dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluations of the different methods for Tokyo (1986-2016) The Tokyo data set The Tokyo data set consists of 23 wards of the Tokyo metropolitan area (621 square kilometers), and the analysis period is approximately 30 years between January 1986 and June 2016. The data set covers previously-owned condominiums (apartments transactions) published in Residential Information Weekly (or Shukan Jyutaku Joho in Japanese) published by RECRUIT, Co. This magazine provides information on the characteristics and asking prices of listed properties on a weekly basis. Moreover, Shukan Jutaku Joho provides time-series data on housing prices from the week they were first posted until the week they were removed as a result of successful transactions. We only use the price in the final week because this can be safely regarded as sufficiently close to the contract price.
The squared term in the quadratics can distort the results for later years. The implication is that there is a trade-off between model fit in the base period and overall performance of the HPI. When the repricing method is used, quadratic terms in the hedonic model should be avoided. It is better to stick with a simpler more linear model. This problem was not observed for the Sydney data set because these variables were not included in the hedonic model. A second difference is that there is no clear evidence of drift in the DIL and DIP results in Table  C3 -3, as compared with what was observed for Sydney apartments. Over the whole sample period, the rise in apartment prices for all hedonic methods, excluding repricing without updating, ranges between 8. 5 and 13. 8 percent. The average masks a rollercoaster ride where prices first went way up and then way down before gradually returning to near their starting point.
Price indices for new apartments in Tokyo Estimating a price index for new apartments is difficult for the Tokyo data set due to the small sample size. We define a new build as any apartment that is less than three years old. We would have preferred less than two years old, but this is not really feasible. The main problem here is to estimate shadow prices for the locational ward dummy variables. For some wards, no transactions on new dwellings are observed in some quarters. This is a problem particularly for the average characteristics and hedonic imputation methods that begin by estimating a separate hedonic model for each quarter as stated in (8). One way of dealing with missing wards is to restrict a comparison between adjacent quarters to the apartments sold in wards that are observed in both quarters.
By contrast, the problem of missing wards does not arise for the RTD method. Any wards that are observed in any quarter can be included in the RTD hedonic model, as stated in (22). This example illustrates an important advantage of the RTD method, in that it performs well on smaller data sets. The stratified median index is particularly badly affected by the small sample size. Faced with a small sample problem, we have greatest confidence in the RTD method with a relatively long window (e. g. , RTD5Q). Using RTD5Q as our benchmark, the downward drift in the repricing index (RP1) is much more pronounced than in the previous figures. Updating the reference shadow prices every five years (RP2) solves this problem. Indeed RP2 approximates RTD5Q quite closely. The average characteristics and hedonic imputation methods in this case are somewhat erratic.
* * * Our main findings are as follows: • The price indices seem to be quite robust over the range of hedonic methods used by NSIs in Europe to compute their HPIs. • In smaller data sets (e. g. , new builds in Tokyo), the HPI becomes more sensitive to the choice of method. Hence smaller countries in the EU need to be more careful when choosing a method. We recommend RTD4Q or RTD5Q for smaller countries with less housing transactions. • The double imputation Paasche and Lasepyres (DIP and DIL) indices for apartments in Sydney are subject to drift. Evidence of a small amount of drift is also apparent in the Tokyo data. The results for Sydney apartments indicate that DIP and DIL should not be used. Fortunately, no NSIs are using either of these methods. • The repricing method seems to have a downward bias relative to the other hedonic indices when the reference shadow prices are updated only every five years or not at all.
The upward bias of stratified medians over the whole sample period in both datasets can be attributed to an upward trend in the quality of transacted dwellings over time. The higher RSME and MAD statistics can be attributed to the stratified-medians not properly adjusting for changes in the quality of transacted dwellings on a period-to-period basis. • It is more difficult to construct a quality-adjusted price index for new builds. Again, RTD5Q is recommended for computing an HPI for new dwellings when there is a shortage of data points. • For the HPI we recommend not splitting new and existing dwellings. It is better to combine them in the same hedonic model, with age as one of the explanatory characteristics. • Houses and apartments should be estimated using separate hedonic models, and then combined using the standard Eurostat method for combining strata (see Eurostat, 2013, chapter X).
Note: The properties in the base used for the estimations are described in the Online complement C1. Coverage: Municipalities in mainland France in which at least one real estate transaction was recorded in 2006 and which was covered by a PPRi in 2006, and outside seismic zoning and outside the scope of an official classification as "natural disaster" in 2006. Sources: Perval and BIEN 2006 notarial databases, Cartorisque and Gaspar; author's calculations.
presents the statistics describing the variables in the database for econometric analysis. The average price of building land is 88 Euros per m² for an average surface area of just over 1,000 m². The average cost of building an individual house is 1,097 Euros per m² for an average floor area of 127 m². The characteristics of the houses in the sample are less variable than the characteristics of the land plots. Land represents, on average, 30% of the total building cost of an individual house, and the floor area covers on average 15% of the land area. We use five qualitative variables.
The clone database is built up in the following way. The number of new dwellings transactions in ECLN and the number of second-hand dwellings transactions in the Notaries' databases are computed for each triplet 'year (instead of quarter to have enough observations) -type of dwelling (houses, only "Individuel groupé" in ECLN, or flats) -municipality (arrondissement for Paris, Marseille and Lyon)' over the 2006_Q1-2010_Q4 period.
These data are completed by the list of official natural disaster classifications in the Gaspar register, and by indicators from the National 15. The Perval databases are produced by a company named Min. Not ADSN. The data used in this study were produced following work to ensure consistency between the fields and variable dictionaries used by BIEN and Perval, conducted by the SOeS (statistics department of the GASPAR -Gestion Assistée des Procédures Administratives relatives aux Risques naturels et technologiques/Assisted management of administrative procedures concerning natural and technological risks. Gaspar contains information on preventive or regulatory documents down to municipal level.
64 with the first principal component and a null correlation with the second principal component. The percentage indicated on each axis refers to the percentage of variance explained by the axis. Coverage: 25 countries (cf. Box). Sources: OECD, Affordable Housing Database (AHD); authors' calculations. variance the Slovak Republic). To facilitate the presentation of the results, in what follows we will name the country groups, respectively, "Western", "Northern", "Southern-Central" and "Eastern". Overall, the classification is broadly in line with our priors, based on housing market and institutional information. Nevertheless, a few remarks on specific cases are in order before moving to more detailed analyses. Finland is classified in the first group 11. As noted above, dwellings owned by Swedish municipalities and Danish and Dutch housing associations are not classified as social housing, even though they play an important role in housing low-income households.
61 0. 60 0. 50 0. 71 0. 70 - - - - - Household liabilities as a share of disposable income 1. 32 1. 34 1. 97 0. 83 0. 57 - - - - - Reading note: The numbers indicated in the table are the averages of each indicator for the identified group of countries. Coverage: 25 countries for the left part, 32 countries for the right part (cf. Box). Sources: OECD, Affordable Housing Database (AHD); authors' calculations.
73 Portugal 0. 01 3. 63 10. 32 Romania 0. 00 0. 00 Slovak Republic 0. 66 0. 00 Slovenia 3. 89 0. 33 Spain 0. 01 1. 79 0. 90 Sweden 0. 45 32. 75 1. 47 6. 87 9. 73 80. 16 0. 77 2. 47 Switzerland 1. 87 0. 55 United Kingdom 1. 41 29. 06 13. 23 0. 00 0. 27 28. 50 71. 23 0. 00 United States 0. 10 Coverage: 37 countries (cf. Box). Sources: OECD, Affordable Housing Database (AHD); authors' calculations.
In terms of floor area, individual areas with a spatial distribution very close to houses (single and grouped) represent almost that of the issued building permits, as in the half of total construction, due to significantly Sit@del2 database. larger surfaces than collective housing. This gap has narrowed sharply in the recent years, Each observation in the final sample is due to the decreasing size of houses and the matched to municipal construction measures relative increase in the construction of collec- from building permits filed between 1974 and tive buildings. In terms of land area, the gap 2015, derived from the raw data in Sit@del2 is even wider between pure individual houses (Box 1). This measure of construction includes and collective housing, while the latter shows all residential construction, not only pure levels close to grouped individual houses: individual houses resulting from single-unit individual houses account for approximately construction projects, but also grouped indi- 90% of the total surface area intended for vidual houses resulting from multi-unit con- construction.
Databases: EPTB and Sit@del2 The units surveyed in the Building Land Price Survey geocoded to the plot identifier using the cadastral (Enquête sur le prix des terrains à bâtir, EPTB) are information (Majic II from the DGFiP). individuals who have been authorized to build indi- vidual houses. Collection was done by post. The first EPTB survey covering the entire French territory dates from 1985, it was stopped at national level in 1996. It was relaunched in 2006 and has been exhaustive since 2010. We use the raw unadjusted EPTB data for 2006-2014 referenced on the date the land was purchased. The data allows the price of land to be traced back to the 1990s, although in almost 75% of cases, the land is purchased in the year the permit is filed. Georeferencing data comes from Sit@del2, the information and automated process- ing system for basic data on housing and prem- ises, provided by the Department of Statistical Data and Studies (SDES).
The number of constructions for the non-private market is estimated from the number of homes built by the social sector including homes built in "VEFA" (dwellings sold before they are built -vente en l'état futur d'achèvement) by the private sector for the public sector (seeCDC, 2015). Sales of second-hand homes are estimated from CGEDD, from DGFiP (MEDOC) and notaries database (http://www. cgedd. developpement-durable. gouv. fr/nombre-et-montant-des-ventes-immobilieres-a1003. html). SeeFriggit (2014)for the method.
The clone database is built up in the following way. The number of new dwellings transactions in ECLN and the number of second-hand dwellings transactions in the Notaries' databases are computed for each triplet 'year (instead of quarter to have enough observations) -type of dwelling (houses, only "Individuel groupé" in ECLN, or flats) -municipality (arrondissement for Paris, Marseille and Lyon)' over the 2006_Q1-2010_Q4 period. Region 2006-2007 2008 2009 2010 2011 2012 Mean ÎLE-DE-FRANCE 8. 95 7. 97 8. 12 6. 23 3. 54 4. 43 6. 88 CHAMPAGNE-ARDENNE 2. 55 2. 53 2. 74 2. 51 1. 81 1. 67 2. 34 PICARDIE 2. 52 2. 34 2. 13 2. 90 2. 81 3. 16 2. 63 HAUTE-NORMANDIE 2. 84 3. 13 2. 73 3. 32 3. 67 3. 70 3. 17 CENTRE 4. 38 4. 24 3. 96 4. 81 5. 39 4. 56 4. 53 BASSE-NORMANDIE 3. 10 3. 37 3. 22 3. 22 3. 42 3.
and month. The source of these variables is the Conseil général de l'environnement et du développement durable (CGEDD). The raw data on the RETT come from the Service de publicité foncière (datasets MEDOC and Fidji) and are compiled and modified by the CGEDD, before being made available at the departmental level in open access. MEDOC provides the tax revenue by dépar- tement and month, and is exhaustive. Fidji provides the tax revenue and the number of transactions also by département and month, but it presents the inconvenience to be not com- pletely exhaustive (1% of the transactions are missing). To solve this problem, the CGEDD uses both databases, and applies a correction coefficient 9 , in order to recover an estimation of the total number of transactions by départe- ment 10. Next, the CGEDD computes the monthly total tax bases of each département, by dividing the tax revenue by the corresponding RETT rate, and publishes in open access two datasets.
The first one provides monthly data on the number of transactions of the Régime de Droit Commun by département, for the period from April 2004 up to now (from MEDOC + Fidji), but those data are computed on a 12-month cumulative basis. The second one provides monthly total tax bases (raw and 12-month cumulative) of the Régime de droit commun and the Régime dérogatoire separately and by département, for the period from January 2000 up to now (from MEDOC). The data on the Régime de droit commun (whereby the reform is implemented) are composed approximately of 95% of established properties (whose 15% of non-residential premises) and around 5% of lands. We only use this second dataset because it is impossible at this stage to recover the sim- ple monthly data for the first dataset.
These data are completed by the list of official natural disaster classifications in the Gaspar register, and by indicators from the National 15. The Perval databases are produced by a company named Min. Not ADSN. The data used in this study were produced following work to ensure consistency between the fields and variable dictionaries used by BIEN and Perval, conducted by the SOeS (statistics department of the GASPAR -Gestion Assistée des Procédures Administratives relatives aux Risques naturels et technologiques/Assisted management of administrative procedures concerning natural and technological risks. Gaspar contains information on preventive or regulatory documents down to municipal level. French ministry of Environment). 16. These criteria are recommended by the company Min. Not ADSN (the producer of Perval databases) for real estate statistics.
5 -3. 9 1 st of. (3. 4) (2. 4) (2. 6) (2. 5) (3. 7) (2. 7) (3. 5) (2. 7) R2 0. 58 0. 58 0. 58 0. 58 -2. 4 -17. 6* 0. 3 -4. 5 Number of observations 3,468 3,468 3,468 3,468 (8. 0) (9. 8) (13. 5) (14. 1) Houses R2 0. 58 0. 58 0. 58 0. 58 PPRi Zone x After the -4. 2 -1. 8 -2. 7 1. 0 Number of observations 3,468 3,468 3,468 3,468 1 st of. (4. 4) (3. 6) (3. 8) (4. 7) Houses R2 0. 5 0. 5 0. 57 0. 57 PPRi Zone x After the 1 st -5. 1 -0. 5 -2. 8 -1. 5 Number of observations 3,465 3,465 3,465 3,465 of. (4. 9) (3. 8) (3. 7) (3. 6) At least one 'CatNat' in 2005 x 11. 6* 0. 7 3. 6 -7. 1 PPRi Zone x After the 1 st of. (6. 0) (4. 8) (5. 1) (5. 1) R2 - 0. 58 0. 58 0. 58 Number of observations 3,468 3,468 3,468 3,468 Sources: Perval and BIEN 2006 notarial databases, Cartorisque, ONRN ; Insee ; Corine Land Cover, IGN ; author's calculations.
The aim of this work is to present the XLSSsC N01 supercluster, to characterise the clusters it is composed of, and to investigate the stellar population properties of galaxies classified as members or belonging to the surrounding high-density and low-density fields. The paper is organised as follows: in Section 2 we present the datasets and the tools that we used to compute galaxy stellar population properties; in Section 3 we characterise different environments in the region of the XLSSsC N01 supercluster; in Section 4 we explore the dependence of the stellar population properties on environment; and finally in Section 5 we summarise this work and discuss our results. Throughout the paper we assume H 0 = 69. 3 km s - 1  M pc -1 , Ω M = 0. 29, Ω Λ = 0. 71  (Planck Collaboration et al. 2014, Planck13+Alens).
Galaxy catalogue To characterise the properties of the galaxies in the XLSSsC N01 supercluster, we extract the useful information from the spectrophotometric catalogue presented by XXL Paper XXII. We focus on the area covered by the supercluster  (RA [35. 25:38. 0], Dec  [-6. 25:-3. 5] ), and redshift range 0. 25 < z < 0. 35. The photometric and photo-z information for this region are mainly drawn from the CFHTLS-T0007 photo-z catalogue in the W1 Field (8 • ×9 • , centred at RA=34. 5 • and Dec=-07 • ). The data cover the wavelength range 3500Å< λ < 9400Å in the u * , g , r , i , and z filters. These data are complemented with photometric and photo-z measurements in the SF catalogue (Sotiria Fotopoulou, private communication), containing aperture magnitudes in the g , r , i , z , J , H , and K bands, which have been converted into total magnitudes using a common subsample of galaxies with the CFHTLS-T0007 W1 field catalogue.
The percentage of galaxies belonging to this sample that will be included in the scientific analysis presented in this paper is 2. 5%. All magnitudes are Sextractor MAG_AUTO magnitudes  (Bertin & Arnouts 1996 ) in the AB system corrected for Milky Way extinction according to  Schlegel et al. (1998). The error associated with photo-z in the magnitude range that we are probing in this work has a dependence with redshift of σ/(1 + z) ∼ 0. 03, and therefore assumes the minimum value of 0. 039 at z=0. 25 and the maximum 0. 42 at z=0. 35. Spectroscopic redshifts are drawn from the XXL spectroscopic database hosted in the CeSAM (Centre de donnéeS Astrophysiques de Marseille) database in Marseille. 2 As described in XXL Paper XXII, the starting point is a heterogeneous ensemble of spectra and redshift coming from different surveys superposed in the sky (mainly GAMA, SDSS, VIPERS, VVDS, VUDS, and XXL dedicated spectroscopic campaigns, see Table 2 in XXL Paper XXII), and the final spectroscopic catalogue was obtained by removing duplicates from the database using a careful combination of selection criteria (called the priorities) that take into account the parent survey of the spectrum and the quality of the redshift measurement.
Overall, the uncertainties on the galaxy redshift in the database vary from 0. 00025 to 0. 0005, computed from multiple observations of the same object and de-2 http://www. lam. fr/cesam/ pending on the sample used (more details on the XXL spectroscopic database are given in XXL Paper XX); we consider the highest value in this range as the typical redshift error for all objects. A further 17 spectroscopic redshifts (Lonoce, private communication), obtained observing the centre of the XLSSsC N01 field with the AF2 multifiber spectrograph at the 4. 2m William Herschel Telescope (WHT, La Palma Island, Spain) in January 2017 during a campaign within the WEAVE project, appear to confirm the complexity of the supercluster. These data will be presented in  Lonoce et al. (in prep. ). 3 he spectroscopic catalogue of galaxies in the area of the supercluster and in the redshift range 0.
Tools To derive the properties of galaxies and of their stellar populations, we exploit two different codes. Absolute magnitudes are computed using LePhare 4   (Arnouts et al. 1999; Ilbert et al. 2006) , as described in XXL Paper XXII. This code was developed mainly to compute photometric redshifts, but it can also compute physical properties of galaxies, and the spectroscopic redshift can be used as an input fixed parameter to improve the quality of the physical outputs. The LePhare output physical parameters that are going to be used in this work are absolute magnitudes, and thus rest-frame colours. Galaxy stellar population properties have been derived by fitting the spectra with SINOPSIS 5  (SImulatiNg OPtical Spectra wIth Stellar population models), a spectrophotometric model fully described in  Fritz et al.
We include in the LD computation also galaxies with no reliable photo-z, but whose spectroscopic redshift is 0. 25 ≤ spec-z ≤ 0. 35. The photo-z sample of galaxies used in the LD computation is shown in the top left panel of Figure  1. We define the projected LD relative to a given galaxy as the number of neighbours in a fixed circular region in the sky of radius 1 Mpc at z=0. 2956 (the redshift of XLSSsC N01). We consider all galaxies in the photo-z sample in a slightly larger rect-  The LD is defined as the ratio between the number counts of galaxies N c in the circle around the considered galaxy and the area A of the circle itself. Count corrections are performed for galaxies in the proximity of the edges in the high declination side of the rectangle (-3. 76743 ≤ Dec (deg) ≤ -3. 70524) by computing the area of the circular segment that falls outside the field and dividing the LD estimate by the ratio F c (≤ 1) between the area actually covered by the data and the circular area.
We perform Kolmogorov-Smirnov statistical tests (KS) to compare these distributions, and find that they are all compatible with being drawn from a single parent sample (i. e. the p-values are above the significance level of 0. 05). Overall, we conclude that the sSFR-Mass relation does not seem to depend on global environment above the galaxy stellar mass limit in our sample, even considering extremely different environments such as X-ray clusters within the XLSSsC N01 supercluster and the field (uncontaminated by X-ray groups or clusters). Second, we focus on the SFR-Mass relation of star-forming galaxies in the four defined environments, shown in Fig. 8. It is evident that galaxies in the different environments are similarly distributed in this plane. In fact, the linear fits performed on the data points of each environment separately are completely superposed when considering virial members and high-and low-density fields, and is slightly steeper when the population of outer members is considered, although the difference can be appreciated only at masses higher than those covered by our sample.
pbworks. com/. The Australia Telescope Compact Array is part of the Australia Telescope National Facility, which is funded by the Australian Government for operation as a National Facility managed by CSIRO. GAMA is a joint European-Australasian project based around a spectroscopic campaign using the Anglo-Australian Telescope. The GAMA input catalogue is based on data taken from the Sloan Digital Sky Survey and the UKIRT Infrared Deep Sky Survey. Complementary imaging of the GAMA regions is being obtained by a number of independent survey programmes including GALEX MIS, VST KiDS, VISTA VIKING, WISE, Herschel-ATLAS, GMRT, and ASKAP providing UV to radio coverage. GAMA is funded by the STFC (UK), the ARC (Australia), the AAO, and the participating institutions. The GAMA website is http://www.