Every year, BARI updates our Geographical Infrastructure for the City of Boston–an up-to-date database of all properties, parcels, Census blocks, Census blockgroups, and Census tracts in the city. Below, you can read just a few examples of how the BARI research team is using these tools to explore the City of Boston.
by Justin de Benedictis-Kessner, Nov 6, 2018
The Boston Area Research Initiative (BARI) is excited to announce an update to a data set that we’ve made publicly available as part of the Boston Data Portal for several years: the Geographical Infrastructure for the City of Boston. The Geographical Infrastructure is essentially the City’s property records database along with aggregate versions of these data. BARI releases these at the property, parcel, Census block, Census blockgroup, and Census tract levels every year.
I wanted to explore some of the dynamics in the use of individual parcels in the property records. For this I’m going to start with the individual parcel-level dataset (posted on the BARI Dataverse here) rather than one of the geographic aggregated datasets. This database shows a lot of information about each of the many thousands of land parcels in the City, including its land use, its owner, its square footage, and its value.
Specifically, I wanted to explore parking. According to urban transportation planners, both on- and off-street parking is one of the main drivers of induced demand for roads – that is, when more parking is available, more people will drive, but when parking is less available or more expensive, fewer people will drive.
I used the parcel-level data in Boston to look at how off-street parking was distributed across the City. I first wanted to look at the parcel data aggregated using one of the geographic identifiers from the Census Tract-level files that we include with this dataset – the neighborhood as defined by the City’s Inspectional Services Department.
A summary of this information is plotted below. Across the neighborhoods in Boston, we can see that some neighborhoods have very little square footage dedicated to off-street parking while other neighborhoods have orders of magnitude more.
This shows that some of the smallest and densest neighborhoods (e.g. Bay Village and Chinatown) are most heavily dedicated to off-street parking. This is especially interesting considering their proximity to downtown and their proximity to mass transit stations.
Next, let’s look at how has the existence of parking has fluctuated over time. One way to check this is by seeing when a parcel dedicated to off-street parking was developed. I did this for the last few decades, plotted below:
So, overall very little of the overall square footage remodeled in each year is dedicated to parking in most years, with many years having no remodeled parcels dedicated to parking. However, in some years it jumps up notably — for instance, in 2014. As Boston moves towards its goals from its Climate Resilience Plan, it will be interesting to see whether these patterns continue, or if fewer parcels in the City are dedicated to parking in new construction and remodeling.
Using these shapefiles I mapped out where the parcels dedicated to off-street parking are in the city. This map spatially demonstrates what the charts showed: off-street parking (plotted in turquoise on this map) is incredibly concentrated (relative to total area) in the downtown areas of Boston. This corroborates what we saw in the neighborhood summary.
On a policy note, these summaries point out one way in which transportation resources currently exist in the City. As Boston moves towards a more climate-friendly resilience plan, leveraging the area dedicated to cars to instead prioritize non-driving forms of transportation will be important. Currently, dedicating so much of the downtown neighborhoods to parking only serves to increase the number of cars there, which seems counterintuitive for traffic and urban planning more broadly. If the City of Boston wanted to decrease traffic in these central neighborhoods and decrease the number of cars entering such dense neighborhoods, dedicating less of the area in these neighborhoods to cars would be a good first step.
More broadly, these summaries and various visuals together demonstrate the use of BARI’s geographical infrastructure dataset more generally: easily being able to summarize information at various geographic levels across the City.
by Nolan Phillips, Nov. 7, 2018
The Boston Area Research Initiative (BARI) is excited to announce a new data set that is publicly available as part of our annual geographical infrastructure data release! The new data provides measurements of distances and travel durations (using several modes of travel) between Boston’s census block groups and tracts. The dataset and documentation can be downloaded from the Boston Data Library.
The first part of the new data set contains population-weighted centroids of Boston’s census block groups and tracts. These improve upon the census estimates by using the populations of census blocks nested within each type of areal unit. The population-weighted centroids minimize the error of how far residents must travel and how long it will take them to travel between block groups and tracts.The second part of the new data set uses the population-weighted centroids to quantify several distance and travel duration metrics between block groups and tracts, respectively. These include:
Practitioners, policy makers, advocacy groups, researchers or the general public can use these files to examine inequities in travel durations relative to their Euclidean distances. Bostonians know that it can take a long time to travel to particular parts of Boston (just try to get to Logan Airport during rush hour!), but those durations are unequal burdens to residents of Boston. Residents of Brighton, Alston or East Boston have longer travel durations not simply because they are on the outskirts of the city, but due to a lack of infrastructure (particularly public transit) that connects them to the rest of the city.
The figure below shows the total distance and travel time that residents of each block group must travel to reach all other block groups in Boston. Blocks groups that are equidistant but vary dramatically on travel durations highlight the travel inequities.
While this may seem obvious to many, there are serious consequences for assuming that residents can travel distances equally. For example, Boston Public Schools (BPS) uses Euclidean distances rather than driving or public transit distances when assigning kids to schools (see BARI’s report on BPS’s school assignment here). This new data set can be used to examine these inequities, and it is our sincere hope that it will be used to rectify them.
by Mike Shields, Nov. 8, 2018
How much does that vacant lot cost? As part of the Boston Area Research Initiative’s (BARI) recent release of the 2018 Geographic Infrastructure for the City of Boston, we examined the makeup of vacant parcels within the city by neighborhood. The City of Boston systematically categorizes parcels using Property Occupancy Codes. These codes distinguish parcels and properties not only by their land use type (e.g. Residential, Commercial, Tax-Exempt, etc.), but also by their building function (e.g. public school, library, single family dwelling, etc.). This allows the city to annually assess the values of properties and appropriately plan and zone for new development. But what about those empty spaces within the city? How many are there, where are they located, and how much do they cost?
We first mapped the full distribution of vacant parcels as part of our “City of Boston 2018 Parcels” layer on our Boston Research Map (along with a few other “special parcels”). We noticed that the city has a very broad categorization for a “vacant” parcel (see Figure 1). For example, Franklin Park, the Fens, and the Rose Kennedy Greenway are all categorized as vacant, along with various piers and highway underpasses. However, their definition also includes the more typical residential and commercial vacant lots scattered throughout most city neighborhoods.
Figure 1: The Distribution of Vacant Parcels Across Boston
We then used the Boston Planning and Development Agency’s planning districts as a proxy boundary for a neighborhood to show the distribution of vacant parcels across Boston’s neighborhoods (see Table 1). Table 1 shows that the greatest share of vacant parcels can be found in Roxbury (n=1476) followed by West Roxbury (n=959) and South Dorchester (n=928), while the more dense and centralized neighborhoods of Back Bay/Beacon Hill and Fenway/Kenmore have the least (n=43 and n=44 respectively). This is unsurprising considering the greater number of parcels within these larger outer-neighborhoods when compared to the smaller downtown areas.
Table 1: 2018 Vacant Parcels in the City of Boston
We then differentiated the parcel’s land use types within each neighborhood to better understand the makeup of these vacant parcels (see Table 2). Unsurprisingly, most neighborhoods’ vacant parcels are zoned as residential plots; this means they are zoned for single family dwellings, multiple family dwellings, apartments, or condominiums. It is only in the Central and Fenway/Kenmore neighborhoods where vacant commercial parcels and vacant tax-exempt parcels outnumber vacant residential ones, and only the South End shows a higher number of vacant tax-exempt to vacant residential parcels.
Table 2: 2018 Vacant Parcels in the City of Boston by Land Use Type
Finally, we were interested in seeing the difference in value among vacant parcels across neighborhoods. Table 3 shows the value (in dollars per square foot) of vacant parcels by land use type within each neighborhood. We used each parcel’s total assessed value and divided it by the lot size. Surprisingly, vacant residential parcels are valued much higher than their commercial and tax-exempt counterparts (means=$64.62, $18.04, and $17.34 respectively), and outer neighborhoods – like West Roxbury and Mattapan – have higher values than downtown. In fact, the Central neighborhood has the lowest valued vacant residential parcels at $22.96. Vacant commercial parcels have a high range ($31.78) and show higher valued parcels in South Boston, Central, and North Dorchester neighborhoods ($34.11, $31.11, and $30.79 respectively). While South Boston’s Seaport District and Downtown’s Financial Center help explain these high values, a conclusive case for North Dorchester remains undetermined. Surprisingly, vacant commercial parcels in the Back Bay/Beacon Hill are valued the lowest at $2.33; this is odd considering this neighborhood’s concentration of businesses and proximity to downtown. Finally, vacant tax-exempt parcels had the highest range ($56.63) and follow a more predictable pattern of higher values correlating with proximity to downtown.
Table 3: Value (in $ per SqFt) of Vacant Parcels by Land Use Type
To better visualize these differences, we created a new layer entitled “Vacant Parcel Values” on our Boston Research Map (see Figure 2). After turning the layer on, right click the title and click the “Styles” option. A small window with a pull-down bar will appear. Use this layer to see how the City of Boston values different vacant lots.
Figure 2: Visualizing Vacant Parcels Value in Boston
by Dan O’Brien, Nov. 9, 2018
Urban researchers and policymakers often talk about “disparities” and “inequities” across the city, but the geographic scale of these issues is often unclear. Many speak of disadvantaged neighborhoods, but are Dorchester, South Boston, and Allston-Brighton homogeneous regions, with every pocket looking and feeling the same? How do things like crime, disorder, educational attainment, and pollution vary by census tract? By street? By address? This is a deceptively difficult question to answer as these three levels of geography are literally nested within each other. BARI has developed a new methodology that solves this issue, made possible by our Geographical Infrastructure for the City of Boston. Here I illustrate this new methodology by addressing the concentration of physical disorder (i.e., “broken windows”) across the census tracts, streets, and addresses of Boston, which is also presented in a recent article in Journal of Research in Crime and Delinquency.
The new methodology is called the “nested Gini.” Before describing what it does, consider the following problem. We want to know which geographic scale is responsible for the distribution of crime events, which can help us to design efforts around prevention or response that target the appropriate level, be it at-risk neighborhoods, hotspot streets, or “problem properties.” This is a more difficult question than it might appear to be on the surface. For example, take a city where crime events are unevenly distributed across neighborhoods. Suppose then that the crimes within each neighborhood are distributed perfectly evenly across all the streets therein. In this case, it would be inaccurate to attribute any of the distribution of crime to streets. Nonetheless, if streets are analyzed directly, they will appear to exhibit just as much variation as neighborhoods. Put another way, if the same number of events are distributed across a far greater number of units, concentrations will inevitably look greater.
The nested Gini disentangles concentrations at multiple geographic scales by combining the quantitative power of the Gini coefficient with the logic of—you guessed it—nesting. The Gini coefficient calculates the overall level of inequality in a set of items. It is most commonly used for income, but it can be applied to any quantity, in this case crime events. The methodology is “nested” in how it compares quantities across units. Instead of calculating the concentration of crime, for example, across all streets in a city, it calculates them for the streets within a census tract. It then does so for all census tracts, generating an estimate of the typical level of concentration of crime events across streets accounting for the distribution of crime across neighborhoods. It can also be applied to addresses, nested within street segments.
To illustrate, we will leverage BARI’s Geographical Infrastructure for the City of Boston, which can accommodate any data set with addresses and then nests them within the appropriate street segment and census geographies. In order to demonstrate down to the address level, we will opt, however, to use incidents of physical disorder (drawn from 311 reports), or “broken windows,” rather than crime events, which can be more sensitive.
Figure 1 shows how the nested Gini works. The census tracts of Boston are reasonably diverse in the number of physical disorder events occurring within them, amounting to a moderate Gini of .46. Let us then zoom in on a single high-crime census tract in Mattapan. The incidents of physical disorder occurring within that neighborhood are largely concentrated on a handful of street segments, generating a more marked Gini of .80. If we zoom in further on one of these hotspots, we see that the 20 or so incidents occurred at only four addresses, with one generating more than half. This Gini is similarly high at .83.
This single example suggests that hotspot streets and problem properties are the primary concern when considering the distribution of physical disorder—provided the patterns are consistent. It turns out that they are. The average tract had a Gini of .70 for the distribution of physical disorder across its streets, and the average street had a Gini of .73 for the distribution of physical disorder across its addresses.
To summarize, the nested Gini is a new methodology developed by BARI to disentangle concentrations at multiple levels. In this case, it revealed the importance of attending particularly to street segments and addresses as loci for physical disorder, but it might be applied to any quantity distributed unevenly across the urban landscape. Further, it is one of many methodologies that are possible thanks to BARI’s Geographical Infrastructure, which facilitates the linkage of data describing the same geographic units; the seamless aggregation of data across nested geographic scales; and the straightforward visualization of the results.
To learn more about the nested Gini, please see the recent paper “The Action is Everywhere, but Greater at More Localized Spatial Scales” published in Journal of Research in Crime and Delinquency.