析を行う。世界全体でも欧州全体でも、都市の安全性の性能には大きな違いがあるが、すなわち多くの都市で戦略的かつ効果的な安全政策の余地があることを意味する。 In his seminal work on Urban world history, Tellier (2009) offers a fascinating description of the socio-economic, demographic and political evolution of our world, seen through the lenses of city formation, settlement development and urbanization. The long-term history of our world is not only mirrored in, but also shaped by cities. In his view, "space is the main organizer and the great mold of socio-economic phenomena" (Tellier, 2009, p. 4). His arguments are in line with an earlier study by Diamond (1997, p. 57), who claimed: "History followed different courses for different peoples because of differences among peoples' environments, not because of biological differences among peoples themselves." Apparently, geographical location and spatial context matter for the socio-economic welfare pattern and performance profile of the world population. But is geography a constant factor over time? Or does it induce fluctuating patterns of urban decline and resurgence? Despite the relative robustness of geographical conditions over time, urban developments in different epochs of the history of mankind have shown a great diversity of (sometimes long-term, sometimes short-term) fluctuations which were certainly not a consequence of a given "fate of nature" or of fixed physical-geographical determinants. Such dynamic developments were inter alia caused by socio-economic competition among cities, by local and cultural circumstances in agglomerations, by intrinsic urban strengths or weaknesses, by shared social capital assets and by political stressors. This space–time evolution has led to the phenomenon of urban life cycles (see van den Berg, Burns, & Klaassen, 1987): cities and urban agglomerations exhibit a dynamic evolution which is co-determined by internal and external forces. In a European context, various patterns of urbanization and counter-urbanization can be observed, including also suburbanization and de-urbanization. For an interesting historical overview of time-varying urbanization trends in Europe since Roman times we refer to Heikkila and Kaskinoro (2009). Especially in the recently emerging new urban world (Kourtit, 2019), where a massive urbanization takes place (in particular, in Africa, Asia and Latin America), cities tend to become the socio-economic, cultural, technological and political escalators of shifting welfare distributions and socio-economic discrepancies all over the world (see also Glaeser, Kourtit, & Nijkamp, 2020). The fluctuating and challenging dynamics of cities calls for adequate and effective responses of urban policy-makers and stakeholders, so that the manifold urban challenges (e.g., poverty, quality of life, mobility, criminality, social stress) are properly and effectively coped with. Cities in decline or in a downturn need tailor-made and effective policy action in order to ensure urban resilience. Over the past decades, various buzzwords have come into being to describe the nature of such urban responses, for example, urban regeneration, urban renewal, urban gentrification, urban rehabilitation, urban reconstruction, or urban resurgence. These expressions which can be summarized under the general heading of urban redevelopment articulate one common message: the city deserves and needs to be protected, to be maintained, to be cherished, and to be actively managed, as it is The home of man (Ward, 1972), not only for residents, but also for business and visitors. Consequently, city redevelopment calls for appropriate and comprehensive urban survival and adjustment strategies in many domains, for example, housing, employment, infrastructure, public amenities, energy saving, environmental management, and safety and security. Urban redevelopment policy cannot be based on speculative insights, but needs to be anchored in empirical evidence. To provide urban policy-makers with solid information for good governance, access to reliable and up-to-date information is a sine qua non. This means that a systematic urban performance assessment (UPA) of city policy in light of pre-specified long-range goals for the city concerned is needed. UPA is a systematic assessment analysis framework of the achievements of a city based on quantitative indicators that reflect the relevant socio-economic, cultural or technological state of affairs—and developments therein—of a city, against the background of pre-specified objectives for the city concerned. Addressing then critical performance factors in urban (re)development policy is a necessary condition for any sustainable city to prevent a decline or to cope with a downturn. Sustainability conditions may relate to environmental quality, employment, a peaceful ambiance etc. Since safety and security are key factors in urban quality of life (and perceptions thereof), we will in the present study zoom in on urban safety and security as a critical success condition for sound urban development. The notions of safety and security are closely related. In general, safety reflects a situation of being protected or secured against unintended threats, harm or catastrophes, while security is a state in which people feel protected against liberate threats or adverse actions. In reality, these notions are often used interchangeably. In many cities world-wide a major severe concern is nowadays formed by the lack of safety (either empirically observed or subjectively perceived), a situation which erodes the residents' well- being in the "home of man." Lack of safety has in many cities even become a motive for residents to relocate, either within the city boundaries or to another place. Therefore, safety policy and management is a critical part of urban redevelopment or resurgence. Clearly, there are many statistical data on safety issues in most big cities (e.g., on policing, on crimes of various nature, or on security measures). But rich statistical databases that are consistently designed across multiple cities are more rare. This situation often prevents a solid and evidence-based comparative performance analysis of "safe cities." In the present study we will employ a systematic world-wide data base on safety conditions in 57 cities. We will employ these data for a comparative study of the background factors of urban safety using data envelopment analysis (DEA). Before doing so, we will first in the next section map out in slightly more detail the contours of present-day urban dynamics. The present study aims to provide an operational framework for a comparative assessment of urban safety factors in several large cities in the world using super-efficiency (SE) data envelopment analysis (DEA). The study is organized as follows. Section 2 will sketch the context for sustainable urban policy, with a particular view to the position of safety and security in urban development policy. Next, Section 3 is devoted to the methodology for comparative urban performance assessment in an urban performance assessment (UPA) context, in particular a super-efficiency (SE) data envelopment. Analysis (DEA), while also the international urban data base will be discussed. Section 4 presents the results from a world-wide safety comparison of 57 cities, followed by a similar analysis for 14 European Cities. A final section concludes. Sustainable cities need a smart governance, in particular since cities are usually densely populated agglomerations with many positive, but also negative externalities. Lack of safety, criminality, low environmental quality and feelings of alienation are not compatible with the "home of man." A necessary condition for sound urban development is about the care for the daily living environment, both physically and socially. It should be added here that smart cities are not a goal per se, but rather act as instruments for achieving higher liveability and sustainability goals. Such a generic strategy, based on knowledge- based action and data-driven urban planning, is also needed for implementing the UN Sustainable Development Goals (SDGs) and its related New Urban Agenda (NUA). And therefore, the development, storage, open access and public use of modern data systems (both official statistical sources and informal digital social media data) may become a promising opportunity for creating high- liveability urban spaces (comparable to the XXQ-city spaces advocated by Nijkamp, 2008). The assessment of attractive and safe urban spaces is however fraught with many difficulties of both a methodological and empirical nature. The above mentioned concept of UPA may be a useful instrument in tracing the conditions for and effects of policies for a 3-S city. In the present study, we will use an extensive systemic international data base on broad urban safety conditions which may be regarded as 'signposts' for a 3-S city. We will then use a comparative methodology that is particularly appropriate for a performance assessment of urban safety in different world cities, based on a so-called super-efficient (SE) data envelopment analysis (DEA) (see Section 3). It should finally be added that, safety and security are two complementary concepts and are increasingly seen as critical building blocks of an attractive urban environment. These concepts are not only linked to crime rates in cities, but also to a sound living environment in relation to public health, in particular in relation to public spaces in cities. Positive externalities created by a safe and sound urban space may be regarded as a major success factor for urban renewal and urban resurgence, and should therefore be a focus of any urban (re)development strategy. Low safety levels in urban areas are often correlated with urban deprivation and low urban prosperity levels. Therefore, it is a challenging effort to undertake an international comparative study of safety conditions in various global cities. The data base and methodology for this study are described in the next section. This study seeks to provide an evidence-based framework for comparing the safety and security performance of 57 large cities in the world. Performance measurement is an efficiency assessment tool from management and industrial organization. It serves to relate the necessary input for a production process to a set of pre-specified output variables. Hence, it is essentially a measurement tool for efficiency or productivity analysis. There is a wide variety of strategic performance measurement tools in both the public and the private sector (e.g., hospitals, schools, corporate organizations etc.; see e.g. de Waal & Karima, 2013). A frequently used—and nowadays rather popular—quantitative assessment tool is data envelopment analysis (DEA). The use of DEA as an evaluation tool of business performance already has a long history. It is essentially based on the principles of multi-objective linear programming theory. It seeks to identify efficient agencies (decision making units or DMUs) and to separate efficient and non-efficient DMUs, given the basic proposition that all efficient DMUs are by definition located on an efficiency frontier. The limited publication space does not permit us to offer here a broad exposition on the foundations and mathematics of DEA, but for an overview of the theory and application domains of DEA we refer to Suzuki and Nijkamp (2017a, 2017b), (Suzuki, Kourtit, & Nijkamp, 2017). A major well-known weakness of a conventional DEA is that it does not allow us to make a distinction between efficient DMUs. All efficient DMUs are equally efficient and have a score 1.0. This is in practice often seen as a restricted approach, as there may be different degrees of efficiency, in way similar to the fact that there are different degrees of inefficiency. To cope with these feeble features of DEA, the concept of so-called super-efficiency (SE) has been introduced (see Andersen & Petersen, 1993). An SE-DEA is able to provide an unambiguous ranking—and even rating—of the performance of all DMUs under consideration. We will apply in our study the SE-DEA approach for a comparison of safety conditions in cities. The basic premise in the current study is that cities—as DMUs—may be regarded as dynamic organisms which may go through several fluctuations. To cope with the need for redevelopment (including resurgence) a sound 3-S city strategy is needed, in which urban safety and security play a critical role. The main challenge of the present study is to compare the safety achievements of cities using SE-DEA as an analytical tool for a UPA of safe cities. This means that multiple indicators for urban safety and security have to be identified, while for each class of safety and security indicators a distinction has to be made between input and output variables, following the logic of DEA. In our study we will use an extensive data base on safe cities, collected by The Economist Intelligence Unit (2017). This Safe City Index 2017 report addresses security in an urbanizing world and ranks many cities from all over the world across 49 indicators covering four major domains: (i) digital security; (ii) health security; (iii) infrastructure security; and (iv) personal security. Each of these four domains comprises between 3 and 12 sub-indicators which are divided between inputs (such as policy measures and access to services) and outputs (such as air quality and crime). These will now briefly be described.Digital security refers to free access to internet and digital services without fear of privacy violations or identity theft. Input indicators contain, in particular, awareness of digital threats, access to digital technology and provision of dedicated cyber security services. Output indicators refer inter alia to frequency of identity theft or share of computers infected with a virus. Health security refers to quality of natural environment in the city and the quality of health care services. Input factors relate inter alia to environmental quality and access to and quality of health care services in the city, while output variables may comprise air and water quality, life expectancy and child mortality. A specific indicator comprising the number of chemical, biological or radiological attacks on the city was also included in order to take into consideration the impact of terrorism. Infrastructure security addresses the built environment, in particular city infrastructure and its vulnerability to natural disasters and terrorist attacks. As input indicators the study takes inter alia infrastructure quality and transport safety regulations, while as output indicators vehicular accidents and pedestrian deaths are selected, as well as terrorist attacks on facilities and infrastructure.Personal security measures the risk exposure of citizens to crime, violence or other man-made threats. On the input side, police engagement, data-driven crime prevention and political stability are considered, while on the output side inter alia prevalence of petty and violent crime, perception of safety, and threat of civil unrest, military conflict and terrorism are taken into consideration. All these 49 sub-indicators were collected for all 57 world cities under consideration. The data base has attempted to quantify the sub-indicators to the maximum extent possible. The origin of the data base came from public information, official sources where applicable, primary sources and best guesses. All data were at the end whenever possible normalized on a quantitative scale of 0–100. This comprehensive data set on 57 world cities forms an ideal quantitative set of input and output indicators for the application of a SE-DEA. The framework of analysis in our comparative study is sketched out in Figure 1. To comply with some general rules of thumb on the aggregation and the number of input indicators and the number of output indicators in relation to the total set of DMUs (see Dyson et al., 2001), we had to adjust the data structure in order to let it fit in a regular SE-DEA approach (see Tables 1–4). Input and output indicators were next transformed into the standard rule: "the higher the better" (see Ali & Seiford, 1993). The 57 cities in our comparative experiment are listed in Table 5. The comprehensive empirical findings from the application of our SE-DEA model to the four main safety categories (and all sub-categories) are presented in Figure 2. These results show an interesting pattern. First, there is a gradually declining safety profile for all 57 cities in our study. Second, there is quite some variability in the safety performance scores for each of the four main categories of safety across cities. And third, to a large extent these four safety categories follow the same declining trend and are clearly correlated. Finally, it turns out that large cities in the rich developed world (e.g. Singapore, Amsterdam, Osaka, Tokyo, Toronto, Melbourne, etc.) show the highest safety performance, scores, while large cities in the less developed word (e.g., Karachi, Jakarta, Ho Chi Minh City, Quito, Caracas, Cairo, etc.) have the lowest score on the safety performance ladder. This suggests that levels of economic welfare and safety performance are positively correlated. This finding has clear implications for urban safety and security policy. It may also be an interesting additional experiment to analyse separately the safety performance for the large European cities in our sample. There are 14 European cities in the data base. This number offers enough degrees of freedom for a DEA application. The use of the SE-DEA model (see Figure 3) leads to a ranking that is largely similar to the world ranking of cities. But two observations are to be made here. The efficiency scores for the performance for safety indicators are not necessarily identical to those at a world-wide level. The number of competing firms has implications for the relative scores. And second, the ranking of the 14 European cities is not entirely the same as in the world ranking. Especially as the position of Frankfurt vs. Zurich and Paris vs. London has changed. This is a well-known phenomenon in the discrete choice literature, known as independence of irrelevant alternatives (IIA). But all in all, the European results largely in correspond to the world safety profiles of cities, so that the conclusions on the ranking patterns of world cities also hold here. In the modern age of high spatial mobility and super-connectivity, the profile of cities is subject to rapid change. Simple indicators (like income or employment) no longer represent the real (or perceived) welfare patterns and feelings of well-being of urban residents. Urban development and resurgence policy have become a much more complicated challenge for public actors and stakeholders of cities. In the contemporaneous urban context, quality of life—including safety and security, but also human health and ecological quality—is becoming a critical variable. Our study may be seen as a quantitative contribution to a comparative assessment of urban safety performance across many cities all over the world. Our findings show clearly significant differences in the safety performance of cities, which are not only related to physical or social geography, but also to economic welfare positions. It goes without saying that both world-wide and Europe-wide, there is much scope for an improvement of the safety performance of cities. For example, we can clearly identify strong and weak safety points in each safety category for each of the 14 European cities based on Figure 3. This more detailed strength-weakness analysis is shown in Table 6. This Table provides color mapping results for each of the 4 efficiency score categories (above 1.0 is blue, between 0.8 to 1.0 is yellow and below 0.8 is orange). From Table 6, we can easily derive that relatively Amsterdam does not have any weak safety point (all blue); Madrid has a strong performance in infrastructure and personal security, but digital security is relatively a weak point, so that this issue calls for improvement in Madrid. All cities can be judged from the same policy perspective. These kinds of informed statistical conclusions may be useful for policy and practice regarding safety improvement in each city.