Impact factors of GIScience journals continue to increase in 2016

I am not a particular fan of the journal impact factor (IF): it is obsolete, it is susceptible to manipulation, and it does not guarantee quality. Furthermore, the distribution of citations within the same journal is usually highly skewed, which makes it inappropriate to talk about arithmetic means (on which IF is based). Even some editors of journals with a high IF denounce it openly.

Having said that, it’s hard to deny its importance in academia. For instance, in some countries it conditions promotions and the allocation of government funding.

For starters: the impact factor is a measure of citability of recently published papers in a journal. It is supposed to quantify the influence, reputation, and prestige of a journal; and everything is gauged and consolidated in a single number. The IF of a journal is calculated yearly as the number of references made to all papers published in the journal in the two preceding years, divided by their number (resulting in the yearly average of citedness of recent papers). For instance, the impact factor in 2015 of a journal is calculated as the ratio of cites in 2015 to papers published in 2013 and 2014, and the number of papers published in 2013 and 2014. For example, the journal Housing Theory & Society had published 42 papers in 2013 and 2014. In 2015 there were 43 cites to these 42 papers, as indexed by Thomson Reuters, resulting in an impact factor of 1.024. This also means that papers published before 2013 do not count. The same goes for papers published in 2015 (so a citation from a 2015 paper to a 2015 paper doesn’t count, nor ever will, oddly enough). Finally, not all journals have an IF – only those that are considered influential and of high quality by Thomson Reuters.

The IFs are announced yearly by Thomson Reuters in the Journal Citation Reports. The impact factors for 2015 have just been announced in the 2016 Journal Citation Reports. You can check them here if your institution has a subscription.

As I did last year, I checked the new IFs for the 19 journals that I consider relevant to people in GIScience. The list is composed from my scientometric paper published in IJGIS earlier this year (with the exception of JOSIS – Journal of Spatial Information Science, because it is not yet indexed by Thomson Reuters). For an extended list of journals please see the page compiled by my group.

The results are presented in the table below, also with the IFs in 2013 and 2014:


Generally the IFs continue to rise, as it was the case last year. On average, the IFs grew 16.5%.

The impact of 5 out of 18 journals has dropped: PFG‘s IF shrank by 24% (highest in relative terms), and IJDE’s by 0.5 (highest in absolute terms).

Cartography and Geographic Information Science is a clear winner, as its IF continues to grow substantially – from 0.5 to 2.2 in just two years.

While some journals experienced a considerable boost, in the previous edition their IF plummeted. See the case of GeoInformatica: 1.288 in 2013, down to 0.745 in 2014, and up by 42% to 1.061 in 2015. A substantial increase, but still lower than what it had two years ago.

One news is that the ISPRS International Journal of Geo-Information (IJGI), the Open Access journal published by MDPI got its IF (0.651) for the first time.

For some journals it paid off to deliberately delay paginating papers to boost the impact factor. There are some quintessential cases of holding papers without pagination for a long time, like Transactions in GIS:

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For IJDE that was not so lucrative: despite that the journal holds papers for long, its IF dropped. I wonder how much further it would drop if they didn’t employ such methods.

In total, the sum of all IFs continued to increase:


This might indicate that GIScience papers are recently attaining an increased reach outside the field. The results also show that IFs can be quite dynamic: IFs really go up and down, and in just one year their difference can be substantial, as for a third of journals (6/18) the IF changed by more than 30%.

Despite the general aversion to the IF, and its flaws, it’s certainly good news that GIScience papers continue to get more attention.

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Using 3D GIS to estimate population and refine census maps

The remote estimation of a region’s population has for decades been a key application of geographic information science in demography. Most studies have used 2D data (maps, satellite imagery) to estimate population avoiding field surveys and questionnaires. As the availability of semantic 3D city models is constantly increasing, in our new paper we investigate to what extent they can be used for the same purpose:

Biljecki, F., Arroyo Ohori, K., Ledoux, H., Peters, R., & Stoter, J. (2016). Population Estimation Using a 3D City Model: A Multi-Scale Country-Wide Study in the Netherlands. PLOS ONE, 11(6), e0156808. doi:10.1371/journal.pone.0156808

Based on the assumption that housing space is a proxy for the number of its residents, we use two methods to estimate the population with 3D city models in two directions: (1) disaggregation (areal interpolation) to estimate the population of small administrative entities (e.g. neighbourhoods) from that of larger ones (e.g. municipalities); and (2) a statistical modelling approach to estimate the population of large entities from a sample composed of their smaller ones (e.g. one acquired by a government register). Starting from a complete Dutch census dataset at the neighbourhood level and a 3D model of all 9.9 million buildings in the Netherlands, we compare the population estimates obtained by both methods with the actual population as reported in the census, and use it to evaluate the quality that can be achieved by estimations at different administrative levels. We also analyse how the volume-based estimation enabled by 3D city models fares in comparison to 2D methods using building footprints and floor areas, as well as how it is affected by different levels of semantic detail in a 3D city model. We conclude that 3D city models are useful for estimations of large areas (e.g. for a country), and that the 3D approach has clear advantages over the 2D approach.

The paper is Open Access.


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Applications of 3D City Models: State of the Art Review

My colleagues and I have published a new study in IJGI:

Biljecki, F., Stoter, J., Ledoux, H., Zlatanova, S., & Çöltekin, A. (2015). Applications of 3D City Models: State of the Art Review. ISPRS International Journal of Geo-Information, 4(4), 2842-2889. doi:10.3390/ijgi4042842

This review paper provides a comprehensive overview of use cases of 3D city models. During the work 29 use cases have been identified and described. The paper is supported by 400 references of related work. The PDF is freely available (Open Access) from the publisher’s website.



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