Study on Housing Exclusion: Welfare Policies, Housing Provision and Labour Markets

This is a six country comparative study of the relationship between housing, welfare states and labour markets. The study employs both quantitative (using EU-SILC) and qualitative data

Interview with Tako Postma, Stadsbouwmeester Delft

When a million new homes are needed nationally, your own neighbourhood is as good a place to look for possible opportunities and implications as anywhere else. Marja Elsinga and Harald Mooij (1MH) met digitally with Tako Postma (TP), ‘Stadsbouwmeester’ (city architect) in Delft since 2019 and overviewing the municipality’s aim to build 15.000 new dwellings before 2040

Spotlight on:guideline on current good radiopharmacy practice (cGRPP) for the small-scale preparation of radiopharmaceuticals published in EJNMMI Radiopharmacy and Chemistry (2021)6:8

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The Changing Determinants of Homeownership amongst Young People in Urban China

This article examines the determinants of home ownership among young people in China. More specifically, it aims to shed light on the shifting importance of the state (through ‘redistributive power’) and the ability of young people to compete in housing markets (‘market ability’) after more than three decades of market transition. Through an analysis of data from the China General Social Survey, the paper quantifies the impacts of four types of determinant on young people’s access to homeownership: political affiliation, organizational affiliation, territorial affiliation, and market ability. Results show that a redistributive power (through territorial, political and organizational affiliation) still influences access to housing, mainly in the form of territorial affiliation (hukou registration). Higher market ability does not contribute to homeownership but is related to independent living. The paper points to three housing policy priorities to improve young people’s housing opportunities: reduce inequalities resulting from unequal access to homeownership, improve options for young migrants, and improve conditions in the rented sector

Redistribution, Growth, and Inclusion: The Development of the Urban Housing System in P. R. China, 1949-2015

This paper explains the development of the urban housing system in P. R. China from 1949 to 2011 with an emphasis on the factors driving housing inequality in each policy period. We argue that the logic underpinning the housing policy had shifted from socialist redistribution to the stimulation of growth in the process of market economy reform and has been shifting toward social inclusionary growth since the 2010s. Over the course of time, two institutional factors (work units and household registration/hukou) have played a key role in determining individual households’ housing opportunities. The role of the work units has gradually waned since the 2000s, but the hukou system continues to be important. In the last part of the paper, we set forth the latest changes in Chinese housing policy. Since 2011, the central government has been striving toward a more comprehensive system of housing provision with the aim of making the housing market more inclusive (though not necessarily more equal). Finally, we express concern about an emerging though embedded source of housing inequality: the unequal distribution of family wealth

Stairwalker user manual

Geographical data are typically visualized using various information layers that are displayed over a map. Interactive exploration by zooming and panning actions needs real-time re-calculation. A common operation in calculating with multidimensional data is the computation of aggregates. For layers containing aggregated information derived from voluminous data sets, such real-time exploration is impossible using standard database technology. Calculations require too much time.\ud \ud The University of Twente has developed “Stairwalker”: database technology that accurately aggregates data so that they can geographically be explored in real-time. The technology is a plug-in to common open source technology.\ud \ud Its core is the pre-aggregate index: a database index that cleverly precalculates aggregation values such that it can obtain exact aggregation results from voluminous data with high performance. A fast calculation allows to fully recalculate the result for even the slightest movement of the map, such as a panning or zooming action, without loss of accuracy. Thanks to this indexing mechanism, we can provide a scalable real-time calculation: an order of magnitude larger dataset requires only one additional aggregation level.\ud \ud In geo data visualization, the ability to quickly develop new information layers is important. Although many solutions exist, there is a niche: the combination of visualizing aggregation information, interactive data exploration in real-time, Big Data, calculating exact numbers instead of approximations, and doing so with common open source technology. Our technology for the first time integrates all these features.\ud \ud Our research partners are the companies Arcadis and Nspyre. They both have struggled with this combination of requirements in many of their projects. Our database index technology is not specific to geographical data. It can be used with all types of multidimensional data. Visualization in business intelligence or eScience can also benefit from it.\ud \ud The company Arcadis developed an application for the DCMR Milieudienst Rijnmond based on the Stairwalk technology to investigate whether people send tweets about unpleasant odors as a possible signal of danger. This turns out not to be the case, probably because people think that nobody reads the tweets anyway. But if people have the idea that their complaining tweets are read, then tweets might be much more convenient than the reporting of unpleasant odors by telephone.\ud \ud This manual explains how to use Stairwalker. We first explain in Section 2 how to install the required components in order to have a basic running system. We then explain in Section 3 how to add databases and different kinds of datatypes to Geoserver, an open source server for sharing geospatial data.1 It is explained how to show and customize layers and views, but also how to adjust the system, for example, how to add dimensions or use different dimension types such as median. Finally, Section 4 explains how to extend the system