This is a foundational text in urban geography, now updated to include the essay 'The Right to the City'. Throughout his distinguished and influential career, David Harvey has defined and redefined the relationship among politics, capitalism, and the social aspects of geographical theory. Laying out Harvey's position that geography could not remain objective in the face of urban poverty and associated ills, "Social Justice and the City" is perhaps the most widely cited work in the field. Harvey analyzes core issues in city planning and policy - employment and housing location, zoning, transport costs, concentrations of poverty - asking in each case about the relationship between social justice and space. How, for example, do built-in assumptions about planning reinforce existing distributions of income? Rather than leading him to liberal, technocratic solutions, Harvey's line of inquiry pushes him in the direction of a 'revolutionary geography', one that transcends the structural limitations of existing approaches to space. Harvey's emphasis on rigorous thought and theoretical innovation gives the volume an enduring appeal. This is a book that raises big questions, and for that reason geographers and other social scientists regularly return to it.
Recent advances in artificial intelligence have produced systems capable of remarkable performance across a wide range of tasks. These gains, however, are increasingly accompanied by concerns regarding long-horizon developmental behavior, as many systems converge toward repetitive solution patterns rather than sustained growth. We argue that a central limitation of contemporary AI systems lies not in capability per se, but in the premature fixation of their performance frontier. To address this issue, we introduce the concept of a \emph{Dynamic Intelligence Ceiling} (DIC), defined as the highest level of effective intelligence attainable by a system at a given time under its current resources, internal intent, and structural configuration. To make this notion empirically tractable, we propose a trajectory-centric evaluation framework that measures intelligence as a moving frontier rather than a static snapshot. We operationalize DIC using two estimators: the \emph{Progressive Difficulty Ceiling} (PDC), which captures the maximal reliably solvable difficulty under constrained resources, and the \emph{Ceiling Drift Rate} (CDR), which quantifies the temporal evolution of this frontier. These estimators are instantiated through a procedurally generated benchmark that jointly evaluates long-horizon planning and structural creativity within a single controlled environment. Our results reveal a qualitative distinction between systems that deepen exploitation within a fixed solution manifold and those that sustain frontier expansion over time. Importantly, our framework does not posit unbounded intelligence, but reframes limits as dynamic and trajectory-dependent rather than static and prematurely fixed. \vspace{0.5em} \noindent\textbf{Keywords:} AI evaluation, planning and creativity, developmental intelligence, dynamic intelligence ceilings, complex adaptive systems
Ghina Amalia, Budi Heri Pirngadi, M. Zaenal Ramdhani A. Siddiq
Water needs in industrial areas can be met by using surface water and groundwater sources. However, this can cause its own problems for the region due to increased water demand. The area of the industrial area in Sumberjaya Sub-district which reaches 329,700 m² will require water for industry amounting to 8,534,678 m3/ year, with no direction in sustainable groundwater management it is feared that it will have an impact on the loss of groundwater resources and the occurrence of a crisis or scarcity of water resources. This research aims to find out directions in sustainable groundwater management that are appropriate, integrated, and can be applied to supporting industrial estates in Sumberjaya District, Majalengka Regency. This analysis was conducted using descriptive qualitative and descriptive quantitative approaches. We conducted this analysis using observation, interview, and secondary data collection methods. The analysis method in this research is the calculation method of rainwater harvesting (PAH) and the calculation of the recharge concept. From the results of the analysis of the potential and problems that exist in Sumberjaya Subdistrict, in helping to fulfil industrial water needs other than groundwater, can apply concept direction in groundwater conservation using the Rainwater Harvesting calculation method and recharge wells.
Corridor development refers to the strategic planning, building, and operation of transportation infrastructure that connects key metropolitan areas within a city. This study aims to assess the effects of corridor development initiatives in Addis Ababa. Data were collected through on-site observations and interviews with key informants from relevant offices, alongside secondary data. Thematic analysis was employed to interpret the data. The study’s findings indicate that corridor development initiatives have decreased traffic congestion, enhanced pedestrian and bicycle access, and improved mobility, making commuting more enjoyable and efficient. Furthermore, it promotes sustainability through improving green spaces, open public areas, and non-motorized transportation infrastructure. Overall, the study found that the corridor development project has significantly boosted the city’s image. Finally, the study recommends using Addis Ababa’s corridor development as a model for urban planning and financial investment in transportation infrastructure, which can enhance the city’s quality of life. Besides, to address the city’s mobility challenges and promote a smart city, the study advocates for implementing integrated transit systems, vehicle sharing, traffic calming measures, and parking fees as part of the city’s ongoing development efforts.
Muhammad Abdul Mubdi Bindar, Muhammad Zainal Ibad, Goldie Melinda Wijayanti
et al.
This paper studies the commuting patterns of students and staff at the Sumatera Institute of Technology (ITERA), a rapidly growing university located in a peri-urban area of Lampung Province, Indonesia. The research is grounded in the understanding that peri-urban commuters face unique mobility challenges shaped by transitional land use, limited infrastructure, and high motorcycle dependency. Using both statistical and spatial analyses, the article analyzed distinct travel behaviors and their socioeconomic determinants. Findings reveal that motorcycles dominate as the primary commuting mode for both groups, driven by cultural norms and constrained public transport access. Staff exhibit higher rates of vehicle ownership and longer, more dispersed commutes, while students tend to reside closer to campus and rely on borrowed motorcycles. Temporal analysis shows structured weekday travel among staff and more flexible, weekend-active patterns among students. The findings offer targeted insights for developing sustainable transportation strategies in rapidly expanding peri-urban institutions—such as promoting bicycle and pedestrian infrastructure, designing transport policies that account for widespread motorcycle borrowing among students, and differentiating mobility interventions based on the spatial dispersion and financial profiles of staff versus students.
Rantika Ariandini, Reswita Reswita, Ketut Sukiyono
et al.
This study examines raw material inventory control in micro, small, and medium-scale fish milling businesses in Bengkulu City, focusing on mackerel as the primary raw material. Mackerel is highly perishable and affected by seasonal and weather factors, requiring careful supply planning. The research location was selected purposively based on Law Number 20 of 2008. The study aims to determine optimal inventory control using the EOQ method, as well as safety stock and reorder points (ROP). The results show that the optimal order quantity per period is 894 kg for micro-scale, 1,617 kg for small-scale, and 1,870 kg for medium-scale businesses. Additionally, the total inventory cost using the EOQ method is lower than the company's existing approach, making it a more efficient option for managing raw material supplies.
The integration of digital technologies into urban planning has given rise to "smart cities," aiming to enhance quality of life and operational efficiency. However, the implementation of such technologies introduces ethical challenges, including data privacy, equity, inclusion, and transparency. This article employs the Beard and Longstaff framework to discuss these challenges through a combination of theoretical analysis and case studies. Focusing on principles of self-determination, fairness, accessibility, and purpose, the study examines governance models, stakeholder roles, and ethical dilemmas inherent in smart city initiatives. Recommendations include adopting regulatory sandboxes, fostering participatory governance, and bridging digital divides to ensure that smart cities align with societal values, promoting inclusivity and ethical urban development.
The existing body of research into the environmental and socio-economic benefits of green infrastructure supports the case for it to be positioned as a form of critical infrastructure, particularly in urban settings. It is broadly recognized that extreme weather and climate change pose significant risks to critical infrastructure systems linked to the provision of services, including electricity, water, communications, and transport, and consequently risk assessments and associated adaptation strategies are common practice. However, although green infrastructure is also at risk from extreme weather and climate change, threatening the realization of benefits that it can deliver in urban settings, associated risks to green infrastructure are not widely understood or assessed in practice. This paper discusses the status of existing research on this topic and uses this as a foundation for a Greater Manchester (UK) case study that assesses the risk of low water availability to grassed areas, which represent a key element of the city-region’s green infrastructure. In doing so, the paper demonstrates how risks linked to extreme weather and climate change can be assessed spatially to inform green infrastructure planning. In summary, this paper aims to raise awareness of extreme weather and climate change risk to urban green infrastructure, present an empirical case study and associated methodological approach on this topic, and ultimately support efforts to enhance the resilience of urban green infrastructure to extreme weather and climate change.
Matteo Bruno, Hygor Piaget Monteiro Melo, Bruno Campanelli
et al.
Proximity-based cities have attracted much attention in recent years. The 15-minute city, in particular, heralded a new vision for cities where essential services must be easily accessible. Despite its undoubted merit in stimulating discussion on new organisations of cities, the 15-minute city cannot be applicable everywhere, and its very definition raises a few concerns. Here, we tackle the feasibility and practicability of the '15-minute city' model in many cities worldwide. We provide a worldwide quantification of how close cities are to the ideal of the 15-minute city. To this end, we measure the accessibility times to resources and services, and we reveal strong heterogeneity of accessibility within and across cities, with a significant role played by local population densities. We provide an online platform (\href{whatif.sonycsl.it/15mincity}{whatif.sonycsl.it/15mincity}) to access and visualise accessibility scores for virtually all cities worldwide. The heterogeneity of accessibility within cities is one of the sources of inequality. We thus simulate how much a better redistribution of resources and services could heal inequity by keeping the same resources and services or by allowing for virtually infinite resources. We highlight pronounced discrepancies among cities in the minimum number of additional services needed to comply with the 15-minute city concept. We conclude that the proximity-based paradigm must be generalised to work on a wide range of local population densities. Finally, socio-economic and cultural factors should be included to shift from time-based to value-based cities.
This study investigates city dynamics employing a nonextensive diffusion equation suited for addressing diffusion within a fractal medium, where the nonadditive parameter, $q$, plays a relevant role. The findings demonstrate the efficacy of this approach in determining the relation between the fractal dimension of the city, the allometric exponent and $q$, and elucidating the stationary phase of urban evolution. The dynamic methodology facilitates the correlation of the fractal dimension with both the entropic index and the urban scaling exponent identified in data analyses. The results reveal that the scaling behaviour observed in cities aligns with the fractal dimension measured through independent methods. Moreover, the interpretation of these findings underscores the intimate connection between the fractal dimension and social interactions within the urban context. This research contributes to a deeper comprehension of the intricate interplay between human behaviour, urban dynamics, and the underlying fractal nature of cities.
Temperature sensors are widely used in industrial production and scientific research, and accurate temperature measurement is crucial for ensuring the quality and safety of production processes. To improve the accuracy and stability of temperature sensors, this paper proposed using an artificial neural network (ANN) model for calibration and explored the feasibility and effectiveness of using ANNs to calibrate temperature sensors. The experiment collected multiple sets of temperature data from standard temperature sensors in different environments and compared the calibration results of the ANN model, linear regression, and polynomial regression. The experimental results show that calibration using the ANN improved the accuracy of the temperature sensors. Compared with traditional linear regression and polynomial regression, the ANN model produced more accurate calibration. However, overfitting may occur due to a small sample size or a large amount of noise. Therefore, the key to improving calibration using the ANN model is to design reasonable training samples and adjust the model parameters. The results of this study are important for practical applications and provide reliable technical support for industrial production and scientific research.
Md Abu Sayed, Md Maksudur Rahman, Moinul Islam Zaber
et al.
Analysis of traffic pattern recognition and traffic congestion expansion in real time are one of the exciting and challenging tasks which help the government to build a robust and sustainable traffic management system specially in a densely populated city like Dhaka. In this paper, we analyze the traffic intensity for small areas which are also known as junction points or corridors. We describe Dhaka city traffic expansion from a congestion point by using gravity model. However, we process real-time traffic data of Dhaka city rather than depend on survey and interview. We exactly show that traffic expansion of Dhaka city exactly follows gravity model. Expansion of traffic from a congestion point spreads out rapidly to its neighbor and impact of congested point decreases as the distance increases from that congested point. This analysis will help the government making a planned urbanized Dhaka city in order to reduce traffic jam.
Tomoya Mori, Takashi Akamatsu, Yuki Takayama
et al.
City-size distributions follow an approximate power law in various countries despite high volatility in relative city sizes over time. Our empirical evidence for the United States and Japan indicates that the scaling law stems from a spatial fractal structure owing to the coordination of industrial locations. While the locations of individual industries change considerably over time, there is a persistent pattern in that more localized industries at a given time are found only in larger cities. The spatial organization of cities exhibits a hierarchical structure in which larger cities are spaced apart to serve as centers for surrounding smaller cities, generating a recursive pattern across different spatial scales. In our theoretical replication of the observed regularities, diversity in scale economy among industries induces diversity in their location pattern, which translates into diversity in city size via spatial coordination of industries and population. The city-size power law is a generic feature of Monte-Carlo samples of stationary states resulting from the spontaneous spatial fractal structure in the hypothetical economy. The identified regularities reveal constraints on feasible urban planning at each regional scale. The success or failure of place-based policies designed to take advantage of individual cities' characteristics should depend on their spatial relationships with other cities, subject to the nationwide spatial fractal structure.
A lack of financial access, which is often an issue in many central-city U.S. neighborhoods, can be linked to higher interest rates as well as negative health and psychological outcomes. A number of analyses of "banking deserts" have also found these areas to be poorer and less White than other parts of the city. While previous research has examined specific cities, or has classified areas by population densities, no study to date has examined a large set of individual cities. This study looks at 319 U.S. cities with populations greater than 100,000 and isolates areas with fewer than 0.318 banks per square mile based on distances from block-group centroids. The relative shares of these "deserts" appears to be independent of city population across the sample, and there is little relationship between these shares and socioeconomic variables such as the poverty rate or the percentage of Black residents. One plausible explanation is that only a subset of many cities' poorest, least White block groups can be classified as banking deserts; nearby block groups with similar socioeconomic characteristics are therefore non-deserts. Outside of the Northeast, non-desert areas tend to be poorer than deserts, suggesting that income- and bank-poor neighborhoods might not be as prevalent as is commonly assumed.
Omar Cheikhrouhou, Ichrak Amdouni, Khaleel Mershad
et al.
Cybersecurity is an inherent characteristic that should be addressed before the large deployment of smart city applications. Recently, Blockchain appears as a promising technology to provide several cybersecurity aspects of smart city applications. This paper provides a comprehensive review of the existing blockchain-based solutions for the cybersecurity of the main smart city applications, namely smart healthcare, smart transportation, smart agriculture, supply chain management, smart grid, and smart homes. We describe the existing solutions and we discuss their merits and limits. Moreover, we define the security requirements of each smart city application and we give a mapping of the studied solutions to these defined requirements. Additionally, future directions are given. We believe that the present survey is a good starting point for every researcher in the fields of cybersecurity, blockchain, and smart cities.
Athanasios Batakis, Thi-Thuy-Nga Nguyen, Michel Zinsmeister
In the first part of this paper we propose a new theoretical model of city growth based on percolation. The second half oh the paper is devoted to a concrete application of the model, namely to the city of Montargis. It appears that the embedded algorithm is quite efficient in terms of computational time and allows to exploit big data type ressources such as individual land lots.
Introduction: Current data on changes in contraceptive use is required to prevent unintended pregnancies. This study aims to study trends, and patterns of contraceptive use from 2000 to 2014.
Methods: This survey research uses data from three fertility surveys conducted in 2000, 2009, and 2014 respectively among representative samples of 1396, 2,934 and 3012 married women aged 15-49 living in the capital city of Tehran. The prevalence of contraceptive methods was estimated according to women’s socio-economic and demographic characteristics.
Results: In 2014, 82% of women used contraceptive methods (48% modern methods, and 34% traditional methods). Withdrawal (33%) and condoms (21%) had the highest prevalence, compared with female sterilization (9%), IUD (7%) and Pills (7%). Over 2000-2014, the use of withdrawal and condoms increased by 69% and 20%, respectively. In contrast, the prevalence of female sterilization, pills, male sterilization, and IUD decreased respectively by 20%, 42%, 45% and 51% over the same period. The use of withdrawal method increased with women’s age. Withdrawal and condom were used by 67% of contraceptive users, who mostly hold higher education levels, were employed, and lived in upper residential districts. In contrast, users of modern methods, namely sterilizations, IUD and pills, mostly hold lower levels of education and income, were unemployed, lived in the lower residential districts, and had two or more children.
Conclusion: The increasing prevalence of withdrawal and condom signifies a need for provision of effective family planning education and counseling to prevent unintended pregnancy.