Methodological framework for shrinking cities case study research: northwest region of Bosnia and Herzegovina

Urban shrinkage is an increasingly global phenomenon that equally affects large cities and small towns, as a result of complex social, economic and spatial changes, thereby causing the emergence of the so-called shrinking cities. This paper presents a model for the analysis of shrinking cities tailored to the needs of research in circumstances of insufficiently developed statistical systems for monitoring the complex structure of changes affecting cities. The model is based on an analysis of international research projects focused on this research problem, analysis of the legislative framework in Bosnia and Herzegovina (B&H) and analysis of available data. The proposed model is tested on the territory of northwest B&H (Republic of Srpska - RS) and aims at mapping, analysis and typological classification of shrinking cities. It is assumed that the process of urban shrinkage is more prevalent than that of urban growth, and that most of the cities and towns in northwest B&H (RS) are faced with this problem, which is considerably more acute when it comes to small and medium-sized towns of this region

Defining methodology of integrative adaptive management for shrinking cities - case study Prijedor

Узајамно деловање негативних друштвених и економских фактора на глобалном нивоу, крајем XX и почетком XXI века, резултовало је поларизацијом просторног развоја која је произвела глобалне градове који су успели да се интегришу у глобалну мрежу, али је довела и до раста неједнакости међу градовима и појаве глобалног феномена г ра д ов а у опа д ању (shrinking cities)...The interaction of negative social and economic factors at the global level, in the late twentieth and early twenty-first century, resulted in the polarisation of spatial development that produced the global cities which managed to integrate into the global network, but also led to an increase in disparities between cities and the emergence of global phenomenon of sh r in k i ng c i t i e s . Research shows that the phenomenon of urban stagnation is increasingly widespread in Central and Eastern Europe, the United States, developed countries in Asia, North America and Australia..

Urban shrinkage in a 'shrinking' serbia - the approach to a global phenomenon in a local context

The initial purpose of this research was to understand the basic patterns of urban shrinkage in Serbia. Urban shrinkage, a common phenomenon in post-socialist countries, is a novelty, albeit very present in Serbia today. Despite presenting a huge challenge for the future of the country, it has not been studied sufficiently at any level. To understand this situation, the first "task" would certainly be to identify which cities in Serbia can be considered as shrinking in a local context. The research will focus on this issue through the development of four models of shrinking cities in Serbia according to globally based factors of urban shrinkage. The aim of the paper is to clarify the potential of their use and to explore the possible locally-based factors of urban shrinkage

Urban Design Competition and Megaprojects in a Context of Identity of Cultural Heritage: Case Study Belgrade`s Riverfronts

Industrial heritage sites that have lost their original function represent significant and valuable cultural heritage which is a part of the urban memory and material evidence of the past, with whose decay a city is losing a part of its history. This paper deals with the problem of preserving identity of industrial heritage sites in the process of sustainable urban regeneration. More precisely, paper deals with the problem of preserving tangible as well as intangible attributes of industrial heritage identity within a context of contemporary projects of urban regeneration. The significance of this problem lies within the contradiction between the industrial heritage sites as places intended for new urban functions in accordance with strategies of contemporary urban development and the industrial heritage sites as places with the inherited spatial, functional and cultural values important to the community. Therefore, the main planning task of sustainable regeneration is finding the balance between preservation and change of industrial heritage sites. In planning practice these changes are most often driven by economic and environmental interests, while cultural values as active components of material culture and urban memory of citizens are neglected. On the other hand, this problem arises from the ambiguous notion of industrial heritage identity, as well as from the lack of appropriate planning tools for identifying and analysing identity attributes that are valuable to preserve. The aim of this paper is to draw attention to the complexity of identity of industrial heritage stemming from the various tangible as well as intangible values of cultural heritage. The paper advocates for using the concept of place as a framework for identifying and investigating identity attributes of industrial heritage sites which contribute to decision making in the initial phase of planning process. The planning treatment of industrial heritage identity in the process of riverfronts regeneration is analysed on the example of urban design competitions and megaprojects in Belgrade. The riverfront regeneration has been initiated through a series of urban plans, projects and design competitions. Some of these plans are located within two planned urban megaprojects: “Danube port” and “Belgrade on water”. Analysis show that the imbalance between the ambitions of the city (authorities, professional associations) and current economic capabilities (overscale, expensive, not considering implementation in phases) is one of the main problems for the implementation of the plans and projects

Comparative Analysis of Classic Clustering Algorithms and Girvan-Newman Algorithm for Finding Communities in Social Networks

Nowadays finding patterns in large social network datasets is a growing challenge and an important subject of interest. One of current problems in this field is identifying clusters within social networks with large number of nodes. Social network clusters are not necessarily disjoint sets; rather they may overlap and have common nodes, in which case it is more appropriate to designate them as communities. Although many clustering algorithms handle small datasets well, they are usually extremely inefficient on large datasets. This paper shows comparative analysis of frequently used classic graph clustering algorithms and well-known Girvan-Newman algorithm that is used for identification of communities in graphs, which is especially optimized for large datasets. The goal of the paper is to show which of the algorithms give best performances on given dataset. The paper presents real problem of data clustering, algorithms that can be used for its solution, methodology of analysis, results that were achieved and conclusions that were derived. This work is licensed under a&nbsp;Creative Commons Attribution-NonCommercial 4.0 International License.</p

Comparative Analysis of Classic Clustering Algorithms and Girvan-Newman Algorithm for Finding Communities in Social Networks

Nowadays finding patterns in large social network datasets is a growing challenge and an important subject of interest. One of current problems in this field is identifying clusters within social networks with large number of nodes. Social network clusters are not necessarily disjoint sets; rather they may overlap and have common nodes, in which case it is more appropriate to designate them as communities. Although many clustering algorithms handle small datasets well, they are usually extremely inefficient on large datasets. This paper shows comparative analysis of frequently used classic graph clustering algorithms and well-known Girvan-Newman algorithm that is used for identification of communities in graphs, which is especially optimized for large datasets. The goal of the paper is to show which of the algorithms give best performances on given dataset. The paper presents real problem of data clustering, algorithms that can be used for its solution, methodology of analysis, results that were achieved and conclusions that were derived. This work is licensed under a&nbsp;Creative Commons Attribution-NonCommercial 4.0 International License.</p

Software tool for testing structured regression algorithms based on GCRF model

Predmet istraživanja ovog rada su modeli strukturne regresije, koji su dizajnirani da koriste veze između objekata prilikom predviđanja izlaznih vrijednosti. Drugim riječima, modeli strukturne regresije razmatraju atribute objekata i veze između objekata kako bi dali što tačnije predviđanje. Gaussian Conditional Random Fields (GCRF) model je jedan od najčešće korišćenih modela strukturne regresije koji integriše predikciju tradicionalnih modela nadgledanog učenja (nestrukturnih prediktora) i vezu između objekata u cilju tačnije predikcije. Glavna pretpostavka ovog modela je da su dva objekta koja su usko povezana veoma slični jedan drugom i samim tim vrijednosti njihovih izlaznih varijabli treba da budu slični. Sličnost između objekata u GCRF modelu mora da bude simetrična, ali u velikom broju realnih primjera objekti su nesimetrično povezani. U radu je predstavljeno proširenje GCRF modela koje uzima u obzir asimetričnu sličnost između objekata (nazvan usmjereni GCRF - Directed GCRF). Na sintetičkim i realnim setovima podataka pokazano je da novi model daje tačnije predviđanje od standardnog GCRF modela i tradicionalnih nestrukturnih prediktora. Rad obuhvata i razvoj softverskog alata otvorenog koda koji integriše različite vrste GCRF modela i omogućava treniranje i testiranje tih modela na različitim setovima podataka, preko grafičkog korisničkog interfejsa. Izvršena je evaluacija alata sa korisnicima različitih profila i različitog znanja iz oblasti mašinskog učenja. Rezultati su potvrdili da je alat je intuitivan i lak za korišćenje kako za eksperte, tako i za početnike i istraživače iz različitih domena kojima GCRF model može pomoći da dođu do željenih informacija.The subject of this dissertation are structured regression models that are designed to use relationships between objects for predicting output variables. In other words, structured regression models consider the attributes of objects and dependencies between objects to make predictions as accurately as possible. Gaussian Conditional Random Fields (GCRF) model is commonly used structured regression model that incorporates the outputs of traditional supervised learning models (unstructured predictors) and the correlation between output variables in order to achieve a higher prediction accuracy. A main assumption in the GCRF model is that if two objects are closely related, they should be more similar to each other and they should have similar values of the output variable. The similarity considered in GCRF is symmetric. However, in many real-world examples objects are asymmetrically linked. This dissertation presents extension of GCRF model that considers asymmetric similarities between objects (called Directed GCRF). The effectiveness of new model is characterized on synthetic datasets and real-world datasets, on which it was more accurate than the standard GCRF model and baseline unstructured predictors. This dissertation also presents development of an open-source software tool that integrates various GCRF methods and supports training and testing of those methods on different datasets using graphical user interface. The tool was evaluated with users with different level of knowledge in the machine learning field. Evaluation results confirmed that this tool is intuitive and easy to use for experts, as well as for beginners and researchers from different domains that can use GCRF for data prediction

Značaj valorizacije i reaktivacije industrijskog nasljeđa XX vijeka za kulturni identitet Republike Srpske

Polazeći od problema neprepoznavanja industrijske baštine XX vijeka kao bitnog dijela kulturnog nasljeđa i kulturnog identiteta zajednice, rad ukazuje da ovaj značajan razvojni potencijal Republike Srpske, u njenom zakonodavnom i institucionalnom okviru nije dovoljno zastupljen. Ističu se vrijednosti industrijskog nasljeđa prošlog vijeka, s fokusom na materijalnim svjedočanstvima procesa industrijalizacije i urbanizacije u periodu SFRJ. Insistira se na pokretanju procesa identifikacije i valorizacije nasljeđa industrijske prošlosti, u funkciji njegove zaštite i reaktivacijesa ciljem jačanja regionalnog kulturnog identiteta

Promena uloge demografskog opadanja u istraživanju urbanog opadanja

Demographic decline is traditionally considered as the most prominent indicator of the globally presented phenomenon of urban shrinkage. However, in-depth research of this phenomenon implies that the demographic indicators cannot be simply positioned as consequences. Some of them are more the causes of urban shrinkage and the third ones have the features of both of them. The aim of this paper is to present the current knowledge regarding the role of demographic decline in the phenomenon of urban shrinkage. In accordance to the aim, the paper results with the better determination and categorisation of the main demographic indicators in this currently widespread phenomenon.Demografsko opadanje se obično smatra najvažnijim pokazateljem urbanog opadanja, pojave prisutne širom sveta. Ipak, podrobnije istraživanje pojave posredno govori da demografski pokazatelji ne mogu jednostavno biti svrstani među njene posledice. Neki od njih su više uzroci, dok neki imaju odlike i jednog i drugog. Cilj ovog rada je da predstavi trenutna saznanja o ulozi demografskog opadanja u pojavi urbanog opadanja. U skladu sa tim ciljem, ishodi rada su vezani za bolje određenje i kategorizaciju glavnih demografskih pokazatelja u ovoj danas raširenoj pojavi