. This was already mentioned by Leaman et al. (2011) for the LOSS galaxy sample.

Distribution of SN types
In Table 5, we present the distribution of SN types according to the morphology of their host galaxies in an analogous fashion as in Table 5 of Barbon et al. (1999). It is clear that most SNe are found in spiral hosts. The percentage of unclassified SNe is ∼18% of the total sample. Approximately half of the unclassified SNe were discovered before 2000.
and that of CC SN hosts, while types Ibc and II SN hosts have similar distributions. These trends were previously reported by van den Bergh et al. (2002van den Bergh et al. (, 2003van den Bergh et al. (, 2005 and Leaman et al. (2011). It is generally believed that hosts of CC SNe are objects with young stellar populations (generally spiral or irregular galaxies), while the old stellar population of early-type galaxies can produce only SNe Ia (e.g., van den Bergh & Tammann 1991; Cappellaro et al. 1999 (Filippenko & Chornock 2000) has been confirmed to be an S0, with a central region showing dust and a disky central gas distribution (e.g., Hakobyan et al. 2008). According to the outer isophotal structure and radial surface brightness profile of the host of SN 2006ee (Puckett et al. 2006), this must be an S0 galaxy. It has been shown that the surface brightness distribution has some small degree of asymmetry in the region to the south-southwest of the nucleus. Here, we suspected the presence of an embryonic spiral arm. We classified the host galaxy of SN 2007ke (Filippenko et al. 2007) as type E, this classification is also given in both NED and HyperLeda. It is in interacting system and is a member of the cluster of galaxies. The host galaxy of SN 2009fe (Kasliwal et al. 2009) is classified as an uncertain type S0. The same morphological type is reported in Gyulzadyan et al. (2011). In NED, it is classified as a blazar (Seyfert 1). This object also shows 1.4 GHz radio continuum emission (Condon et al. 1998). However, more detailed inspection on high resolution images is still required. In principle, the galaxy could have some diffuse spiral arms and be classified as S0/a but due to insufficient resolution of the SDSS image at the distance of this object, it has been classified as an uncertain type S0.
The presence of CC SNe in early-type galaxies can be interpreted as an additional indication that residual star formation episodes also take place in E or S0 galaxies, due to merging/accretion or interaction with close neighbors. Recently, Kaviraj et al. (2009) have found that the recent star formation is likely to be driven by minor mergers, which seems to fit with our interpretation as well. Meanwhile, using rest-frame UV photometry of early-type galaxies in the nearby Universe, Kaviraj et al. (2007) suggested that low-level recent star formation is widespread in nearby early-type galaxies. The situation is also very similar at intermediate redshifts (Kaviraj et al. 2008). Hence, the detection of SNe II in early-type galaxies is expected, but at lower frequency than type Ia.
The middle panel of Fig. 14 shows the number distribution of different types of SNe as a function of the inclination of S0-Sm hosts (see also the top left panel of Fig. 13 and its explanation). The KS test suggests that there is no significant difference between the distributions of types Ia and CC SNe. The same behavior occurs when comparing type Ia SNe separately with types Ib and II SNe. A similar trend was mentioned by Cappellaro et al. (1997).
The right panel of Fig. 14 shows the number distribution of SN types as a function of distance. All the major types of SNe are peaked at 50-100 Mpc. It is clear that the sample of SNe is largely incomplete beyond ∼100 Mpc. The distributions of types Ibc and II SNe are similar and display a sharp decline beyond 100 Mpc. Type Ia SNe, because of their comparatively high luminosity and the presence of dedicated surveys (e.g., SDSS SN Survey, ESSENCE etc.), are discovered at much greater distances than CC SNe. A similar behavior was also found by Leaman et al. (2011). Table 6 displays the numbers of different types of SNe in hosts with different levels of nuclear activity. It is important to note that nuclear activity is affected by aperture bias (e.g., Kewley et al. 2005). The SDSS spectra are taken with a fixed fiber size (3 ′′ ). For a nearby galaxy, the SDSS fiber covers the central nuclear region or its part, while for more distant case it covers a larger fraction of the galaxy (e.g., 120 pc at z = 0.004 but 2.7 kpc at z = 0.1). Hence, the activity data can be contaminated by the emission of circumnuclear regions or from the whole galaxy. The effect also depends on galaxy size, as for dwarf galaxies the fiber will cover a larger fraction of the total emission.

Summary and perspectives
In a series of papers, we will analyze how the different types of SNe events and their spatial distribution are correlated with the properties of the nuclei and global physical parameters of the host galaxies, as well as with the nearby and distant environments of these galaxies. In this first paper, we report the creation of large and well-defined database of 3876 SNe and their 3679 host galaxies that are located on the SDSS DR8 coverage. This database has been created to increase the size of the sample relative to previous works, and to carry out morphological classification, as well as individual measurements of the global parameters of SN host galaxies to a more homogenous and detailed level. Throughout this work, we analyzed and discussed many selection effects that can bias future analyses.
We identified 91% of the host galaxy sample (3340 hosts with 3536 SNe) among which the SDSS identification of ∼1100 hosts have been done for the first time. Using the SDSS multiband images, photometric and spectral data, we provided accurate coordinates, heliocentric redshifts, morphological types, nuclear activity classes (RP, SF, C, LINER, and Sy), apparent g-band magnitudes, major axes (D 25 ), axial ratios (b/a), and position angles (PA) of the host galaxies.
During the mutual comparison of spectroscopic classification of SNe taken from the ASC and SSC and the literature search, we updated spectroscopic types for 67 SNe. We collected all the available data on 3166 SNe types when it was present in one of the SN catalogs or in the CBAT. Our total sample consists of 72 SNe I, 1990 SNe Ia, 234 SNe Ibc, 870 SNe II, and 710 unclassified SNe. In addition, the sample includes 3599 SNe with offset data. We corrected offsets of 43 SNe and calculated (relatively uncertain) coordinates for 332 SNe with unavailable astrometry via offsets and accurate coordinates of the identified nuclei of their host galaxies.
The morphological classification is available for 2104 host galaxies, which is 63% of our sample of identified galaxies and includes 73 hosts in interacting ("inter"), and 56 hosts in merging ("merg") systems. The total sample of host galaxies collects heliocentric redshifts for 3317 (90%) galaxies. The g-band magnitudes, D 25, b/a, and PA are available for 2030 hosts of the morphologically classified sample of galaxies.
We also provided information on the nuclear activity of 1106 host galaxies by using the BPT and WHAN diagrams. The database also includes 6 broad-line AGNs and 77 passive (P) galaxies without any emission features.
The creation of this large database will minimize possible selection effects and errors that often arise when data is collected from different sources and catalogs. In future papers of this series, we will largely use all photometric and spectral data to constrain the nature of SN progenitors, using their distribution within host galaxies of different morphological types and levels of nuclear activity.Probabilistic Real Estate is a prototype we built to explore the New York City real estate market. As explained in a previous post, we used probabilistic programming’s ability to incorporate hierarchical models to make predictions across neighborhoods with sparse amounts of pricing data. In this post, we’ll focus on how we designed the prototype to capitalize on another strength of probabilistic programming: the ability to generate probability distributions.
In price mode, Probabilistic Real Estate shows the median price for each neighborhood.
In price mode, the prototype’s most conventional view, the strength of the orange shading is determined by the median price for each neighborhood. This creates a heat map of relative neighborhood real estate prices. The most expensive neighborhoods are clustered in Manhattan, with nearby Brooklyn and Queens neighborhoods a close second. The map view is complemented by the sidebar, which provides a list of the neighborhoods ordered by price. Use the sidebar to quickly spot things like the most expensive neighborhood (the Upper East Side).
Changing years shows the change in median prices over time.
Use the year selector in the bottom right corner to cycle from the past (2010) to the predicted future (2018). You can see the dark orange of high median prices spread out from Manhattan further and further into the surrounding boroughs as the years progress.
The neighborhood detail view shows the probability distribution for the selected neighborhood.
Clicking on a neighborhood reveals a more detailed view. A map of hexagonal groups (roughly five blocks each) provides more geographically specific price predictions. The graphs on the left reveal the