Data Sources
This data set includes information on the most recent sale for all of the properties in the Portland metro area, including address and property ID information that can be used to spatially join it with a tax lot shapefile. There seems to be no way to systematically download prior sales through the Portland Maps assessor; though this data is publicly-available, the bulk search tool I used to download this information doesn’t return the previous sales for properties. It would be theoretically possible to obtain previous sales data manually, by searching for the address in Portland Maps and inputing the prior sale date and price. As I refine my geographic focus, such an undertaking may become somewhat more plausible, though this would still present some methodological issues. A repeat-sales analysis would need to pay careful attention to the timing of sales, incorporating both the broader market trends and timing of transit expansions to get at my questions. Hedonic analysis seems like a more promising direction for using this data.
Though I was unable to find an aggregated and downloadable data set of issued permits for Portland, this building footprint data provides much of the information I had hoped to obtain through permit data—notably, the spatial extent of new construction. The building footprint data includes the year the structure was built, as well as height and square footage. Data validation may be required, as some fields do contain a relatively high number of null entries. Another limitation of this data source is its lack of data relating to major remodels, which would constitute an aspect of gentrification-associated reinvestment. Nevertheless, it provides the very useful metric of construction over time.
Environmental Studies Journals
This interdisciplinary journal is concerned with the contemporary interface of human geography, environmental studies, and urban research, often with a view towards the future of cities. It emphasizes publishing “agenda-shaping papers” with particular significance for place-based social science. It is generally focused on issues of governance and contestation of environmental change, globalization, development, metropolitan policy, and urban restructuring.
- Motivation: Concern over how “big data” is and may be utilized within cities to reproduce
- Research Questions: What kind of city is envisioned and affected by big data as it looks to the future? What is the array of potential urban futures enacted by urban derivatives as they are projected onto the horizon of possibilities via the data-security calculus?
- Data: Examination of mobile neighborhood safety apps that sought to use data and create algorithms to indicate which parts of the city were safe to walk in. Examination of the nascent field of “sentiment analytics” that uses geocoded social media data to indicate where unrest is, so that pacifying police tactics can be preemptively deployed in an area.
- Methods: Engages with these two sectors of urban big data apps in light of theories on the “anticipatory security calculus”
- Conclusion: As big data looks to the future and creates increasingly pervasive algorithms of governance, it reproduces existing inequalities and securitizes cities against the risks of social change. Additionally, the increased deployment of data in the governance of cities creates an “urban derivative” that disassembles, codifies, and reassembles places, people, flows, and events for the sake of delimiting the horizon of possibility.
Annals of the American Association of Geographers
Baics, Gergely, and Leah Meisterlin. 2016. “Zoning Before Zoning: Land Use and Density in Mid-Nineteenth-Century New York City.” Annals of the American Association of Geographers 106 (5): 1152–75. doi:10.1080/24694452.2016.1177442.
- Motivation: Understanding the spatial order of the un-zoned city and the roots of desires for comprehensive zoning.
- Research Question: What was the spatial order (in terms of land use patterns, density, and crowding) of nineteenth-century Manhattan? Did the 1811 street grid alter land-use conditions? What were the city-wide and neighborhood effects of pre-zoning land-use controls (building/fire codes, nuisance laws, and restrictive covenants)?
- Data: Geo-referenced and digitized versions of the Perris Fire Insurance Atlas of 1852-1854.
- Methods: Data aggregation of building footprints to the blocks on which they sit and the streets that they face, coupled with a categorization of buildings as residential, commercial, mixed-use, and industrial, to create maps of the relative concentration and mixing of uses. To find population density by block, they multiplied the percent of a ward’s residential building area held within a block by the 1855 Census population figures per ward. To estimate overcrowding by block, they divided the total footprint of interior buildings (with no street frontage) per block by the block’s unbuilt interior area. They additionally weighted this result by population density.
- Conclusion: Industrial sites mixed closely and widely with dwellings, especially within block interiors and in working-class districts. Restrictive covenants in the elite uptown neighborhood between Third and Sixth Avenues north of Washington Square effectively created a single-use residential district. The 1811 grid plan changed the pattern of mixing between commercial and residential uses—pre-grid areas had commerce on streets in multiple directions, while post-grid areas featured commercial corridors only on the north-south avenues. The Lower Manhattan Financial District was already well established as a consolidated commercial downtown. Overcrowding was concentrated in immigrant neighborhoods. Overall, despite the lack of comprehensive zoning, market forces created economically- and socially-differentiated landscapes, albeit in close spatial proximity.
- Motivation: Investigate the metropolitan-level connection between an enlarged tech industry and economic prosperity for those not in the tech sector, in light of actors championing high-tech industries as drivers of thriving urban economies and denigrating them as creating relative poverty and social exclusion.
- Research Question: Who benefits from the external multipliers of tech employment? Does an enlarged tech sector influence the wages and job opportunities of those at greatest risk for poverty?
- Data: American Community Survey data on poverty, mean wages for low-skilled workers, employment rate for low-skilled workers, and percent of workers employed in high-tech industries (defined by authors with references of ACS codes) for Census-defined Metropolitan Statistical Areas.
- Methods: Simple correlation of the chosen indicators with tech employment. Linear regression model, controlling for the percent of the MSA population that is foreign born, non-white, under 16, male, with a degree, the total MSA population, and the share of workers employed in manufacturing.
- Conclusion: The presence of high-tech industries has no real impact on poverty and especially, extreme poverty. However, it does increase wages for non-degree-educated workers and, to a lesser extent, employment for those without degrees.